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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Mapping molecular datasets back to the brain regions they are extracted from: Remembering the native countries of hypothalamic expatriates and refugees

This article, which includes novel unpublished data along with commentary and analysis, focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined from brain tissue to the original locations within the brain that they are derived from using digital atlas mapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomic analyses conducted in the mammalian hypothalamus. Following a brief historical overview, we highlight studies that have mined biochemical and molecular information from the hypothalamus and then lay out a strategy for how these data can be linked spatially to the mapped locations in a canonical brain atlas where the data come from, thereby allowing researchers to integrate these data with other datasets across multiple scales. A key methodology that enables atlas-based mapping of extracted datasets - laser-capture microdissection - is discussed in detail, with a view of how this technology is a bridge between systems biology and systems neuroscience.

neuroscience↗

bioRxiv2018

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

Genome-, transcriptome- and proteome-wide measurements provide valuable insights into how biological systems are regulated. However, even fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we have generated a systematic, quantitative and deep proteome and transcriptome abundance atlas from 29 paired healthy human tissues from the Human Protein Atlas Project and representing human genes by 17,615 transcripts and 13,664 proteins. The analysis revealed that few proteins show truly tissue-specific expression, that vast differences between mRNA and protein quantities within and across tissues exist and that the expression levels of proteins are often more stable across tissues than those of transcripts. In addition, only ~2% of all exome and ~7% of all mRNA variants could be confidently detected at the protein level showing that proteogenomics remains challenging, requires rigorous validation using synthetic peptides and needs more sophisticated computational methods. Many uses of this resource can be envisaged ranging from the study of gene/protein expression regulation to protein biomarker specificity evaluation to name a few.

systems biology↗

bioRxiv2018

Feedback-mediated signal conversion promotes viral fitness

A fundamental signal-processing problem is how biological systems maintain phenotypic states (i.e., canalization) long after degradation of initial catalyst signals. For example, to efficiently replicate, herpesviruses (e.g., human cytomegalovirus, HCMV) rapidly counteract cell-mediated silencing using trans-activators packaged in the tegument of the infecting virion particle. But, the activity of these tegument trans-activators is inherently transient--they undergo immediate proteolysis but delayed synthesis--and how transient activation sustains lytic viral gene expression despite cell-mediated silencing is unclear. By constructing a two-color, conditional-feedback HCMV mutant, we find that positive feedback in HCMVs Immediate Early 1 (IE1) protein is of sufficient strength to sustain HCMV lytic expression. Single-cell time-lapse imaging and mathematical modeling show that IE1 positive feedback converts transient transactivation signals from tegument pp71 proteins into sustained lytic expression, which is obligate for efficient viral replication, whereas attenuating feedback decreases fitness by promoting a reversible silenced state. Together, these results identify a regulatory mechanism enabling herpesviruses to sustain expression despite transient activation signals--akin to early electronic transistors--and expose a potential target for therapeutic intervention.

systems biology↗

bioRxiv2018

A Persistence Detector for Metabolic Network Rewiring in an Animal

Biological systems must possess mechanisms that prevent inappropriate responses to spurious environmental signals. Gene regulatory network circuitries known as coherent type 1 feed-forward loops (FFLs) with AND-logic gates have been proposed to function as a persistence detector because it generates a delay in target activation and prevents target induction unless the input signal is sustained. While such a circuit has been found for the L-arabinose utilization system in E. coli, their existence and relevance multicellular organisms has remained unclear. Here, we identify the first persistence detector in an animal that redirects propionate breakdown to a shunt pathway when flux through the canonical propionate breakdown pathway is perturbed. We propose that this mechanism has evolved to ensure the shunt pathway stays off unless propionate accumulation is persistent because the shunt pathway generates highly toxic acrylate. Our study uniquely connects persistence detector circuitry to a physiological response in an animal.

systems biology↗

bioRxiv2018

Multi-scale modeling reveals angiogenesis-induced drug resistance in brain tumor and predicts a synergistic drug combination targeting EGFR and VEGFR pathways

