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Donghyuk Kim, Hyunjung Kim Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, 16499, KoreaCorrespondence: Hyunjung Kim, Email hyunkim@ajou.ac.krIntroduction: Due to its distinct advantage of non-invasive application in treatment, photothermal therapy (PTT) is being studied by many researchers to reduce the need for surgical incisions. It is characterized by the injection of nanoparticles into biological tissue as photothermal agents (PTAs) which diffuse within the tissue. In this study, the diffusion behavior of various doses of gold nanoparticles (AuNPs) injected into tumor tissues is analyzed and the effectiveness of PTT at each elapsed time after injection is confirmed by numerical analysis.Methods: The diffusion behavior of AuNPs within biological tissues is assessed using the convection-diffusion equation, while the temperature distribution is determined using the Pennes bioheat transfer equation. In addition, the effect of the diffusion behavior of AuNPs on the effectiveness of PTT is quantitatively confirmed by analyzing the temperature distribution in the medium through the apoptotic variable. Numerical simulation parameters are selected with doses ranging from 100 to 400 μg/mL, elapsed time after injection from 1 min to 24 h, and laser power ranging from 0 to 1 W.Results: After evaluating PTT’s efficacy in every situation, it was discovered that a dosage of 100– 300 μg/mL produced the best therapeutic result, with the highest impact occurring 12 hours after injection. In contrast, when the dosage was 400 μg/mL, the highest therapeutic effect was achieved after 18 hours post-injection. Additionally, it was discovered that the ideal laser power at each injection dose was 0.22, 0.14, 0.12, and 0.12 W, respectively.Conclusion: The conditions required to achieve the optimal treatment effect at each dosage, presented here, are expected to accelerate the commercialization of PTT.Keywords: apoptosis, elapsed time after injection, g

Text generation is a compelling sub-field of natural language processing, aiming to generate human-readable text from input words. In particular, the decoder-only generative models, such as generative pre-trained transformer (GPT), are widely used for text generation, with two major computational stages: summarization and generation. Unlike the summarization stage, which can process the input tokens in parallel, the generation stage is difficult to accelerate due to its sequential generation of output tokens through iteration. Moreover, each iteration requires reading a whole model with little data reuse opportunity. Therefore, the workload of transformer-based text generation is severely memory-bound, making the external memory bandwidth system bottleneck. In this paper, we proposed a subarray-level processing-in-memory architecture named SAL-PIM, HBM-based PIM architecture for the end-to-end acceleration of transformer-based text generation. The SAL-PIM architecture includes three architectural features. First, the SAL-PIM architecture utilizes higher internal bandwidth by integrating multiple subarray-level arithmetic logic units with optimized data mapping schemes. Second, the SAL-PIM architecture adopts LUT-based linear interpolation to perform complex non-linear functions in PIM. Third, the SAL-PIM architecture accelerates end-to-end inference on PIM in text generation. Furthermore, to validate the SAL-PIM architecture, we built cycle-accurate simulator and implemented the SAL-PIM's logic units in 28-nm CMOS technology. As a result, when the input size is from 32 to 128 and the output size is from 1 to 256, SAL-PIM achieves a maximum of 4.72 times speedup and an average of 1.83 times speedup for the text generation based on the GPT-2 medium model compared to the server-level GPU., Comment: 14 pages, 15 figures

