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4. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0

Author

Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N., Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S., Wachowiak, Jacek, Keating, Sarah M., Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu H. J., Aurich, Maike K., Clancy, Catherine M., Modamio, Jennifer, Sauls, John T., Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, Assal, Diana C. El, Valcarcel, Luis V., Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Guebila, Marouen Ben, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M., Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T., Oliveira, Miguel A. P., Vuong, Phan T., Assal, Lemmer P. El, Kuperstein, Inna, Zinovyev, Andrei, Hinton, H. Scott, Bryant, William A., Artacho, Francisco J. Aragón, Planes, Francisco J., Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A., Maranas, Costas D., Lewis, Nathan E., Sauter, Thomas, Palsson, Bernhard Ø., Thiele, Ines, and Fleming, Ronan M. T.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods.

Published
2017

5. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Author

Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N, Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S, Wachowiak, Jacek, Keating, Sarah M, Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu HJ, Aurich, Maike K, Clancy, Catherine M, Modamio, Jennifer, Sauls, John T, Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, El Assal, Diana C, Valcarcel, Luis V, Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Ben Guebila, Marouen, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M, Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T, Oliveira, Miguel AP, Vuong, Phan T, El Assal, Lemmer P, Kuperstein, Inna, Zinovyev, Andrei, Hinton, H Scott, Bryant, William A, Aragón Artacho, Francisco J, Planes, Francisco J, Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A, Maranas, Costas D, Lewis, Nathan E, Sauter, Thomas, Palsson, Bernhard Ø, Thiele, Ines, and Fleming, Ronan MT

Subjects
Information and Computing Sciences, Biochemistry and Cell Biology, Biological Sciences, Biotechnology, Genome, Metabolic Networks and Pathways, Models, Biological, Software, Systems Biology, q-bio.QM, Chemical Sciences, Medical and Health Sciences, Bioinformatics
Abstract

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Published
2019

10. A Fermentation State Marker Rule Design Task in Metabolic Engineering.

Author

Stalidzans, Egils, Muiznieks, Reinis, Dubencovs, Konstantins, Sile, Elina, Berzins, Kristaps, Suleiko, Arturs, and Vanags, Juris

Subjects
*MODELS & modelmaking, *METABOLIC models, *DELETION mutation, *ENGINEERING, *CELL metabolism, *ETHANOL, *FERMENTATION
Abstract

There are several ways in which mathematical modeling is used in fermentation control, but mechanistic mathematical genome-scale models of metabolism within the cell have not been applied or implemented so far. As part of the metabolic engineering task setting, we propose that metabolite fluxes and/or biomass growth rate be used to search for a fermentation steady state marker rule. During fermentation, the bioreactor control system can automatically detect the desired steady state using a logical marker rule. The marker rule identification can be also integrated with the production growth coupling approach, as presented in this study. A design of strain with marker rule is demonstrated on genome scale metabolic model iML1515 of Escherichia coli MG1655 proposing two gene deletions enabling a measurable marker rule for succinate production using glucose as a substrate. The marker rule example at glucose consumption 10.0 is: IF (specific growth rate μ is above 0.060 h−1, AND CO2 production under 1.0, AND ethanol production above 5.5), THEN succinate production is within the range 8.2–10, where all metabolic fluxes units are mmol ∗ gDW−1 ∗ h−1. An objective function for application in metabolic engineering, including productivity features and rule detecting sensor set characterizing parameters, is proposed. Two-phase approach to implementing marker rules in the cultivation control system is presented to avoid the need for a modeler during production. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Kinetic and Stoichiometric Modeling-Based Analysis of Docosahexaenoic Acid (DHA) Production Potential by Crypthecodinium cohnii from Glycerol, Glucose and Ethanol

Author

Berzins, Kristaps, primary, Muiznieks, Reinis, additional, Baumanis, Matiss R., additional, Strazdina, Inese, additional, Shvirksts, Karlis, additional, Prikule, Santa, additional, Galvanauskas, Vytautas, additional, Pleissner, Daniel, additional, Pentjuss, Agris, additional, Grube, Mara, additional, Kalnenieks, Uldis, additional, and Stalidzans, Egils, additional