Experimental studies have demonstrated that both the extracellular vasculature, microenvironment and intracellualr molecular network (e.g. epidermal growth factor receptor (EGFR) signaling pathways) are essentially important for brain tumor growth. Some drugs have been developed to inhibit the EGFR signaling pathways. However, how does angiogenesis affect the response of tumor cells to the drug treatment has rarely been mechanistically studied. Therefore, a multiscale model is required to investigate such complex biological systems that contain interactions and feedbacks among multi-levels. In this study, we developed a single cell-based multi-scale spatio-temporal model to simulate more realistic vascular tumor growth and drug response, based on VEGFR signaling pathways, EGFR signaling pathway and cell cycle as well as several microenvironmental factors that determine cell fate switches in a temporal and spatial context. The simulation reconstructed an evolving profile of vascular tumor growth, demonstrating the dynamic interplay between angiogenesis and various types of tumor cells (e.g., migrating, proliferating, apoptosis and quiescent cells). Moreover, we revealed the critical role of angiogenesis in the acquired drug resistance. We further investigated the optimal timing of combing VEGFR inhibition with EGFR inhibition and predicted that the drug combination targeting both EGFR pathway and VEGFR pathway has a synergistic effect. The experimental data validated the prediction of drug synergy, confirming the effectiveness of our model. The developed multiscale model explored mechanistic and functional mechanisms of angiogenesis underlying tumor growth and drug resistance, which advances our understanding of novel mechanisms of drug resistance and provides implications for designing more effective cancer therapies.\n\nAuthor summaryMany targeted therapies have been designed to treat malignant tumors including gliomas, but the clinical effectiveness of these therapies are limited due to the emergence of drug resistance during cancer therapeutics. The mechanisms underlying cancer drug resistance have not been fully understood until now, which restricts the rational designing of robust and effective therapeutics. Increasing number of experimental studies have indicated that angiogenesis plays important role s in influencing the effect of drug treatment. However, how does angiogenesis affect the response of tumor cells to the drug treatment has rarely been mechanistically studied. In this study we developed a single cell-based multi-scale spatio-temporal model to investigate the role of angiogenesis in drug response of brain tumors. The model demonstrated dual roles of angiogenesis in drug treatment of brain tumors and revealed a novel mechanism of angiogenesis-induced drug resistance. Moreover, the model predicted a synergistic drug combination targeting both EGFR and VEGFR pathways with optimal combination timing. This study has been dedicated to elucidate mechanistic and functional mechanisms of angiogenesis underlying tumor growth and drug resistance, providing implications for designing more effective drug combination therapies.

systems biology↗

bioRxiv2018

Cell-type switches induced by stochastic histone modification inheritance

AO_SCPCAPBSTRACTC_SCPCAPCell plasticity is important for tissue developments during which somatic cells may switch between distinct states. Genetic networks to yield multistable states are usually required to yield multiple states, and either external stimuli or noise in gene expressions are trigger signals to induce cell-type switches between the states. In many biological systems, cells show highly plasticity and can switch between different state spontaneously, but maintaining the dynamic equilibrium of the cell population. Here, we considered a mechanism of spontaneous cell-type switches through the combination between gene regulation network and stochastic epigenetic state transitions. We presented a mathematical model that consists of a standard positive feedback loop with changes of histone modifications during with cell cycling. Based on the model, nucleosome state of an associated gene is a random process during cell cycling, and hence introduces an inherent noise to gene expression, which can automatically induce cell-type switches in cell cycling. Our model reveals a simple mechanism of spontaneous cell-type switches through a stochastic histone modification inheritance during cell cycle. This mechanism is inherent to the normal cell cycle process, and is independent to the external signals.

systems biology↗

bioRxiv2018

Charting a tissue from single-cell transcriptomes

Massively multiplexed sequencing of RNA in individual cells is transforming basic and clinical life sciences. However, in standard experiments, tissues must first be dissociated. Thus, after sequencing, information about the spatial relationships between cells is lost although this knowledge is crucial for understanding cellular and tissue-level function. Recent attempts to overcome this fundamental challenge rely on employing additional in situ gene expression imaging data which can guide spatial mapping of sequenced cells. Here we present a conceptually different approach that allows to reconstruct spatial positions of cells in a variety of tissues without using reference imaging data. We first show for several complex biological systems that distances of single cells in expression space monotonically increase with their physical distances across tissues. We therefore seek to map cells to tissue space such that this principle is optimally preserved, while matching existing imaging data when available. We show that this optimization problem can be cast as a generalized optimal transport problem and solved efficiently. We apply our approach successfully to reconstruct the mammalian liver and intestinal epithelium as well as fly and zebrafish embryos. Our results demonstrate a simple spatial expression organization principle and that this principle (or future refined principles) can be used to infer, for individual cells, meaningful spatial position probabilities from the sequencing data alone.

systems biology↗

bioRxiv2018

A Multi-Scale Modelling Framework of the Whole Human Body Based on Multi-Objective Functions

Constraint-based multi-scale metabolic models are powerful tools to study complex biological systems like the human body, and to develop new treatment strategies for human diseases. They capture different scales of the system under study and provide the opportunity of understanding the interaction between multiple scales. In this paper, we have used our previously developed multi-scale whole body metabolism framework to establish a new approach to include multiple objectives of the human cells in computational analysis. The model has 3555 ordinary differential equations (ODEs) integrated with a genome-scale model of the hepatocyte, including 1826 biochemical reactions. The model has been solved for 74 objective functions simultaneously. Simulation results show that the integration algorithm has promise with respect to convergence, computational efficiency and response to perturbation. The results suggest that this method holds significant potential for the simulation of the range of metabolic phenotypes for mammalian cells where there are multiple objectives.

