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1. The Southern-sky MWA Rapid Two-metre (SMART) pulsar survey -- II. Survey status, pulsar census, and first pulsar discoveries

Author

Bhat, N. D. R., Swainston, N. A., McSweeney, S. J., Xue, M., Meyers, B. W., Kudale, S., Dai, S., Tremblay, S. E., van Straten, W., Shannon, R. M., Smith, K. R., Sokolowski, M., Ord, S. M., Sleap, G., Williams, A., Hancock, P. J., Lange, R., Tocknell, J., Johnston-Hollitt, M., Kaplan, D. L., Tingay, S. J., and Walker, M.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

In Paper I, we presented an overview of the Southern-sky MWA Rapid Two-metre (SMART) survey, including the survey design and search pipeline. While the combination of MWA's large field-of-view and the voltage capture system brings a survey speed of ~450 square degrees per hour, the survey progression relies on the availability of compact configuration of the Phase II array. Over the past few years, by taking advantage of multiple windows of opportunity when the compact configuration was available, we have advanced the survey to 75% completion. To date, about 10% of the data collected thus far have been processed for a first-pass search, where 10 minutes of observation is processed for dispersion measures out to 250 ${\rm pc\,cm^{-3}}$, to realise a shallow survey for long-period pulsars. The ongoing analysis has led to two new pulsar discoveries, as well as an independent discovery and a rediscovery of a previously incorrectly characterised pulsar, all from ~3% of the data for which candidate scrutiny is completed. Here we describe the strategies for further detailed follow-up including improved sky localisation and convergence to timing solution, and illustrate them using example pulsar discoveries. The processing has also led to re-detection of 120 pulsars in the SMART observing band, bringing the total number of pulsars detected to date with the MWA to 180, and these are used to assess the search sensitivity of current processing pipelines. The planned second-pass (deep survey) processing is expected to yield a three-fold increase in sensitivity for long-period pulsars, and a substantial improvement to millisecond pulsars by adopting optimal de-dispersion plans. The SMART survey will complement the highly successful Parkes High Time Resolution Universe survey at 1.2-1.5 GHz, and inform future large survey efforts such as those planned with the low-frequency Square Kilometre Array., Comment: 22 pages, 9 figures, 7 tables, Accepted for publication in PASA

Published
2023
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2. The Southern-sky MWA Rapid Two-metre (SMART) pulsar survey -- I. Survey design and processing pipeline

Author

Bhat, N. D. R., Swainston, N. A., McSweeney, S. J., Xue, M., Meyers, B. W., Kudale, S., Dai, S., Tremblay, S. E., van Straten, W., Shannon, R. M., Smith, K. R., Sokolowski, M., Ord, S. M., Sleap, G., Williams, A., Hancock, P. J., Lange, R., Tocknell, J., Johnston-Hollitt, M., Kaplan, D. L., Tingay, S. J., and Walker, M.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We present an overview of the Southern-sky MWA Rapid Two-metre (SMART) pulsar survey that exploits the MWA's large field of view and voltage capture system to survey the sky south of 30 degree in declination for pulsars and fast transients in the 140-170 MHz band. The survey is enabled by the advent of the Phase II MWA's compact configuration, which offers an enormous efficiency in beam-forming and processing costs, thereby making an all-sky survey of this magnitude tractable with the MWA. Even with the long dwell times of the survey (4800 s), data collection can be completed in < 100 hours of telescope time, while still retaining the ability to reach a limiting sensitivity of ~2-3 mJy. Each observation is processed to generate ~5000-8000 tied-array beams that tessellate the full ~610 square degree field of view, which are then processed to search for pulsars. The voltage-capture recording allows a multitude of post hoc processing options including the reprocessing of data for higher time resolution. Due to the substantial computational cost in pulsar searches at low frequencies, processing is undertaken in multiple passes: in the first pass, a shallow survey is performed, where 10 minutes of each observation is processed, reaching about one-third of the full search sensitivity. Here we present the system overview and initial results. Further details including first pulsar discoveries and a census of low-frequency detections are presented in a companion paper. Future plans include deeper searches to reach the full sensitivity and acceleration searches to target binary and millisecond pulsars. Simulation analysis forecasts ~300 new pulsars upon the completion of full processing. The SMART survey will also generate a complete digital record of the low-frequency sky, which will serve as a valuable reference for future pulsar searches planned with the low-frequency Square Kilometre Array., Comment: 22 pages, 12 figures, 2 tables, Accepted for publication in PASA

Published
2023
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3. A targeted search for repeating fast radio bursts with the MWA

Author

Tian, J., Anderson, G. E., Hancock, P. J., Miller-Jones, J. C. A., Sokolowski, M., James, C. W., Bhat, N. D. R., Swainston, N. A., Ung, D., and Meyers, B. W.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present a targeted search for low-frequency (144--215\,MHz) FRB emission from five repeating FRBs using 23.3\,hr of archival data taken with the Murchison Widefield Array (MWA) Voltage Capture System (VCS) between 2014 September and 2020 May. This is the first time that the MWA VCS has been used to search for FRB signals from known repeaters, which enables much more sensitive FRB searches than previously performed with the standard MWA correlator mode. We performed a standard single pulse search with a temporal and spectral resolution of $400\,\mu$s and 10\,kHz, respectively, over a $100\,\text{pc}\,\text{cm}^{-3}$ dispersion measure (DM) range centred at the known DM of each studied repeating FRB. No FRBs exceeding a $6\sigma$ threshold were detected. The fluence upper limits in the range of 32--1175\,Jy\,ms and 36--488\,Jy\,ms derived from 10 observations of FRB 20190711A and four observations of FRB 20201124A respectively, allow us to constrain the spectral indices of their bursts to $\gtrsim-1$ if these two repeaters were active during the MWA observations. If free-free absorption is responsible for our non-detection, we can constrain the size of the absorbing medium in terms of the electron temperature $T$ to $<1.00\times(T/10^4\text{K})^{-1.35}\,\text{pc}$, $<0.92\times(T/10^4\text{K})^{-1.35}\,\text{pc}$ and $<[0.22\text{--}2.50]\times(T/10^4\text{K})^{-1.35}\,\text{pc}$ for FRB 20190117A, 20190711A, and 20201124A, respectively. However, given that the activities of these repeaters are not well characterised, our non-detections could also suggest they were inactive during the MWA observations., Comment: Accepted for publication in MNRAS

Published
2022
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4. pulsar_spectra: A pulsar flux density catalogue and spectrum fitting repository

Author

Swainston, N. A., Lee, C. P., McSweeney, S. J., and Bhat, N. D. R.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We present the pulsar_spectra software repository, an open-source pulsar flux density catalogue and automated spectral fitting software that finds the best spectral model and produces publication-quality plots. The Python-based software includes features that enable users in the astronomical community to add newly published spectral measurements to the catalogue as they become available. The spectral fitting software is an implementation of the method described in Jankowski et al. (2018) which uses robust statistical methods to decide on the best-fitting model for individual pulsar spectra. pulsar_spectra is motivated by the need for a centralised repository for pulsar flux density measurements to make published measurements more accessible to the astronomical community and provide a suite of tools for measuring spectra.