The massive sequencing of transposon insertion mutant libraries (Tn-Seq) represents a commonly used method to determine essential genes in bacteria. Using a hypersaturated transposon mutant library consisting of 400,096 unique Tn insertions, 523 genes were classified as essential in Escherichia coli K-12 MG1655. This provided a useful genome-wide gene essentiality landscape for rapidly identifying 233 of 301 essential genes previously validated by a knockout study. However, there was a discrepancy in essential gene sets determined by conventional gene deletion methods and Tn-Seq, although different Tn-Seq studies reported different extents of discrepancy. We have elucidated two causes of this discrepancy. First, 68 essential genes not detected by Tn-Seq contain nonessential subgenic domains that are tolerant to transposon insertion, which leads to the false assignment of an essential gene as a nonessential or dispensable gene. These genes exhibited a high level of transposon insertion in their subgenic nonessential domains. In contrast, 290 genes were additionally categorized as essential by Tn-Seq, although their knockout mutants were available. The comparative analysis of Tn-Seq and high-resolution footprinting of nucleoid-associated proteins (NAPs) revealed that a protein-DNA interaction hinders transposon insertion. We identified 213 false-positive genes caused by NAP-genome interactions. These two limitations have to be considered when addressing essential bacterial genes using Tn-Seq. Furthermore, a comparative analysis of high-resolution Tn-Seq with other data sets is required for a more accurate determination of essential genes in bacteria. IMPORTANCE Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. In this study, we generated a hypersaturated Escherichia col

While global transcription factors (TFs) have been studied extensively in Escherichia coli model strains, conservation and diversity in TF regulation between strains is still unknown. Here we use a combination of ChIP-exo-to define ferric uptake regulator (Fur) binding sites-and differential gene expression-to define the Fur regulon in nine E. coli strains. We then define a pan-regulon consisting of 469 target genes that includes all Fur target genes in all nine strains. The pan-regulon is then divided into the core regulon (target genes found in all the strains, n = 36), the accessory regulon (target found in two to eight strains, n = 158) and the unique regulon (target genes found in one strain, n = 275). Thus, there is a small set of Fur regulated genes common to all nine strains, but a large number of regulatory targets unique to a particular strain. Many of the unique regulatory targets are genes unique to that strain. This first-established pan-regulon reveals a common core of conserved regulatory targets and significant diversity in transcriptional regulation amongst E. coli strains, reflecting diverse niche specification and strain history.

Processing-in-memory (PIM) architecture is an inherent match for data analytics application, but we observe major challenges to address when accelerating it using PIM. In this paper, we propose Darwin, a practical LRDIMM-based multi-level PIM architecture for data analytics, which fully exploits the internal bandwidth of DRAM using the bank-, bank group-, chip-, and rank-level parallelisms. Considering the properties of data analytics operators and DRAM's area constraints, Darwin maximizes the internal data bandwidth by placing the PIM processing units, buffers, and control circuits across the hierarchy of DRAM. More specifically, it introduces the bank processing unit for each bank in which a single instruction multiple data (SIMD) unit handles regular data analytics operators and bank group processing unit for each bank group to handle workload imbalance in the condition-oriented data analytics operators. Furthermore, Darwin supports a novel PIM instruction architecture that concatenates instructions for multiple thread executions on bank group processing entities, addressing the command bottleneck by enabling separate control of up to 512 different in-memory processing units simultaneously. We build a cycle-accurate simulation framework to evaluate Darwin with various DRAM configurations, optimization schemes and workloads. Darwin achieves up to 14.7x speedup over the non-optimized version. Finally, the proposed Darwin architecture achieves 4.0x-43.9x higher throughput and reduces energy consumption by 85.7% than the baseline CPU system (Intel Xeon Gold 6226 + 4 channels of DDR4-2933). Compared to the state-of-the-art PIM, Darwin achieves up to 7.5x and 7.1x in the basic query operators and TPC-H queries, respectively. Darwin is based on the latest GDDR6 and requires only 5.6% area overhead, suggesting a promising PIM solution for the future main memory system., Comment: 14 pages, 16 figures

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

Two major transcriptional regulators of carbon metabolism in bacteria are Cra and CRP. CRP is considered to be the main mediator of catabolite repression. Unlike for CRP, in vivo DNA binding information of Cra is scarce. Here we generate and integrate ChIP-exo and RNA-seq data to identify 39 binding sites for Cra and 97 regulon genes that are regulated by Cra in Escherichia coli. An integrated metabolic-regulatory network was formed by including experimentally-derived regulatory information and a genome-scale metabolic network reconstruction. Applying analysis methods of systems biology to this integrated network showed that Cra enables optimal bacterial growth on poor carbon sources by redirecting and repressing glycolysis flux, by activating the glyoxylate shunt pathway, and by activating the respiratory pathway. In these regulatory mechanisms, the overriding regulatory activity of Cra over CRP is fundamental. Thus, elucidation of interacting transcriptional regulation of core carbon metabolism in bacteria by two key transcription factors was possible by combining genome-wide experimental measurement and simulation with a genome-scale metabolic model.