Published
2022
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24. An early stage researcher's primer on systems medicine terminology

Author

Zanin, Massimiliano, Aitya, Nadim A.A., Basilio, José, Baumbach, Jan, Benis, Arriel, Behera, Chandan K., Bucholc, Magda, Castiglione, Filippo, Chouvarda, Ioanna, Comte, Blandine, Dao, Tien-Tuan, Ding, Xuemei, Pujos-Guillot, Estelle, Filipovic, Nenad, Finn, David P, Glass, David H, Harel, Nissim, Iesmantas, Tomas, Ivanoska, Ilinka, Joshi, Alok, Boudjeltia, Karim Zouaoui, Kaoui, Badr, Kaur, Daman, Maguire, Liam P., McClean, Paula L, McCombe, Niamh, de Miranda, João Luís, Moisescu, Mihnea Alexandru, Pappalardo, Francesco, Polster, Annikka, Prasad, Girijesh, Rozman, Damjana, Sacala, Ioan, Sanchez-Bornot, Jose M., Schmid, Johannes A., Sharp, Trevor, Solé-Casals, Jordi, Spiwok, Vojtěch, Spyrou, George M, Stalidzans, Egils, Stres, Blaž, Sustersic, Tijana, Symeonidis, Ioannis, Tieri, Paolo, Todd, Stephen, Van Steen, Kristel, Veneva, Milena, Wang, Da-Hui, Wang, Haiying, Wang, Hui, Watterson, Steven, Wong-Lin, KongFatt, Yang, Su, Zou, Xin, Schmidt, Harald H.H.W., and Mary Ann Liebert

Subjects
multiscale data science, systems medicine, multiscale modeling
Abstract

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Published
2021

26. An Early Stage Researcher's Primer on Systems Medicine Terminology

Author

Zanin, Massimiliano, primary, Aitya, Nadim A.A., additional, Basilio, José, additional, Baumbach, Jan, additional, Benis, Arriel, additional, Behera, Chandan K., additional, Bucholc, Magda, additional, Castiglione, Filippo, additional, Chouvarda, Ioanna, additional, Comte, Blandine, additional, Dao, Tien-Tuan, additional, Ding, Xuemei, additional, Pujos-Guillot, Estelle, additional, Filipovic, Nenad, additional, Finn, David P., additional, Glass, David H., additional, Harel, Nissim, additional, Iesmantas, Tomas, additional, Ivanoska, Ilinka, additional, Joshi, Alok, additional, Boudjeltia, Karim Zouaoui, additional, Kaoui, Badr, additional, Kaur, Daman, additional, Maguire, Liam P., additional, McClean, Paula L., additional, McCombe, Niamh, additional, de Miranda, João Luís, additional, Moisescu, Mihnea Alexandru, additional, Pappalardo, Francesco, additional, Polster, Annikka, additional, Prasad, Girijesh, additional, Rozman, Damjana, additional, Sacala, Ioan, additional, Sanchez-Bornot, Jose M., additional, Schmid, Johannes A., additional, Sharp, Trevor, additional, Solé-Casals, Jordi, additional, Spiwok, Vojtěch, additional, Spyrou, George M., additional, Stalidzans, Egils, additional, Stres, Blaž, additional, Sustersic, Tijana, additional, Symeonidis, Ioannis, additional, Tieri, Paolo, additional, Todd, Stephen, additional, Van Steen, Kristel, additional, Veneva, Milena, additional, Wang, Da-Hui, additional, Wang, Haiying, additional, Wang, Hui, additional, Watterson, Steven, additional, Wong-Lin, KongFatt, additional, Yang, Su, additional, Zou, Xin, additional, and Schmidt, Harald H.H.W., additional

Published
2021
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27. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Author