Published
2022
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5. Spectral analysis of 22 radio pulsars using SKA-Low precursor stations

Author

Lee, C. P., Bhat, N. D. R., Sokolowski, M., Swainston, N. A., Ung, D., Magro, A., and Chiello, R.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the first observational study of pulsars performed with the second-generation precursor stations to the low-frequency component of the Square Kilometre Array (SKA-Low): the Aperture Array Verification System 2 (AAVS2) and the Engineering Development Array 2 (EDA2). Using the SKA-Low stations, we have observed 100 southern-sky pulsars between 70-350 MHz, including follow-up observations at multiple frequencies for a selected sample of bright pulsars. These observations have yielded detections of 22 pulsars, including the lowest-frequency detections ever published for 6 pulsars, despite the modest sensitivity of initial system where the recording bandwidth is limited to ~1 MHz. By comparing simultaneous flux density measurements obtained with the SKA-Low stations and performing rigorous electromagnetic simulations, we verify the accuracy of the SKA-Low sensitivity simulation code presented in Sokolowski et al. (2022). Furthermore, we perform model fits to the radio spectra of the detected pulsars using the method developed by Jankowski et al. (2018), including 9 pulsars which were not fitted in the original work. We robustly classify the spectra into 5 morphological classes and find that all but one pulsar exhibit deviations from simple power-law behaviour. These findings suggest that pulsars with well-determined spectra are more likely to show spectral flattening or turn-over than average. Our work demonstrates how SKA-Low stations can be meaningfully used for scientifically useful measurements and analysis of pulsar radio spectra, which are important inputs for informing pulsar surveys and science planned with the SKA-Low., Comment: 16 pages, 6 figures, 4 tables, accepted for publication in PASA

Published
2022
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6. High time resolution search for prompt radio emission from the long GRB 210419A with the Murchison Widefield Array

Author

Tian, J., Anderson, G. E., Hancock, P. J., Miller-Jones, J. C. A., Sokolowski, M., Swainston, N. A., Rowlinson, A., Williams, A., Kaplan, D. L., Hurley-Walker, N., Morgan, J., Bhat, N. D. R., Ung, D., Tingay, S., Bannister, K. W., Bell, M. E., Meyers, B. W., and Walker, M.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present a low-frequency (170\textendash200\,MHz) search for prompt radio emission associated with the long GRB 210419A using the rapid-response mode of the Murchison Widefield Array (MWA), triggering observations with the Voltage Capture System (VCS) for the first time. The MWA began observing GRB 210419A within 89\,s of its detection by \textit{Swift}, enabling us to capture any dispersion delayed signal emitted by this GRB for a typical range of redshifts. We conducted a standard single pulse search with a temporal and spectral resolution of $100\,\upmu$s and 10\,kHz over a broad range of dispersion measures from 1 to $5000\,\text{pc}\,\text{cm}^{-3}$, but none were detected. However, fluence upper limits of $77\text{--}224$\,Jy\,ms derived over a pulse width of $0.5\text{--}10$\,ms and a redshift of $0.6

Published
2022
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7. MWA Tied-Array Processing IV: A Multi-Pixel Beamformer for Pulsar Surveys and Ionospheric Corrected Localisation

Author

Swainston, N. A., Bhat, N. D. R., Morrison, I. S., McSweeney, S. J., Ord, S. M., Tremblay, S. E., and Sokolowski, M.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

The Murchison Widefield Array (MWA) is a low-frequency aperture array capable of high-time and frequency resolution astronomy applications such as pulsar studies. The large field-of-view of the MWA (hundreds of square degrees) can also be exploited to attain fast survey speeds for all-sky pulsar search applications, but to maximise sensitivity requires forming thousands of tied-array beams from each voltage-capture observation. The necessity of using calibration solutions that are separated from the target observation both temporally and spatially makes pulsar observations vulnerable to uncorrected, frequency-dependent positional offsets due to the ionosphere. These offsets may be large enough to move the source away from the centre of the tied-array beam, incurring sensitivity drops of $\sim$30-50\% in Phase II extended array configuration. We analyse these offsets in pulsar observations and develop a method for mitigating them, improving both the source position accuracy and the sensitivity. This analysis prompted the development of a multi-pixel beamforming functionality that can generate dozens of tied-array beams simultaneously, which runs a factor of ten times faster compared to the original single-pixel version. This enhancement makes it feasible to observe multiple pulsars within the vast field of view of the MWA and supports the ongoing large-scale pulsar survey efforts with the MWA. We explore the extent to which ionospheric offset correction will be necessary for the MWA Phase III and the low-frequency Square Kilometre Array (SKA-Low)., Comment: 10 pages, 5 figures

Published
2022
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8. Discovery of a steep-spectrum low-luminosity pulsar with the Murchison Widefield Array

Author

Swainston, N. A., Bhat, N. D. R., Sokolowski, M., McSweeney, S. J., Kudale, S., Dai, S., Smith, K. R., Morrison, I. S., Shannon, R. M., van Straten, W., Xue, M., Ord, S. M., Tremblay, S. E., Meyers, B. W., Williams, A., Sleap, G., Johnston-Hollitt, M., Kaplan, D. L., Tingay, S. J., and Wayth, R. B.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We report the discovery of the first new pulsar with the Murchison Widefield Array (MWA), PSR J0036$-$1033, a long-period (0.9 s) nonrecycled pulsar with a dispersion measure (DM) of 23.1 ${\rm pc\,cm^{-3}}$. It was found after processing only a small fraction ($\sim$1%) of data from an ongoing all-sky pulsar survey. Follow-up observations have been made with the MWA, the upgraded Giant Metrewave Radio Telescope (uGMRT), and the Parkes 64 m telescopes, spanning a frequency range from $\sim$150 MHz to 4 GHz. The pulsar is faint, with an estimated flux density ($S$) of $\sim$1 mJy at 400 MHz and a spectrum $S(\nu)\,\propto\,\nu^{-2.0 \pm 0.2}$, where $\nu$ is frequency. The DM-derived distance implies that it is also a low-luminosity source ($\sim$ 0.1 ${\rm mJy\,kpc^2}$ at 1400 MHz). The analysis of archival MWA observations reveals that the pulsar's mean flux density varies by up to a factor of $\sim$5-6 on timescales of several weeks to months. By combining MWA and uGMRT data, the pulsar position was determined to arcsecond precision. We also report on polarization properties detected in the MWA and Parkes bands. The pulsar's nondetection in previous pulsar and continuum imaging surveys, the observed high variability, and its detection in a small fraction of the survey data searched to date, all hint at a larger population of pulsars that await discovery in the southern hemisphere, with the MWA and the future low-frequency Square Kilometre Array., Comment: 12 pages, 6 figures, 2 tables, Accepted for publication in The ApJ Letters

Published
2021
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9. MWA Tied-Array Processing II: Polarimetric Verification and Analysis of two Bright Southern Pulsars

Author

Xue, Mengyao, Ord, S. M., Tremblay, S. E., Bhat, N. D. R., Sobey, C., Meyers, B. W., McSweeney, S. J., and Swainston, N. A.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Polarimetric studies of pulsars at low radio frequencies provide important observational insights into the pulsar emission mechanism and beam models, and probe the properties of the magneto-ionic interstellar medium (ISM). Aperture arrays are the main form of next-generation low-frequency telescopes, including the Murchison Widefield Array (MWA). These require a distinctly different approach to data processing (e.g. calibration and beamforming) compared to traditional dish antennas. As the second paper of this series, we present a verification of the MWA's pulsar polarimetry capability, using two bright southern pulsars, PSRs J0742-2822 and J1752-2806. Our observations simultaneously cover multiple frequencies (76-313 MHz) and were taken at multiple zenith angles during a single night for each pulsar. We show that the MWA can be reliably calibrated for zenith angles < 45 degree and frequencies < 270 MHz. We present the polarimetric profiles for PSRs J0742-2822 and J1752-2806 at frequencies lower than 300 MHz for the first time, along with an analysis of the linear polarisation degree and pulse profile evolution with frequency. For PSR J0742-2822, the measured degree of linear polarisation shows a rapid decrease at low frequencies, in contrast with the generally expected trend, which can be attributed to depolarisation effects from small-scale, turbulent, magneto-ionic ISM components. This effect has not been widely explored for pulsars in general, and will be further investigated in future work., Comment: accepted for publication in PASA

Published
2019
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10. DNA scanner: A web application for comparing DNA synthesis feasibility, price and turnaround time across vendors

Author

Doçi, G, Fuchs, L, Kharbanda, Y, Schickling, P, Zulkower, V, Hillson, N, Oberortner, E, Swainston, N, and Kabisch, J

Abstract

DNA synthesis has become a major enabler of modern bioengineering, allowing scientists to simply order online in silico-designed DNA molecules. Rapidly decreasing DNA synthesis service prices and the concomitant increase of research and development scales bolstered by computer-aided DNA design tools and laboratory automation has driven up the demand for synthetic DNA. While vendors provide user-friendly online portals for purchasing synthetic DNA, customers still face the time-consuming task of checking each vendor of choice for their ability and pricing to synthesize the desired sequences. As a result, ordering large batches of DNA sequences can be a laborious manual procedure in an otherwise increasingly automatable workflow. Even when they are available, there is a high degree of technical knowledge and effort required to integrate vendors’ application programming interfaces (APIs) into computer-aided DNA design tools or automated lab processes. Here, we introduce DNA Scanner, a software package comprising (i) a web-based user interface enabling users to compare the feasibility, price and turnaround time of synthetic DNA sequences across selected vendors and (ii) a Python API enabling integration of these functionalities into computer-aided DNA design tools and automated lab processes. We have developed DNA Scanner to uniformly streamline interactions between synthetic DNA vendors, members of the Global Biofoundry Alliance and the scientific community at large.