Cancer cell mechanotype changes are newly recognized cancer phenotypic events, whereas metastatic cancer cells show decreased cell stiffness and increased deformability relative to normal cells. To further examine how cell mechanotype changes in early stages of cancer transformation and progression, an in vitro multi-step human urothelial cell carcinogenic model was used to measure cellular Young's modulus, deformability, and transit time using single-cell atomic force microscopy, microfluidic-based deformability cytometry, and quantitative deformability cytometry, respectively. Measurable cell mechanotype changes of stiffness, deformability, and cell transit time occur early in the transformation process. As cells progress from normal, to preinvasive, to invasive cells, Young's modulus of stiffness decreases and deformability increases gradually. These changes were confirmed in three-dimensional cultured microtumor masses and urine exfoliated cells directly from patients. Using gene screening and proteomics approaches, we found that the main molecular pathway implicated in cell mechanotype changes appears to be epithelial to mesenchymal transition.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

Yeon Ju Kim,1,* Haribalan Perumalsamy,1,* Verónica Castro-Aceituno,1 Donghyuk Kim,2 Josua Markus,1 Seungah Lee,3 Sung Kim,4 Ying Liu,1 Deok Chun Yang1 1Department of Oriental Medicinal Biotechnology and Graduate School of Biotechnology, College of Life Science, Kyung Hee University, Yongin-si, Gyeonggi-do, Republic of Korea; 2School of Energy and Chemical Engineering/School of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea; 3Department of Applied Chemistry and Institute of Natural Sciences, College of Applied Science, Kyung Hee University, Seoul, Republic of Korea; 4Center for Global Converging Humanities, Kyung Hee University, Seoul, Republic of Korea*These authors contributed equally to this workCorrespondence: Yeon Ju KimCollege of Life Sciences, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of KoreaTel +82 31 201 2645Fax +82 31 204 8116Email yeonjukim@khu.ac.krBackground: Zinc oxide nanoparticles (ZnO NPs) are used in modern cancer therapy based on their specific target, efficacy, low toxicity and biocompatibility. The photocatalytic performance of Zinc oxide (ZnO) nanocomposites with hyaluronic acid (HA) was used to study anticancer properties against various human cancer cell lines.Methods: Zinc oxide (ZnO) nanocomposites functionalized by hyaluronic acid (HA) were prepared by a co-precipitation method (HA-ZnONcs). The submicron-flower-shaped nanocomposites were further functionalized with ginsenoside Rh2 by a cleavable ester bond via carbodiimide chemistry to form Rh2HAZnO. The physicochemical behaviors of the synthesized ZnO nanocomposites were characterized by various analytical and spectroscopic techniques. We carried out 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide (MTT) assay to evaluate the toxicity of Rh2HAZnO in various human cancer cells (A549, MCF-7, and HT29). Furthermore, to confirm the apoptotic effects of Rh2HAZnO

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

A redundant manipulator has multiple inverse kinematics solutions per end-effector pose. Accordingly, there can be many trajectories for joints that follow a given endeffector path in the Cartesian space. In this paper, we present a trajectory optimization of a redundant manipulator (TORM) to synthesize a trajectory that follows a given end-effector path accurately, while achieving smoothness and collisionfree manipulation. Our method holistically incorporates three desired properties into the trajectory optimization process by integrating the Jacobian-based inverse kinematics solving method and an optimization-based motion planning approach. Specifically, we optimize a trajectory using two-stage gradient descent to reduce potential competition between different properties during the update. To avoid falling into local minima, we iteratively explore different candidate trajectories with our local update. We compare our method with state-of-the-art methods in test scenes including external obstacles and two non-obstacle problems. Our method robustly minimizes the pose error in a progressive manner while satisfying various desirable properties., Comment: 8 pages