Stalidzans, Egils and Stalidzans, Egils

Abstract

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body

Published
2020

28. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Author

Stalidzans, Egils, primary, Zanin, Massimiliano, additional, Tieri, Paolo, additional, Castiglione, Filippo, additional, Polster, Annikka, additional, Scheiner, Stefan, additional, Pahle, Jürgen, additional, Stres, Blaž, additional, List, Markus, additional, Baumbach, Jan, additional, Lautizi, Manuela, additional, Van Steen, Kristel, additional, and Schmidt, Harald H.H.W., additional

Published
2020
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30. Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine

Author

Zanin, Massimiliano, primary, Chorbev, Ivan, additional, Stres, Blaz, additional, Stalidzans, Egils, additional, Vera, Julio, additional, Tieri, Paolo, additional, Castiglione, Filippo, additional, Groen, Derek, additional, Zheng, Huiru, additional, Baumbach, Jan, additional, Schmid, Johannes A, additional, Basilio, José, additional, Klimek, Peter, additional, Debeljak, Nataša, additional, Rozman, Damjana, additional, and Schmidt, Harald H H W, additional

Published
2017
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33. Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine.

Author

Zanin, Massimiliano, Chorbev, Ivan, Stres, Blaz, Stalidzans, Egils, Vera, Julio, Tieri, Paolo, Castiglione, Filippo, Groen, Derek, Zheng, Huiru, Baumbach, Jan, Schmid, Johannes A, Basilio, José, Klimek, Peter, Debeljak, Nataša, Rozman, Damjana, and Schmidt, Harald H H W

Subjects
MULTISCALE modeling, COMPUTER systems, DATA science, SCIENTIFIC community, MEDICAL practice, EUROPEAN cooperation
Abstract

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine. [ABSTRACT FROM AUTHOR]

Published
2019
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34. Strategies for structuring interdisciplinary education in Systems Biology: an European perspective

Author

Cvijovic, Marija, primary, Höfer, Thomas, additional, Aćimović, Jure, additional, Alberghina, Lilia, additional, Almaas, Eivind, additional, Besozzi, Daniela, additional, Blomberg, Anders, additional, Bretschneider, Till, additional, Cascante, Marta, additional, Collin, Olivier, additional, de Atauri, Pedro, additional, Depner, Cornelia, additional, Dickinson, Robert, additional, Dobrzynski, Maciej, additional, Fleck, Christian, additional, Garcia-Ojalvo, Jordi, additional, Gonze, Didier, additional, Hahn, Jens, additional, Hess, Heide Marie, additional, Hollmann, Susanne, additional, Krantz, Marcus, additional, Kummer, Ursula, additional, Lundh, Torbjörn, additional, Martial, Gifta, additional, dos Santos, Vítor Martins, additional, Mauer-Oberthür, Angela, additional, Regierer, Babette, additional, Skene, Barbara, additional, Stalidzans, Egils, additional, Stelling, Jörg, additional, Teusink, Bas, additional, Workman, Christopher T, additional, and Hohmann, Stefan, additional

Published
2016
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35. APPLICATION OF MATHEMATICAL MODELING AS A TOOL IN THE USE OF RYE MALT TO BALANCE THE QUALITY OF RYE FLOUR.

Author

Murniece, Ruta, Stalidzans, Egils, Dabina-Bicka, Ilona, Galoburda, Ruta, Cinkmanis, Ingmars, and Klava, Dace