Published
2020

11. A Census of Southern Pulsars at 185 MHz

Author

Xue, Mengyao, Bhat, N. D. R., Tremblay, S. E., Ord, S. M., Sobey, C., Swainston, N. A., Kaplan, D. L., Johnston, Simon, Meyers, B. W., and McSweeney, S. J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Murchison Widefield Array (MWA), and its recently-developed Voltage Capture System (VCS), facilitates extending the low-frequency range of pulsar observations at high-time and -frequency resolution in the Southern Hemisphere, providing further information about pulsars and the ISM. We present the results of an initial time-resolved census of known pulsars using the MWA. To significantly reduce the processing load, we incoherently sum the detected powers from the 128 MWA tiles, which yields ~10% of the attainable sensitivity of the coherent sum. This preserves the large field-of-view (~450 deg2 at 185 MHz), allowing multiple pulsars to be observed simultaneously. We developed a WIde-field Pulsar Pipeline (WIPP) that processes the data from each observation and automatically folds every known pulsar located within the beam. We have detected 50 pulsars to date, 6 of which are millisecond pulsars. This is consistent with our expectation, given the telescope sensitivity and the sky coverage of the processed data (~17,000 deg2). For ten pulsars, we present the lowest-frequency detections published. For a subset of the pulsars, we present multi-frequency pulse profiles by combining our data with published profiles from other telescopes. Since the MWA is a low-frequency precursor to the Square Kilometre Array (SKA), we use our census results to forecast that a survey using Phase 1 of SKA-Low (SKA1-Low) can potentially detect around 9400 pulsars.

Published
2017
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12. The Southern-sky MWA Rapid Two-metre (SMART) pulsar survey - I. Survey design and processing pipeline

Author

Bhat, N. D. R., primary, Swainston, N. A., additional, McSweeney, S. J., additional, Xue, M., additional, Meyers, B.W., additional, Kudale, S., additional, Dai, S., additional, Tremblay, S. E., additional, van Straten, W., additional, Shannon, R. M., additional, Smith, K. R., additional, Sokolowski, M., additional, Ord, S. M., additional, Sleap, G., additional, Williams, A., additional, Hancock, P. J., additional, Lange, R., additional, Tocknell, J., additional, Johnston-Hollitt, M., additional, Kaplan, D. L., additional, Tingay, S. J., additional, and Walker, M., additional

Published
2023
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13. The Southern-sky MWA Rapid Two-metre (SMART) pulsar survey—II. Survey status, pulsar census, and first pulsar discoveries

Author

Bhat, N. D. R., primary, Swainston, N. A., additional, McSweeney, S. J., additional, Xue, M., additional, Meyers, B.W., additional, Kudale, S., additional, Dai, S., additional, Tremblay, S. E., additional, van Straten, W., additional, Shannon, R. M., additional, Smith, K. R., additional, Sokolowski, M., additional, Ord, S. M., additional, Sleap, G., additional, Williams, A., additional, Hancock, P. J., additional, Lange, R., additional, Tocknell, J., additional, Johnston-Hollitt, M., additional, Kaplan, D. L., additional, Tingay, S. J., additional, and Walker, M., additional

Published
2023
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14. SYNBIOCHEM Synthetic Biology Research Centre, Manchester – A UK foundry for fine and speciality chemicals production

Author

Le Feuvre RA, Carbonell P, Currin A, Dunstan M, Fellows D, Jervis AJ, Rattray NJW, Robinson CJ, Swainston N, Vinaixa M, Williams A, Yan C, Barran P, Breitling R, Chen GG, Faulon JL, Goble C, Goodacre R, Kell DB, Micklefield J, Scrutton NS, Shapira P, Takano E, and Turner NJ

Subjects
Biotechnology, TP248.13-248.65, Biology (General), QH301-705.5
Abstract

The UK Synthetic Biology Research Centre, SYNBIOCHEM, hosted by the Manchester Institute of Biotechnology at the University of Manchester is delivering innovative technology platforms to facilitate the predictable engineering of microbial bio-factories for fine and speciality chemicals production. We provide an overview of our foundry activities that are being applied to grand challenge projects to deliver innovation in bio-based chemicals production for industrial biotechnology.

Published
2016
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16. High time resolution search for prompt radio emission from the long GRB 210419A with the Murchison Widefield Array

Author

Tian, J, primary, Anderson, G E, additional, Hancock, P J, additional, Miller-Jones, J C A, additional, Sokolowski, M, additional, Swainston, N A, additional, Rowlinson, A, additional, Williams, A, additional, Kaplan, D L, additional, Hurley-Walker, N, additional, Morgan, J, additional, Bhat, N D R, additional, Ung, D, additional, Tingay, S, additional, Bannister, K W, additional, Bell, M E, additional, Meyers, B W, additional, and Walker, M, additional

Published
2022
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20. targeted search for repeating fast radio bursts with the MWA.

Author

Tian, J, Anderson, G E, Hancock, P J, Miller-Jones, J C A, Sokolowski, M, James, C W, Bhat, N D R, Swainston, N A, Ung, D, and Meyers, B W

Subjects
ELECTRON temperature, CORRELATORS
Abstract

We present a targeted search for low-frequency (144–215 MHz) fast radio burst (FRB) emission from five repeating FRBs using 23.3 h of archival data taken with the Murchison Widefield Array (MWA) voltage capture system (VCS) between 2014 September and 2020 May. This is the first time that the MWA VCS has been used to search for FRB signals from known repeaters, which enables much more sensitive FRB searches than previously performed with the standard MWA correlator mode. We performed a standard single-pulse search with a temporal and spectral resolution of |$400\, \mu$| s and 10 kHz, respectively, over a |$100\, \text{pc}\, \text{cm}^{-3}$| dispersion measure (DM) range centred at the known DM of each studied repeating FRB. No FRBs exceeding a 6σ threshold were detected. The fluence upper limits in the range of 32–1175 and 36–488 Jy ms derived from 10 observations of FRB 20190711A and four observations of FRB 20201124A, respectively, allow us to constrain the spectral indices of their bursts to ≳−1 if these two repeaters were active during the MWA observations. If free–free absorption is responsible for our non-detection, we can constrain the size of the absorbing medium in terms of the electron temperature T to |${\lt} 1.00\times (T/10^4\,\text{K})^{-1.35}\, \text{pc}$|⁠ , |${\lt} 0.92\times (T/10^4\,\text{K})^{-1.35}\, \text{pc}$|⁠ , and |${\lt} [0.22\!-\!2.50]\times (T/10^4\,\text{K})^{-1.35}\, \text{pc}$| for FRB 20190117A, FRB 20190711A, and FRB 20201124A, respectively. However, given that the activities of these repeaters are not well characterized, our non-detections could also suggest they were inactive during the MWA observations. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Discovery of a Steep-spectrum Low-luminosity Pulsar with the Murchison Widefield Array

Author

Swainston, N. A., primary, Bhat, N. D. R., additional, Sokolowski, M., additional, McSweeney, S. J., additional, Kudale, S., additional, Dai, S., additional, Smith, K. R., additional, Morrison, I. S., additional, Shannon, R. M., additional, van Straten, W., additional, Xue, M., additional, Ord, S. M., additional, Tremblay, S. E., additional, Meyers, B. W., additional, Williams, A., additional, Sleap, G., additional, Johnston-Hollitt, M., additional, Kaplan, D. L., additional, Tingay, S. J., additional, and Wayth, R. B., additional