Transcriptional regulation enables cells to respond to environmental changes. Of the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified, but ChIP methods have been used to fully characterize only a few dozen. Identifying these remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to (i) identify 16 candidate TFs from over a hundred uncharacterized genes; (ii) capture a total of 255 DNA binding peaks for ten candidate TFs resulting in six high-confidence binding motifs; (iii) reconstruct the regulons of these ten TFs by determining gene expression changes upon deletion of each TF and (iv) identify the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of l-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron-limited conditions, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.

Understanding the complex interactions of protein posttranslational modifications (PTMs) represents a major challenge in metabolic engineering, synthetic biology, and the biomedical sciences. Here, we present a workflow that integrates multiplex automated genome editing (MAGE), genome-scale metabolic modeling, and atomistic molecular dynamics to study the effects of PTMs on metabolic enzymes and microbial fitness. This workflow incorporates complementary approaches across scientific disciplines; provides molecular insight into how PTMs influence cellular fitness during nutrient shifts; and demonstrates how mechanistic details of PTMs can be explored at different biological scales. As a proof of concept, we present a global analysis of PTMs on enzymes in the metabolic network of Escherichia coli Based on our workflow results, we conduct a more detailed, mechanistic analysis of the PTMs in three proteins: enolase, serine hydroxymethyltransferase, and transaldolase. Application of this workflow identified the roles of specific PTMs in observed experimental phenomena and demonstrated how individual PTMs regulate enzymes, pathways, and, ultimately, cell phenotypes.

Mobile manipulation planning commonly adopts a decoupled approach that performs planning separately on the base and the manipulator. While this approach is fast, it can generate sub-optimal paths. Another direction is a coupled approach jointly adjusting the base and manipulator in a high-dimensional configuration space. This coupled approach addresses sub-optimality and incompleteness of the decoupled approach, but has not been widely used due to its excessive computational overhead. Given this trade-off space, we present a simple, yet effective mobile manipulation sampling method, harmonious sampling, to perform the coupled approach mainly in difficult regions, where we need to simultaneously maneuver the base and the manipulator. Our method identifies such difficult regions through a low-dimensional base space by utilizing a reachability map given the target end-effector pose and narrow passage detected by generalized Voronoi diagram. For the rest of simple regions, we perform sampling mainly on the base configurations with a predefined joint configuration, accelerating the planning process. We compare our method with the decoupled and coupled approaches in six different problems with varying difficulty. Our method shows meaningful improvements experimentally in terms of time to find an initial solution (up to 5.6 times faster) and final solution cost (up to 17% lower) over the decoupled approach, especially in difficult scenes with narrow space. We also demonstrate these benefits with a real, mobile Hubo robot.

In bacterial transcription, transcription initiation is arguably the most important regulatory point, because transcribing unnecessary genes into RNA could be a waste of energy, time and resources. There are multiple components which are involved in bacterial transcription initiation: RNA polymerase, [sigma]-factors, transcription factors, and transcription start sites. Each component has been intensively investigated, however in a limited scope and mostly with low-throughput methods. New technologies, such as hybridization on microarray and deep-sequencing, enabled researchers to study each component in a systems level, in a combination of two or more components, and in comparison between different species. In order to facilitate the analysis, integration, and comparison, software, MetaScope, was developed to accommodate multiple genome-scale datasets to visualize, analyze, integrate, and compare. TSS-seq, modified 5'-RACE with deep- sequencing, gave a genome-scale landscape of transcription start sites, and comparison of TSSs of conserved genes between closely-related species, E. coli and K. pneumoniae, showed significantly different usage of promoters, which implies different regulation of orthologous genes. To further investigate properties of promoters which were identified by TSS-seq, ChIP-chip experiments were performed for [sigma]-factors in E. coli to determine [sigma]-factor regulons. From the reconstructed [sigma]- factor network, extensive overlaps between regulons were observed. [sigma]⁷⁰ and [sigma]³⁸ share the largest set of genes in E. coli, and additional experiments revealed that those [sigma]-factors work in competition and utilize the negative regulation by [sigma]³⁸ . ChIP-exo, which applies exonuclease to present better resolution of DNA-binding, and RNA-seq implemented more detailed identification of Fur regulon in E. coli. Reconstruction of Fur regulon completed the previous knowledge of bacterial response to iron change, and also enabled its