Subjects
RYE flour, FLOUR quality, MALT
Abstract

Rye grain and flour quality are highly variable within one variety, depending on ripening and harvesting conditions. Starch quality and endogenous enzyme activity are essential indicators of rye flour baking properties. Optimum quality indicators for rye flour are Falling number (FN) 160-200 s, maximum viscosity 400-600 BU. To ensure the quality raw materials for bread production, rye unfermented malt is used as an enzymatic additive. Malt activity is characterised by Diastatic Power (DP), which may vary around DP 120-450. The aim of the work was to predict the amount of rye malt required to balance the quality of rye flour by modelling baking properties. Rye flour with different enzymatic activities (FN 210, 262, 346 s) was used to develop the mathematical model. Five rye malt samples with DP 170, 179, 261, 362, and 408 were used, and malt was added in the amounts of 0.5, 1.0, and 1.5%. In total 45 samples were analyzed for the rheological and textural properties, the Falling Number, and α.-, β-amylase activity. The enzymatic activity of a-amylase for different malt samples ranged from 45 to 174 U g-1, whereas the b-amylase activity was 8.62-17.1 U g-1. Adding less active malt (DP 120) at 1.5% concentration can achieve similar effect to the case when adding more active malt (DP 362, DP 408) in a lower concentration - 0.5%. The developed model will allow accurate determination of the malt amount addition required, thus reducing raw material consumption and costs. [ABSTRACT FROM AUTHOR]

Published
2023

36. Misinterpretation risks of global stochastic optimisation of kinetic models revealed by multiple optimisation runs.

Author

Stalidzans, Egils, Landmane, Katrina, Sulins, Jurijs, and Sahle, Sven

Subjects
*STOCHASTIC processes, *GENETIC algorithms, *HOMEOSTASIS, *MICROORGANISMS, *ENZYMES, *PARTICLE swarm optimization
Abstract

Highlights • Global stochastic optimisation methods are tested on two kinetic models. • Total enzyme activity constraints and homeostatic constraints are implemented. • Methods are examined by consensus and stagnation of parallel optimisation runs. • Application of single run of stochastic methods may be heavily misleading. • Homeostatic constraints heavily reduce the solution space of steady state solutions. Abstract One of use cases for metabolic network optimisation of biotechnologically applied microorganisms is the in silico design of new strains with an improved distribution of metabolic fluxes. Global stochastic optimisation methods (genetic algorithms, evolutionary programing, particle swarm and others) can optimise complicated nonlinear kinetic models and are friendly for unexperienced user: they can return optimisation results with default method settings (population size, number of generations and others) and without adaptation of the model. Drawbacks of these methods (stochastic behaviour, undefined duration of optimisation, possible stagnation and no guaranty of reaching optima) cause optimisation result misinterpretation risks considering the very diverse educational background of the systems biology and synthetic biology research community. Different methods implemented in the COPASI software package are tested in this study to determine their ability to find feasible solutions and assess the convergence speed to the best value of the objective function. Special attention is paid to the potential misinterpretation of results. Optimisation methods are tested with additional constraints that can be introduced to ensure the biological feasibility of the resulting optimised design: (1) total enzyme activity constraint (called also amino acid pool constraint) to limit the sum of enzyme concentrations and (2) homeostatic constraint limiting steady state metabolite concentration corridor around the steady state concentrations of metabolites in the original model. Impact of additional constraints on the performance of optimisation methods and misinterpretation risks is analysed. [ABSTRACT FROM AUTHOR]

Published
2019
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44. Impact of Thermodynamic Constraint to the Solution Space of Metabolic Pathway Design Using sAnalyzer Tool.

Author

MEITALOVS, Jurijs and STALIDZANS, Egils

Subjects
METABOLITES, GIBBS' free energy, BIOMASS production
Abstract

One of most important stages of metabolic engineering is organism design. One of methods that allows process design is modeling or in silico approach. Application of biological models instead of biological experiments is cheaper and does not require specific resources. sAnalyzer tool uses reactions data to construct new metabolic pathways and predict its feasibility to transform one metabolite to another one using particular chassis organism represented by a model. sAnalyzer searches possible reactions connections in full solution space of all accessible reactions. One of reaction parameters used to predict its flow direction is reaction thermodynamic data - Gibbs free energy (ΔrG) value, which shows how much energy is needed for reaction to flow in specific direction. sAnalyzer tool demonstrates how thermodynamic thresholds of ΔrG value impacts possible solution space of metabolites transformation pathways on the example of using glycerol as substrate for bacteria Z.mobilis model. The solutions are ranked by flux over the pathway of interest, biomass production ability and integrated parameters derived from involved metabolites and reactions. [ABSTRACT FROM AUTHOR]

Published
2015

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