Published
2021
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22. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, Keating, SM, Bergmann, FT, Gillespie, CS, Malik-Sheriff, RS, Moodie, SL, Moraru, II, Myers, CJ, Olivier, BG, Schaff, JC, Smith, LP, Swat, MJ, Wilkinson, DJ, Blinov, ML, Faeder, JR, Gómez, HF, Hamm, TM, Lister, AL, Proctor, CJ, Shaffer, CA, Shapiro, BE, Sauro, HM, Doyle, JC, Adams, RR, Allen, NA, Angermann, BR, Bader, GD, Cox, CD, Denney, WS, Evelo, CT, Fleming, RM, Harris, LA, Heavner, BD, Hlavacek, WS, Hyduke, DR, Karp, PD, Karr, JR, Kell, DB, Loew, LM, Monteiro, PT, Natarajan, KN, Nielsen, PM, Phair, RD, Rohwer, JM, Ruebenacker, OA, Sealfon, SC, Stefan, MI, Sullivan, DP, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, Keating, SM, Bergmann, FT, Gillespie, CS, Malik-Sheriff, RS, Moodie, SL, Moraru, II, Myers, CJ, Olivier, BG, Schaff, JC, Smith, LP, Swat, MJ, Wilkinson, DJ, Blinov, ML, Faeder, JR, Gómez, HF, Hamm, TM, Lister, AL, Proctor, CJ, Shaffer, CA, Shapiro, BE, Sauro, HM, Doyle, JC, Adams, RR, Allen, NA, Angermann, BR, Bader, GD, Cox, CD, Denney, WS, Evelo, CT, Fleming, RM, Harris, LA, Heavner, BD, Hlavacek, WS, Hyduke, DR, Karp, PD, Karr, JR, Kell, DB, Loew, LM, Monteiro, PT, Natarajan, KN, Nielsen, PM, Phair, RD, Rohwer, JM, Ruebenacker, OA, Sealfon, SC, Stefan, MI, and Sullivan, DP

Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Published
2020

23. An integrated national scale SARS-CoV-2 genomic surveillance network

Author

Aanensen, DM, Abudahab, K, Adams, A, Afifi, S, Alam, MT, Alderton, A, Alikhan, N-F, Allan, J, Almsaud, M, Alrezaihi, A, Alruwaili, M, Amato, R, Andersson, M, Angyal, A, Aranday-Cortes, E, Ariani, C, Armstrong, SD, Asamaphan, P, Attwood, S, Aydin, A, Badhan, A, Baker, D, Baker, P, Balcazar, CE, Ball, J, Barton, AE, Bashton, M, Baxter, L, Beale, M, Beaver, C, Beckett, A, Beer, R, Beggs, A, Bell, A, Bellis, KL, Bentley, EG, Berriman, M, Betteridge, E, Bibby, D, Bicknell, K, Birchley, A, Black, G, Blane, B, Bloomfield, S, Bolt, F, Bonsall, DG, Bosworth, A, Bourgeois, Y, Boyd, O, Bradshaw, D, Breuer, J, Bridgewater, H, Brooks, T, Broos, A, Brown, JR, Brown, RL, Brunker, K, Bucca, G, Buck, D, Bull, M, Butcher, E, Caddy, SL, Caller, LG, Cambell, S, Carlile, M, Carmichael, S, Carrilero, L, Castellano, S, Chaloner, J, Chand, M, Chapman, MR, Chappell, J, Charles, I, Chauhan, AJ, Chawla, A, Cheng, E, Churcher, CM, Clark, G, Clark, JJ, Collins, J, Colquhoun, R, Connor, TR, Constantinidou, C, Coombes, J, Corden, S, Cottrell, S, Cowell, A, Curran, MD, Curran, T, Dabrera, G, Danesh, J, Darby, AC, De Cesare, M, Martins, LDO, De Silva, TI, Debebe, B, Dervisevic, S, Dewar, RA, Dia, M, Dorman, M, Dougan, G, Dover, L, Downing, F, Drury, E, Du Plessis, L, Dyal, PL, Eccles, R, Edwards, S, Ellaby, N, Elliott, S, Eltringham, G, Elumogo, N, Essex, S, Evans, CM, Evans, J, Nascimento, FF, Fairley, DJ, Farr, B, Feltwell, T, Ferguson, N, Filipe, ADS, Findlay, J, Forrest, LM, Forrest, S, Foulser, L, Francois, S, Fraser, C, Frost, L, Gallagher, E, Gallagher, MD, Garcia-Dorival, I, Gaskin, A, Gatica-Wilcox, B, Gavriil, A, Geidelberg, L, Gemmell, M, Gerada, A, Gifford, L, Gilbert, L, Gilmore, P, Gilroy, R, Girgis, S, Glaysher, S, Golubchik, T, Goncalves, S, Goodfellow, I, Goodwin, S, Graham, C, Graham, L, Grammatopoulos, D, Green, A, Green, LR, Greenaway, J, Gregory, R, Groves, DC, Groves, N, Guest, M, Gunson, R, Haldenby, S, Hall, G, Hamilton, WL, Han, X, Harris, KA, Harrison, EM, Hartley, C, Herrera, C, Hesketh, A, Heyburn, D, Hill, V, Hiscox, JA, Holden, M, Holmes, A, Holmes, N, Holt, GS, Hopes, R, Hosmillo, M, Houldcroft, CJ, Howson-Wells, H, Hubb, J, Hughe, J, Hughes, M, Hutchings, S, Impey, R, Iturriza-Gomara, M, Jackson, A, Jackson, B, Jackson, DK, Jahun, AS, James, K, Jamrozy, D, Jeffries, A, Jesudason, N, John, M, Johnson, J, Johnson, KJ, Johnson, N, Johnston, I, Jones, B, Jones, R, Jones, S, Jorgensen, D, Kane, L, Kay, GL, Kay, S, Keatley, J-P, Keeley, AJ, Khakh, M, Khokhar, FA, Kitchen, C, Knight, B, Kolyva, A, Kraemer, M, Kristiansen, M, Kumziene-Summerhayes, S, Kwiatkowski, D, Lackenby, A, Langford, C, Lawniczak, M, Thanh, L-V, Lee, D, Letchford, L, Li, K, Li, L, Liggett, S, Lindsey, BB, Livett, R, Lloyd, A, Lo, S, Lockhart, M, Loh, J, Loman, NJ, Loose, M, Lucaci, A, Ludden, C, Luu, L, Lyons, RA, MacIntyre-Cockett, G, MacLean, A, Mair, D, Maksimovic, J, Manley, R, Manso, C, Manson, J, Martincorena, I, Masoli, J, Mather, AE, Mbisa, T, McCluggage, K, McClure, P, McCrone, JT, McDonald, S, McHugh, MP, McKenna, JM, McMinn, L, McMurray, C, Meadows, L, Menegazzo, M, Meredith, LW, Merrick, I, Mestek-Boukhibar, L, Miah, S, Michell, S, Michelsen, ML, Molnar, Z, Moore, C, Moore, N, Morgan, M, Morgan, S, Muddyman, D, Muir, DA, Muir, P, Myers, R, Nastouli, E, Naydenova, P, Nelson, A, Nelson, C, Nelson, R, Nicholls, S, Nichols, J, Niebel, M, Niola, P, Nomikou, K, O'Grady, J, O'Toole, AN, O'Toole, E, Olateju, C, Orton, RJ, Osman, H, Ott, S, Pacchiarini, N, Padgett, D, Page, AJ, Palmer, S, Panchbhaya, YN, Pandey, S, Park, N, Parker, MD, Parkhill, J, Parr, YA, Parsons, PJ, Partridge, DG, Patel, M, Patterson, S, Payne, B, Peacock, SJ, Penrice-Randal, R, Perry, M, Platt, S, Poplawski, R, Prakash, R, Prestwood, L, Price, A, Price, JR, Puethe, C, Pybus, O, Pymont, H, Quail, M, Quick, J, Raghwani, J, Ragonnet-Cronin, M, Rahman, S, Rainbow, L, Rajatileka, S, Rambaut, A, Ramsay, M, Randell, PA, Randle, NP, Raviprakash, V, Raza, M, Silva, PR, Rey, S, Richter, A, Robertson, DL, Robinson, TI, Robson, SC, Rooke, S, Rowan, A, Rowe, W, Roy, S, Rudder, S, Ruis, C, Sang, F, Scarlett, G, Schaefer, U, Scott, C, Scott, G, Sethi, D, Shaaban, S, Shah, R, Sharma, P, Shawli, GT, Shepherd, J, Sherriff, N, Shirley, L, Sillitoe, J, Simpson, DA, Singer, JB, Siveroni, I, Smith, C, Smith, CP, Smith, DL, Smith, N, Smith, W, Smith-Palmer, A, Smollett, K, Southgate, J, Spellman, K, Spencer-Chapman, M, Sridhar, S, Stanley, R, Stark, R, Stewart, JP, Stockton, J, Stuart, C, Studholme, D, Swainston, N, Swindells, E, Taha, Y, Tariq, MA, Taylor, B, Taylor, GP, Taylor, S, Taylor-Joyce, G, Tedim, AP, Temperton, B, Templeton, KE, Thomson, EC, Thomson, NM, Thornton, A, Thurston, S, Todd, J, Tong, L, Tonkin-Hill, G, Torok, ME, Trebes, A, Trotter, AJ, Tsoleridis, T, Tucker, RM, Tutill, HJ, Underwood, A, Unnikrishnan, M, Vamos, E, Vasylyeva, T, Vattipally, S, Victoria, A, Vipond, B, Volz, EM, Wain, J, Wang, D, Warwick-Dugdale, J, Wastnedge, E, Watkins, J, Watts, J, Webber, M, Weeks, S, Weldon, D, Whitehead, M, Williams, CA, Williams, C, Williams, D, Williams, R, Williams, TC, Wise, E, Wright, V, Wyles, MD, Wyllie, S, Yakovleva, A, Yasir, M, Yeats, C, Yew, WC, Young, GR, Yu, X, and Zarebski, A