A transcription factor (TF), OmpR, plays a critical role in transcriptional regulation of the osmotic stress response in bacteria. Here, we reveal a genome-scale OmpR regulon in Escherichia coli K-12 MG1655. Integrative data analysis reveals that a total of 37 genes in 24 transcription units (TUs) belong to OmpR regulon. Among them, 26 genes show more than two-fold changes in expression level in an OmpR knock-out strain. Specifically, we find that: 1) OmpR regulates mostly membrane-located gene products involved in diverse fundamental biological processes, such as narU (encoding nitrate/nitrite transporter), ompX (encoding outer membrane protein X), and nuoN (encoding NADH:ubiquinone oxidoreductase); 2) by investigating co-regulation of entire sets of genes regulated by other stress-response TFs, stresses are surprisingly independently regulated among each other; and, 3) a detailed investigation of the physiological roles of the newly discovered OmpR regulon genes reveals that activation of narU represents a novel strategy to significantly improve osmotic stress tolerance of E. coli. Thus, the genome-scale approach to elucidating regulons comprehensively identifies regulated genes and leads to fundamental discoveries related to stress responses.

In this report, we present multiparameter deformability cytometry (m-DC), in which we explore a large set of parameters describing the physical phenotypes of pluripotent cells and their derivatives. m-DC utilizes microfluidic inertial focusing and hydrodynamic stretching of single cells in conjunction with high-speed video recording to realize high-throughput characterization of over 20 different cell motion and morphology-derived parameters. Parameters extracted from videos include size, deformability, deformation kinetics, and morphology. We train support vector machines that provide evidence that these additional physical measurements improve classification of induced pluripotent stem cells, mesenchymal stem cells, neural stem cells, and their derivatives compared to size and deformability alone. In addition, we utilize visual interactive stochastic neighbor embedding to visually map the high-dimensional physical phenotypic spaces occupied by these stem cells and their progeny and the pathways traversed during differentiation. This report demonstrates the potential of m-DC for improving understanding of physical differences that arise as cells differentiate and identifying cell subpopulations in a label-free manner. Ultimately, such approaches could broaden our understanding of subtle changes in cell phenotypes and their roles in human biology.

Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN-probably the best characterized TRN-several questions remain. Here, we address three questions: (i) How complete is our knowledge of the E. coli TRN; (ii) how well can we predict gene expression using this TRN; and (iii) how robust is our understanding of the TRN? First, we reconstructed a high-confidence TRN (hiTRN) consisting of 147 transcription factors (TFs) regulating 1,538 transcription units (TUs) encoding 1,764 genes. The 3,797 high-confidence regulatory interactions were collected from published, validated chromatin immunoprecipitation (ChIP) data and RegulonDB. For 21 different TF knockouts, up to 63% of the differentially expressed genes in the hiTRN were traced to the knocked-out TF through regulatory cascades. Second, we trained supervised machine learning algorithms to predict the expression of 1,364 TUs given TF activities using 441 samples. The algorithms accurately predicted condition-specific expression for 86% (1,174 of 1,364) of the TUs, while 193 TUs (14%) were predicted better than random TRNs. Third, we identified 10 regulatory modules whose definitions were robust against changes to the TRN or expression compendium. Using surrogate variable analysis, we also identified three unmodeled factors that systematically influenced gene expression. Our computational workflow comprehensively characterizes the predictive capabilities and systems-level functions of an organism's TRN from disparate data types.