Subjects
Microbiology (medical), Scale (ratio), SARS-CoV-2, viruses, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), COVID-19, COVID-19 Genomics UK (COG-UK) consortiumcontact@cogconsortium.uk, C500, Genome, Viral, Genomics, Biology, C700, Microbiology, Article, Infectious Diseases, Virology, Humans, Cartography
Abstract

The Coronavirus Disease 2019 (COVID-19) Genomics UK Consortium (COG-UK) was launched in March, 2020, with £20 million support from UK Research and Innovation, the UK Department of Health and Social Care, and Wellcome Trust. The goal of this consortium is to sequence severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for up to 230 000 patients, health-care workers, and other essential workers in the UK with COVID-19, which will help to enable the tracking of SARS-CoV-2 transmission, identify viral mutations, and integrate with health data to assess how the viral genome interacts with cofactors and consequences of COVID-19.

Published
2020
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25. Engineering the missing Link in Biosynthetic (-)-Menthol Production: Bacterial Isopulegone Isomerase

Author

Universitat Rovira i Virgili, Currin A; Dunstan M; Johannissen L; Hollywood K; Vinaixa M; Jervis A; Swainston N; Rattray N; Gardiner J; Kell D; Takano E; Toogood H; Scrutton N, Universitat Rovira i Virgili, and Currin A; Dunstan M; Johannissen L; Hollywood K; Vinaixa M; Jervis A; Swainston N; Rattray N; Gardiner J; Kell D; Takano E; Toogood H; Scrutton N

Abstract

The realization of a synthetic biology approach to microbial (1R,2S,5R)-(-)-menthol (1) production relies on the identification of a gene encoding an isopulegone isomerase (IPGI), the only enzyme in the Mentha piperita biosynthetic pathway as yet unidentified. We demonstrate that ?5-3-ketosteroid isomerase (KSI) from Pseudomonas putida can act as an IPGI, producing (R)-(+)-pulegone ((R)-2) from (+)-cis-isopulegone (3). Using a robotics-driven semirational design strategy, we identified a key KSI variant encoding four active site mutations, which confer a 4.3-fold increase in activity over the wild-type enzyme. This was assisted by the generation of crystal structures of four KSI variants, combined with molecular modeling of 3 binding to identify key active site residue targets. The KSI variant was demonstrated to function efficiently within cascade biocatalytic reactions with downstream Mentha enzymes pulegone reductase and (-)-menthone:(-)-menthol reductase to generate 1 from 3. This study introduces the use of a recombinant IPGI, engineered to function efficiently within a biosynthetic pathway for the production of 1 in microorganisms.

Published
2018

26. A Census of Southern Pulsars at 185 MHz

Author

Xue, M., Bhat, Ramesh, Tremblay, S., Ord, S., Sobey, Charlotte, Swainston, N., Kaplan, D., Johnston, S., Meyers, B., McSweeney, S., Xue, M., Bhat, Ramesh, Tremblay, S., Ord, S., Sobey, Charlotte, Swainston, N., Kaplan, D., Johnston, S., Meyers, B., and McSweeney, S.

Abstract

The Murchison Widefield Array, and its recently developed Voltage Capture System, facilitates extending the low-frequency range of pulsar observations at high-time and -frequency resolution in the Southern Hemisphere, providing further information about pulsars and the ISM. We present the results of an initial time-resolved census of known pulsars using the Murchison Widefield Array. To significantly reduce the processing load, we incoherently sum the detected powers from the 128 Murchison Widefield Array tiles, which yields ~10% of the attainable sensitivity of the coherent sum. This preserves the large field-of-view (~450 deg 2 at 185 MHz), allowing multiple pulsars to be observed simultaneously. We developed a WIde-field Pulsar Pipeline that processes the data from each observation and automatically folds every known pulsar located within the beam. We have detected 50 pulsars to date, 6 of which are millisecond pulsars. This is consistent with our expectation, given the telescope sensitivity and the sky coverage of the processed data (~17 000 deg 2 ). For 10 pulsars, we present the lowest frequency detections published. For a subset of the pulsars, we present multi-frequency pulse profiles by combining our data with published profiles from other telescopes. Since the Murchison Widefield Array is a low-frequency precursor to the Square Kilometre Array, we use our census results to forecast that a survey using the low-frequency component of the Square Kilometre Array Phase 1 can potentially detect around 9 400 pulsars.

Published
2017

27. Challenges in microbial ecology: Building predictive understanding of community function and dynamics

Author

Widder, S, Allen, RJ, Pfeiffer, T, Curtis, TP, Wiuf, C, Sloan, WT, Cordero, OX, Brown, SP, Momeni, B, Shou, W, Kettle, H, Flint, HJ, Hass, AF, Laroche, B, Kreft, JU, Rainey, PB, Freilich, S, Shuster, S, Milferstedt, K, Van der Meer, JR, Grosskopf, T, Huisman, J, Free, A, Picioreanu, C, Quince, C, Klapper, I, Labarthe, S, Smets, B, Wang, H, Allison, SD, Chong, J, Lagomarsion, MC, Croze, OA, Hamelin, J, Harmand, J, Hoyle, R, Hwa, TT, Jin, Q, Johson, DR, Lorenzo, VD, Mobilia, M, Murphy, B, Peaudecerf, F, Prosser, JI, Quinn, RA, Ralser, M, Smith, AG, Steyer, JP, Swainston, N, Tarnita, CE, Trably, E, Warren, PB, Wilmes, P, Soyer, O, CUBE, Department of Microbiology and Ecosystem Science, Medizinische Universität Wien = Medical University of Vienna, SUPA, School of Physics and Astronomy, University of Edinburgh, New Zealand Institute for Advanced Study, School of Civil Engineering and Geosciences, University of Newcastle upon Tyne, Department of Mathematical Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Department of Civil and Environmental Engineering [Cambridge, USA] (CEE), Massachusetts Institute of Technology (MIT), Centre for Immunity, Infection and Evolution, School of Biological Sciences, Department of Biology, Boston College (BC), Fred Hutchinson Cancer Research Center [Seattle] (FHCRC), Division of Basic Sciences, Biomathematics and Statistics Scotland, Rowett Institute of Nutrition and Health, University of Aberdeen, Biology Department [San Diego], San Diego State University (SDSU), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), School of Biosciences, University of Birmingham, Newe Ya’ar Research Center, Agricultural Research Organization, Department of Bioinformatics, Friedrich-Schiller-Universität Jena, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Department of Fundamental Microbiology [Lausanne], Université de Lausanne (UNIL), School of Life Sciences, University of Warwick, Department of Aquatic Microbiology, University of Amsterdam, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Science, Department of Biotechnology, Delft University of Technology (TU Delft), Warwick Medical School, University of Warwick [Coventry], Department of Mathematics, Temple University [Philadelphia], Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), Department of Environmental Engineering, Technical University of Denmark [Lyngby] (DTU), Department of Systems Biology, Columbia University [New York], Genomic Physics [LCQB] (LCQB-Gphi), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Isaac Newton Institute of Mathematical Sciences, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Department of Civil and Environmental Engineering [Cambridge] (CEE), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Lausanne = University of Lausanne (UNIL), University of Amsterdam [Amsterdam] (UvA), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Widder, Stefanie [0000-0003-0733-5666], Brown, Sam P [0000-0003-1892-9275], Momeni, Babak [0000-0003-1271-5196], Kreft, Jan-Ulrich [0000-0002-2351-224X], Smets, Barth F [0000-0003-4119-6292], Soyer, Orkun S [0000-0002-9504-3796], Apollo - University of Cambridge Repository, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Aquatic Microbiology (IBED, FNWI), Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, and Cordero Sanchez, Otto X.