Rapid growth in size and complexity of biological data sets has led to the 'Big Data to Knowledge' challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

The regulators GadE, GadW and GadX (which we refer to as GadEWX) play a critical role in the transcriptional regulation of the glutamate-dependent acid resistance (GDAR) system in Escherichia coli K-12 MG1655. However, the genome-wide regulatory role of GadEWX is still unknown. Here we comprehensively reconstruct the genome-wide GadEWX transcriptional regulatory network and RpoS involvement in E. coli K-12 MG1655 under acidic stress. Integrative data analysis reveals that GadEWX regulons consist of 45 genes in 31 transcription units and 28 of these genes were associated with RpoS-binding sites. We demonstrate that GadEWX directly and coherently regulate several proton-generating/consuming enzymes with pairs of negative-feedback loops for pH homeostasis. In addition, GadEWX regulate genes with assorted functions, including molecular chaperones, acid resistance, stress response and other regulatory activities. These results show how GadEWX simultaneously coordinate many cellular processes to produce the overall response of E. coli to acid stress.

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.

Three transcription factors (TFs), OxyR, SoxR, and SoxS, play a critical role in transcriptional regulation of the defense system for oxidative stress in bacteria. However, their full genome-wide regulatory potential is unknown. Here, we perform a genome-scale reconstruction of the OxyR, SoxR, and SoxS regulons in Escherichia coli K-12 MG1655. Integrative data analysis reveals that a total of 68 genes in 51 transcription units (TUs) belong to these regulons. Among them, 48 genes showed more than 2-fold changes in expression level under single-TF-knockout conditions. This reconstruction expands the genome-wide roles of these factors to include direct activation of genes related to amino acid biosynthesis (methionine and aromatic amino acids), cell wall synthesis (lipid A biosynthesis and peptidoglycan growth), and divalent metal ion transport (Mn2+, Zn2+, and Mg2+). Investigating the co-regulation of these genes with other stress-response TFs reveals that they are independently regulated by stress-specific TFs.

Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs), ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r(2) (p<1e-6) correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions. Finally, we are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network.

The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements. Integrative data analysis reveals that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We show that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism and biofilm development is found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability.

Background: At the beginning of the transcription process, the RNA polymerase (RNAP) core enzyme requires a sigma-factor to recognize the genomic location at which the process initiates. Although the crucial role of sigma-factors has long been appreciated and characterized for many individual promoters, we do not yet have a genome-scale assessment of their function. Results: Using multiple genome-scale measurements, we elucidated the network of s-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 sigma-factor-specific promoters corresponding to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative sigma-factors (the sigma(70) and sigma(38) regulons), confirming the competition model of sigma substitution and negative regulation by alternative s-factors. Comparison with sigma-factor binding in Klebsiella pneumoniae showed that transcriptional regulation of conserved genes in closely related species is unexpectedly divergent. Conclusions: The reconstructed network reveals the regulatory complexity of the promoter architecture in prokaryotic genomes, and opens a path to the direct determination of the systems biology of their transcriptional regulatory networks.

Background The increasing number of infections caused by strains of Klebsiella pneumoniae that are resistant to multiple antibiotics has developed into a major medical problem worldwide. The development of next-generation sequencing technologies now permits rapid sequencing of many K. pneumoniae isolates, but sequence information alone does not provide important structural and operational information for its genome. Results Here we take a systems biology approach to annotate the K. pneumoniae MGH 78578 genome at the structural and operational levels. Through the acquisition and simultaneous analysis of multiple sample-matched –omics data sets from two growth conditions, we detected 2677, 1227, and 1066 binding sites for RNA polymerase, RpoD, and RpoS, respectively, 3660 RNA polymerase-guided transcript segments, and 3585 transcription start sites throughout the genome. Moreover, analysis of the transcription start site data identified 83 probable leaderless mRNAs, while analysis of unannotated transcripts suggested the presence of 119 putative open reading frames, 15 small RNAs, and 185 antisense transcripts that are not currently annotated. Conclusions These findings highlight the strengths of systems biology approaches to the refinement of sequence-based annotations, and to provide new insight into fundamental genome-level biology for this important human pathogen.