Subjects
0301 basic medicine, microbial, Mini Review, Ecology (disciplines), media_common.quotation_subject, Air Microbiology, Biology, Microbiology, 03 medical and health sciences, Microbial ecology, challenge in microbial, predictive, ecology, challenge, Animals, Humans, Seawater, SDG 14 - Life Below Water, QA, Function (engineering), Ecosystem, Soil Microbiology, Ecology, Evolution, Behavior and Systematics, media_common, Structure (mathematical logic), Ecology, Microbiology and Parasitology, Models, Theoretical, 15. Life on land, Modélisation et simulation, Data science, Method development, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Microbiologie et Parasitologie, QR, 030104 developmental biology, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, 13. Climate action, Modeling and Simulation, Key (cryptography), Research questions, Model building
Abstract

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved., United States. Army Research Office (W911NF-14-1-0445)

Published
2016
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28. A consensus yeast metabolic network obtained from a community approach to systems biology

Author

Herrgard, M.J., Swainston, N., Dobson, P., Dunn, W.B., Arga, K.Y., Arvas, M., Bluthgen, N., Borger, S., Costenoble, E.R., Heinemann, M., Hucka, M., Li, P., Liebermeister, W., Mo, M.L., Oliveira, A.P., Petranovic, D., Pettifer, S., Simeonides, E., Smallbone, K., Spasi, I., Weichart, D., Brent, R., Broomhead, D.S., Westerhoff, H.V., Kirdar, B., Penttila, M., Klipp, E., Paton, N., Palsson, B.O., Sauer, U., Oliver, S.G., Mendes, P., Nielsen, J., Kell, D.B., and Molecular Cell Physiology

Subjects
ComputingMethodologies_PATTERNRECOGNITION
Abstract

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms. © 2008 Nature Publishing Group.

Published
2008
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29. A Census of Southern Pulsars at 185 MHz

Author

Xue, Mengyao, primary, Bhat, N. D. R., additional, Tremblay, S. E., additional, Ord, S. M., additional, Sobey, C., additional, Swainston, N. A., additional, Kaplan, D. L., additional, Johnston, Simon, additional, Meyers, B. W., additional, and McSweeney, S. J., additional

Published
2017
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30. Enzyme kinetics informatics: from instrument to browser

Author

Swainston, N., Golebiewski,N., Messiha, H.L., Malys, N., Kania, R., Kengne, S., Krebs, O., Mir, S., Sauer-Danzwith, H., Smallbone, K., Weidemann, A., Wittig, U., Kell, D.B., Mendes, P., Müller, W., Paton, N.W., Rojas, I.

Abstract

A limited number of publicly available resources provide access to enzyme kinetic parameters. These have been compiled through manual data mining of published papers, not from the original, raw experimental data from which the parameters were calculated. This is largely due to the lack of software or standards to support the capture, analysis, storage and dissemination of such experimental data. Introduced here is an integrative system to manage experimental enzyme kinetics data from instrument to browser. The approach is based on two interrelated databases: the existing SABIO-RK database, containing kinetic data and corresponding metadata, and the newly introduced experimental raw data repository, MeMo-RK. Both systems are publicly available by web browser and web service interfaces and are configurable to ensure privacy of unpublished data. Users of this system are provided with the ability to view both kinetic parameters and the experimental raw data from which they are calculated, providing increased confidence in the data. A data analysis and submission tool, the kineticswizard, has been developed to allow the experimentalist to perform data collection, analysis and submission to both data resources. The system is designed to be extensible, allowing integration with other manufacturer instruments covering a range of analytical techniques.

Published
2010

31. A community-driven global reconstruction of human metabolism.

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) [research center], Luxembourg Centre for Systems Biomedicine (LCSB): Molecular Systems Physiology (Thiele Group) [research center], Thiele, Ines(*), Swainston, N.(*), Fleming, Ronan MT, Hoppe, A., Sahoo, Swagatika, Aurich, Maike Kathrin, Haraldsdottir, Hulda, Mo, M. L., Rolfsson, O., Stobbe, M. D., Thorleifsson, S. G., Agren, R., Bölling, C., Bordel, S., Chavali, A. K., Dobson, P., Dunn, W. B., Endler, L., Hala, D., Hucka, M., Hull, D., Jameson, D., Jamshidi, N., Jonsson, J. J., Juty, N., Keating, S., Nookaew, I., Le Novère, N., Malys, N., Mazein, A., Papin, J. A., Price, N. D., Selkov, E. Sr, Sigurdsson, M. I., Simeonidis, Evangelos, Sonnenschein, N., Smallbone, K., Sorokin, A., van Beek, J. H., Weichart, D., Goryanin, I., Nielsen, J., Westerhoff, H. V., Kell, D. B., Mendes, P., Palsson, B. O., Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) [research center], Luxembourg Centre for Systems Biomedicine (LCSB): Molecular Systems Physiology (Thiele Group) [research center], Thiele, Ines(*), Swainston, N.(*), Fleming, Ronan MT, Hoppe, A., Sahoo, Swagatika, Aurich, Maike Kathrin, Haraldsdottir, Hulda, Mo, M. L., Rolfsson, O., Stobbe, M. D., Thorleifsson, S. G., Agren, R., Bölling, C., Bordel, S., Chavali, A. K., Dobson, P., Dunn, W. B., Endler, L., Hala, D., Hucka, M., Hull, D., Jameson, D., Jamshidi, N., Jonsson, J. J., Juty, N., Keating, S., Nookaew, I., Le Novère, N., Malys, N., Mazein, A., Papin, J. A., Price, N. D., Selkov, E. Sr, Sigurdsson, M. I., Simeonidis, Evangelos, Sonnenschein, N., Smallbone, K., Sorokin, A., van Beek, J. H., Weichart, D., Goryanin, I., Nielsen, J., Westerhoff, H. V., Kell, D. B., Mendes, P., and Palsson, B. O.

Abstract

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus ‘metabolic reconstruction’, which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

Published
2013

32. Using Participatory Design Methods to Engage the Uninterested

Author

Halskov, K, WInschiers-Theophilus, H, Lee, Y, Simonsen, J, Bodker, K, Nicholas, M, Hagen, P, Rahilly, K, Swainston, N, Halskov, K, WInschiers-Theophilus, H, Lee, Y, Simonsen, J, Bodker, K, Nicholas, M, Hagen, P, Rahilly, K, and Swainston, N

Abstract

Engaging young people on issues relating to mental health can be challenging. Although young people have their own health and wellbeing goals, their perception of these issues can be radically different to those of mental health professionals. The Inspire Foundation is a not-for-profit organisation committed to working in partnership with young people to understand their needs and perspectives on mental health and wellbeing, and to designing and delivering relevant services with those young people. This case study documents how participatory design techniques and principles were used to engage young people who have not used mental health services in designing services that would be relevant to them. Specifically, we will share the methods we used in a series of co-design workshops to engage young people in the redesign of the online service ReachOut.com, and the role participatory design principles, such as play, co-operative and mutual learning and design-by-doing had in making these workshops a success.

Published
2012

33. Systematic integration of experimental data and models in systems biology.

Author

Li, P., Dada, J.O., Jameson, D., Spasic, I., Swainston, N., Carroll, K., Dunn, W., Khan, F., Messiha, E., Simeonides, E., Weichart, D., Winder, C., Broomhead, D.S., Goble, C.A., Gaskell, S.J., Kell, D.B., Westerhoff, H.V., Mendes, P., Paton, N.W., Li, P., Dada, J.O., Jameson, D., Spasic, I., Swainston, N., Carroll, K., Dunn, W., Khan, F., Messiha, E., Simeonides, E., Weichart, D., Winder, C., Broomhead, D.S., Goble, C.A., Gaskell, S.J., Kell, D.B., Westerhoff, H.V., Mendes, P., and Paton, N.W.

Abstract

Background: The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.Results: Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis.Conclusions: Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system. © 2010 Li et al; licensee BioMed Central Ltd.

Published
2010
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34. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics

Author

Brown, M., primary, Dunn, W. B., additional, Dobson, P., additional, Patel, Y., additional, Winder, C. L., additional, Francis-McIntyre, S., additional, Begley, P., additional, Carroll, K., additional, Broadhurst, D., additional, Tseng, A., additional, Swainston, N., additional, Spasic, I., additional, Goodacre, R., additional, and Kell, D. B., additional

Published
2009
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37. Recon 2.2: from reconstruction to model of human metabolism

Author

Swainston N, Smallbone K, Hefzi H, Pd, Dobson, Brewer J, Hanscho M, Dc, Zielinski, Ks, Ang, Nj, Gardiner, Jm, Gutierrez, Kyriakopoulos S, Lakshmanan M, Li S, Jk, Liu, Vs, Martínez, Ca, Orellana, Le, Quek, Thomas A, Jürgen Zanghellini, and Borth N

39. The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks

Author

Swainston, N., Smallbone, K., Mendes, P., Douglas Kell, and Paton, N.

Subjects
Medicine(all), Systems Biology, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Data processing, computer science, computer systems, General Medicine, Saccharomyces cerevisiae, ResearchInstitutes_Networks_Beacons/manchester_institute_of_biotechnology, Manchester Institute of Biotechnology, Computer Simulation, TP248.13-248.65, Metabolic Networks and Pathways, Software, Biotechnology
Abstract

Summary The generation and use of metabolic network reconstructions has increased over recent years. The development of such reconstructions has typically involved a time-consuming, manual process. Recent work has shown that steps undertaken in reconstructing such metabolic networks are amenable to automation. The SuBliMinaL Toolbox (http://www.mcisb.org/subliminal/) facilitates the reconstruction process by providing a number of independent modules to perform common tasks, such as generating draft reconstructions, determining metabolite protonation state, mass and charge balancing reactions, suggesting intracellular compartmentalisation, adding transport reactions and a biomass function, and formatting the reconstruction to be used in third-party analysis packages. The individual modules manipulate reconstructions encoded in Systems Biology Markup Language (SBML), and can be chained to generate a reconstruction pipeline, or used individually during a manual curation process. This work describes the individual modules themselves, and a study in which the modules were used to develop a metabolic reconstruction of Saccharomyces cerevisiae from the existing data resources KEGG and MetaCyc. The automatically generated reconstruction is analysed for blocked reactions, and suggestions for future improvements to the toolbox are discussed.

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40. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Author

Herrgård, M.J., Swainston, N., Dobson, P., Dunn, W.B., Arga, K.Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novère, N., Li, P, Liebermeister, W., Mo, M.L., Oliveira, A.P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasić, I., Weichart, D., Brent, R., Broomhead, D.S., Westerhoff, H.V., Kırdar, B.I., Penttilä, M., Klipp, E., Palsson, B.Ø., Sauer, U., Oliver, S.G., Mendes, P., Nielsen, J., Kell, D.B., Herrgård, M.J., Swainston, N., Dobson, P., Dunn, W.B., Arga, K.Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novère, N., Li, P, Liebermeister, W., Mo, M.L., Oliveira, A.P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasić, I., Weichart, D., Brent, R., Broomhead, D.S., Westerhoff, H.V., Kırdar, B.I., Penttilä, M., Klipp, E., Palsson, B.Ø., Sauer, U., Oliver, S.G., Mendes, P., Nielsen, J., and Kell, D.B.

Abstract

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.

41. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Edda Klipp, Marco Antoniotti, Frank Bergmann, James C. Schaff, Peter D. Karp, Daniel Lucio, Kedar Nath Natarajan, Thomas M. Hamm, Leandro Watanabe, Henning Hermjakob, David Tolnay, John Wagner, Joerg Stelling, Alida Palmisano, Falk Schreiber, Yukiko Matsuoka, Harold F. Gómez, Huaiyu Mi, Carole J. Proctor, Ulrike Wittig, Neil Swainston, Jan Červený, Denis Thieffry, Piero Dalle Pezze, Julio Saez-Rodriguez, Maciej J. Swat, Bin Hu, Martina Kutmon, Thomas Pfau, Bas Teusink, Sarah M. Keating, Fedor A. Kolpakov, Andreas Dräger, Pedro Mendes, Martin Scharm, Emek Demir, Ioannis Xenarios, Christoph Flamm, Axel von Kamp, Darren J. Wilkinson, Nick Juty, Fengkai Zhang, Leonard A. Harris, Michael Schubert, Dagmar Waltemath, Lucian P. Smith, Steffen Klamt, Herbert M. Sauro, Ali Ebrahim, Wolfram Liebermeister, Christian Knüpfer, Nicolas Rodriguez, Tramy Nguyen, Naoki Tanimura, Christopher Cox, Stuart C. Sealfon, Nicholas Alexander Allen, Clemens Wrzodek, Bastian R. Angermann, Martin Meier-Schellersheim, Anna Zhukova, Jean-Baptiste Pettit, Hovakim Grabski, Devin P. Sullivan, Claudine Chaouiya, Michael L. Blinov, John Doyle, Ilya Kiselev, Roman Schulte, Alex Gutteridge, Mélanie Courtot, Eric Mjolsness, Finja Wrzodek, Rahuman S Malik-Sheriff, Ronan M. T. Fleming, Bruce E. Shapiro, Kimberly Begley, Leslie M. Loew, Colin S. Gillespie, Ibrahim Vazirabad, Michael Hucka, Akira Funahashi, Bernhard O. Palsson, Hamid Bolouri, Tomáš Helikar, Camille Laibe, William S. Denney, Chris T. Evelo, Florian Mittag, William S. Hlavacek, Ron Henkel, Harish Dharuri, Julien Dorier, Karthik Raman, Martina Fröhlich, Conor Lawless, Rainer Machné, Falko Krause, Damon Hachmeister, Matthias König, Clifford A. Shaffer, Benjamin D. Heavner, Douglas B. Kell, Jonathan R. Karr, Mihai Glont, Lukas Endler, Melanie I. Stefan, Robert Phair, Lu Li, Henning Schmidt, Dirk Drasdo, Johan Elf, Allyson L. Lister, Hiroaki Kitano, Richard R. Adams, Oliver A. Ruebenacker, Roland Keller, Sven Sahle, Ion I. Moraru, Gary D. Bader, Poul M. F. Nielsen, Johann M. Rohwer, Johannes Eichner, Daniel R. Hyduke, James R. Faeder, Stefan Hoops, Emanuel Gonçalves, Yuichiro Inagaki, Aurélien Naldi, Koichi Takahashi, Sylvain Soliman, Brett G. Olivier, Kieran Smallbone, Stuart L. Moodie, Pedro T. Monteiro, Chris J. Myers, Martin Golebiewski, Tomas Radivoyevitch, Jeremy Zucker, Hidde de Jong, Andrew Finney, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Heidelberg University Hospital [Heidelberg], Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), European Molecular Biology Laboratory (EMBL), University of Connecticut (UCONN), National Institutes of Health [Bethesda] (NIH), Chercheur indépendant, Amazon Web Services [Seattle] (AWS), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), University of Toronto, Masaryk University [Brno] (MUNI), Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, The University of Tennessee [Knoxville], The Babraham Institute [Cambridge, UK], Oregon Health and Science University [Portland] (OHSU), Human Predictions LLC, Illumina, Swiss-Prot Group, Swiss Institute of Bioinformatics [Genève] (SIB), Modelling and Analysis for Medical and Biological Applications (MAMBA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of California [San Diego] (UC San Diego), University of California (UC), Center for Bioinformatics (ZBIT), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Uppsala University, Institut für Populationsgenetik [Vienna], Veterinärmedizinische Universität Wien, Maastricht University [Maastricht], Alpen-Adria-Universität Klagenfurt [Klagenfurt, Austria], Medizinische Universität Wien = Medical University of Vienna, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Heidelberg Institute for Theoretical Studies (HITS ), Russian-Armenian University (RAU), GlaxoSmithKline [Stevenage, UK] (GSK), GlaxoSmithKline [Headquarters, London, UK] (GSK), Microsoft Technology Licensing (MTL), Microsoft Corporation [Redmond, Wash.], Vanderbilt University School of Medicine [Nashville], University of Washington [Seattle], University of Rostock, Los Alamos National Laboratory (LANL), Lorentz Institute, Universiteit Leiden, Tegmine Therapeutics, Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget, SRI International [Menlo Park] (SRI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Liverpool, Universitätsklinikum Tübingen - University Hospital of Tübingen, Institute of Information and Computational Technologies (IICT), Max Planck Institute for Dynamics of Complex Technical Systems, Max-Planck-Gesellschaft, Max-Planck-Institut für Molekulare Genetik (MPIMG), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Humboldt University Of Berlin, Newcastle University [Newcastle], École polytechnique (X), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], The Systems Biology Institute [Tokyo] (SBI), Centro de Quimica Estrutural (CQE), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), University of Southern California (USC), Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, University of Southern Denmark (SDU), University of Auckland [Auckland], University of Utah, Virginia Tech [Blacksburg], University of Luxembourg [Luxembourg], Integrative Bioinformatics Inc [Mountain View], Cleveland Clinic, Stellenbosch University, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Universität Heidelberg [Heidelberg] = Heidelberg University, Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] (IPK-Gatersleben), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Mount Sinai School of Medicine, Department of Psychiatry-Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Manchester [Manchester], Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Encodia Inc [San Diego], Shinshu University [Nagano], University of Amsterdam [Amsterdam] (UvA), Versiti Blood Center of Wisconsin, Greifswald University Hospital, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Pacific Northwest National Laboratory (PNNL), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), Department of Bioengineering, University of California (UC)-University of California (UC), ANSYS, Virginia Polytechnic Institute and State University [Blacksburg], Eight Pillars Ltd, Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Université de Lausanne = University of Lausanne (UNIL)-Université de Lausanne = University of Lausanne (UNIL), Universität Heidelberg, Bioquant, Applied Biomathematics [New York], SimCYP Ltd, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Utah School of Medicine [Salt Lake City], University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Mizuho Information and Research Institute, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Oxford, Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), University of California [Irvine] (UC Irvine), Indian Institute of Technology Madras (IIT Madras), California State University [Northridge] (CSUN), Biotechnology and Biological Sciences Research Council (BBSRC), IBM Research [Melbourne], Benaroya Research Institute [Seattle] (BRI), Okinawa Institute of Science and Technology Graduate University, Keio University, Department of Computing and Mathematical sciences, members, SBML Level 3 Community, Université de Lausanne (UNIL), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), University of California, Universiteit Leiden [Leiden], Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Humboldt University of Berlin, Universität Heidelberg [Heidelberg], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Humboldt-Universität zu Berlin, University of California-University of California, Université de Lausanne (UNIL)-Université de Lausanne (UNIL), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Oxford [Oxford], University of California [Irvine] (UCI), Biotechnology and Biological Sciences Research Council, Computer Science, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)

Subjects
computational modeling, Medicine (General), Markup language, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, INFORMATION, Interoperability, interoperability, Review, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], ANNOTATION, 0302 clinical medicine, Software, file forma, Models, Biology (General), 0303 health sciences, Computational model, Applied Mathematics, Systems Biology, systems biology, File format, 3. Good health, Networking and Information Technology R&D, Networking and Information Technology R&D (NITRD), Computational Theory and Mathematics, SIMULATION, General Agricultural and Biological Sciences, STANDARDS, REPOSITORY, Information Systems, QH301-705.5, Bioinformatics, Systems biology, Software ecosystem, Reviews, Bioengineering, Methods & Resources, Biology, MARKUP LANGUAGE, Models, Biological, SBML Level 3 Community members, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, R5-920, Animals, Humans, SBML, reproducibility, 030304 developmental biology, ENVIRONMENT, General Immunology and Microbiology, file format, business.industry, Computational Biology, Biological, ONTOLOGY, Metabolism, Logistic Models, Biochemistry and Cell Biology, Other Biological Sciences, Software engineering, business, 030217 neurology & neurosurgery
Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.

Published
2020
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42. EnzymeML: seamless data flow and modeling of enzymatic data.

Author

Lauterbach S, Dienhart H, Range J, Malzacher S, Spöring JD, Rother D, Pinto MF, Martins P, Lagerman CE, Bommarius AS, Høst AV, Woodley JM, Ngubane S, Kudanga T, Bergmann FT, Rohwer JM, Iglezakis D, Weidemann A, Wittig U, Kettner C, Swainston N, Schnell S, and Pleiss J

Subjects
Reproducibility of Results, Databases, Factual, Kinetics, Data Management, Metadata
Abstract

The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org ., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Published
2023
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43. EnzymeML-a data exchange format for biocatalysis and enzymology.

Author

Range J, Halupczok C, Lohmann J, Swainston N, Kettner C, Bergmann FT, Weidemann A, Wittig U, Schnell S, and Pleiss J

Subjects
Biocatalysis, Databases, Factual, Software
Abstract

EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK., (© 2021 The Authors. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Published
2022
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44. The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering.

Author

Hérisson J, Duigou T, du Lac M, Bazi-Kabbaj K, Sabeti Azad M, Buldum G, Telle O, El Moubayed Y, Carbonell P, Swainston N, Zulkower V, Kushwaha M, Baldwin GS, and Faulon JL

Subjects
Biotechnology, Metabolic Engineering, Software, Escherichia coli genetics, Synthetic Biology
Abstract

Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows., (© 2022. The Author(s).)

Published
2022
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45. SpeedyGenesXL: an Automated, High-Throughput Platform for the Preparation of Bespoke Ultralarge Variant Libraries for Directed Evolution.

Author

Sadler JC, Swainston N, Dunstan MS, Currin A, and Kell DB

Subjects
Gene Library, Directed Molecular Evolution methods, Proteins
Abstract

Directed evolution of proteins is a highly effective strategy for tailoring biocatalysts to a particular application, and is capable of engineering improvements such as k cat, thermostability and organic solvent tolerance. It is recognized that large and systematic libraries are required to navigate a protein's vast and rugged sequence landscape effectively, yet their preparation is nontrivial and commercial libraries are extremely costly. To address this, we have developed SpeedyGenesXL, an automated, high-throughput platform for the production of wild-type genes, Boolean OR, combinatorial, or combinatorial-OR-type libraries based on the SpeedyGenes methodology. Together this offers a flexible platform for library synthesis, capable of generating many different bespoke, diverse libraries simultaneously., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

Published
2022
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46. MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.

Author

Shrivastava AD, Swainston N, Samanta S, Roberts I, Wright Muelas M, and Kell DB

Subjects
Algorithms, Deep Learning, Mass Spectrometry, Molecular Structure, Metabolomics methods, Small Molecule Libraries analysis
Abstract

The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

Published
2021
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47. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.

Author

Kell DB, Samanta S, and Swainston N

Subjects
Cheminformatics, Computer Simulation, Deep Learning
Abstract

The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future., (© 2020 The Author(s).)

Published
2020
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48. DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.

Author

Khemchandani Y, O'Hagan S, Samanta S, Swainston N, Roberts TJ, Bollegala D, and Kell DB

Abstract

We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

Published
2020
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49. Engineering Escherichia coli towards de novo production of gatekeeper (2 S )-flavanones: naringenin, pinocembrin, eriodictyol and homoeriodictyol.

Author

Dunstan MS, Robinson CJ, Jervis AJ, Yan C, Carbonell P, Hollywood KA, Currin A, Swainston N, Feuvre RL, Micklefield J, Faulon JL, Breitling R, Turner N, Takano E, and Scrutton NS

Abstract

Natural plant-based flavonoids have drawn significant attention as dietary supplements due to their potential health benefits, including anti-cancer, anti-oxidant and anti-asthmatic activities. Naringenin, pinocembrin, eriodictyol and homoeriodictyol are classified as (2 S )-flavanones, an important sub-group of naturally occurring flavonoids, with wide-reaching applications in human health and nutrition. These four compounds occupy a central position as branch point intermediates towards a broad spectrum of naturally occurring flavonoids. Here, we report the development of Escherichia coli production chassis for each of these key gatekeeper flavonoids. Selection of key enzymes, genetic construct design and the optimization of process conditions resulted in the highest reported titers for naringenin (484 mg/l), improved production of pinocembrin (198 mg/l) and eriodictyol (55 mg/l from caffeic acid), and provided the first example of in vivo production of homoeriodictyol directly from glycerol (17 mg/l). This work provides a springboard for future production of diverse downstream natural and non-natural flavonoid targets., (© The Author(s) 2020. Published by Oxford University Press.)

Published
2020
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50. SBML Level 3: an extensible format for the exchange and reuse of biological models.

Author

Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik-Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier-Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, and Hucka M

Subjects
Animals, Humans, Logistic Models, Models, Biological, Software, Systems Biology methods
Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., (© 2020 California Institute of Technology Published under the terms of the CC BY 4.0 license.)

Published
2020
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