systems biology↗

bioRxiv2018

Continuous in vivo metabolism by NMR

Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties of metabolism. Here we extend high-resolution-magic angle spinning (HR-MAS) to enable continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) and provide analysis tools for these data. First, we reproduced a result in human chronic lymphoid leukemia cells by using isotope-edited CIVM-NMR to rapidly and unambiguously demonstrate unidirectional flux in branched-chain amino acid metabolism. We then collected untargeted CIVM-NMR datasets for Neurospora crassa, a classic multicellular model organism, and uncovered dynamics between central carbon metabolism, amino acid metabolism, energy storage molecules, and lipid and cell wall precursors. Virtually no sample preparation was required to yield a dynamic metabolic fingerprint over hours to days at ~4-min temporal resolution with little noise. CIVM-NMR is simple and readily adapted to different types of cells and microorganisms, offering an experimental complement to kinetic models of metabolism for diverse biological systems.

systems biology↗

bioRxiv2019

Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania

The integration of computational and mathematical approaches is used to provide a key insight into the biological systems. Here, we seek to find detailed and more robust information on Leishmanial metabolic network by performing mathematical characterization in terms of Forman/Forman-Ricci curvature measures combined with flux balance analysis (FBA). The model prototype developed largely depends on its structure and topological components. The correlation of curvature measures with various network statistical properties revealed the structural-functional framework. The analyses helped us to identify the importance of several nodes and detect sub-networks. Our results revealed several key high curvature nodes (metabolites) belonging to common yet crucial metabolic, thus, maintaining the integrity of the network which signifies its robustness. Further analysis revealed the presence of some of these metabolites in redox metabolism of the parasite. MGO, an important node, has highly cytotoxic and mutagenic nature that can irreversibly modify DNA, proteins and enzymes, making them nonfunctional, leading to the formation of AGEs and MGO[bullet]-. Being a component in the glyoxalase pathway, we further attempted to study the outcome of the deletion of the key enzyme (GLOI) mainly involved in the neutralization of MGO by utilizing FBA. The model and the objective function both kept as simple as possible, demonstrated an interesting emergent behavior. The nonfunctional GLOI in the model contributed to zero flux which signifies the key role of GLOI as a rate limiting enzyme. This has led to several fold increase production of MGO, thereby, causing an increased level of MGO[bullet]- generation. Hence, the integrated computational approaches has deciphered GLOI as a potential target both from curvature measures as well as FBA which could further be explored for kinetic modeling by implying various redox-dependent constraints on the model. Designing various in vitro experimental perspectives could churn the therapeutic importance of GLOI.\n\nAuthor SummaryLeishmaniasis, one of the most neglected tropical diseases in the world, is of primary concern due to the increased risk of emerging drug resistance. To design novel drugs and search effective molecular drug targets with therapeutic importance, it is important to decipher the relation among the components responsible for leishmanial parasite survival inside the host cell at the metabolic level. Here, we have attempted to get an insight in the leishmanial metabolic network and predict the importance of key metabolites by applying mathematical characterization in terms of curvature measures and flux balance analysis (FBA). Our results identified several metabolites playing significant role in parasites redox homeostasis. Among these MGO (methylglyoxal) caught our interest due to its highly toxic and reactive nature of irreversibly modifying DNA and proteins. FBA results helped us to look into the important role of GLOI (Glyoxalase I), the enzyme that catalyses the detoxification of MGO, in the pathway that, when non-functional, has resulted into increased level production of free radicals and AGEs (advanced glycation end products). Thus, our study has deciphered GLOI as a potential target which could further be explored for future in vitro experiments to design potential GLOI inhibitors.

systems biology↗

bioRxiv2019

PhenoGeneRanker: A Tool for Gene Prioritization Using Complete Multiplex Heterogeneous Networks

Uncovering genotype-phenotype relationships is a fundamental challenge in genomics. Gene prioritization is an important step for this endeavor to make a short manageable list from a list of thousands of genes coming from high-throughput studies. Network propagation methods are promising and state of the art methods for gene prioritization based on the premise that functionally-related genes tend to be close to each other in the biological networks.\n\nIn this study, we present PhenoGeneRanker, an improved version of a recently developed network propagation method called Random Walk with Restart on Multiplex Heterogeneous Networks (RWR-MH). PhenoGeneRanker allows multi-layer gene and disease networks. It also calculates empirical p-values of gene ranking using random stratified sampling of genes based on their connectivity degree in the network.\n\nWe ran PhenoGeneRanker using multi-omics datasets of rice to effectively prioritize the cold tolerance-related genes. We observed that top genes selected by PhenoGeneRanker were enriched in cold tolerance-related Gene Ontology (GO) terms whereas bottom ranked genes were enriched in general GO terms only. We also observed that top-ranked genes exhibited significant p-values suggesting that their rankings were independent of their degree in the network.\n\nCCS CONCEPTS* Bioinformatics * Biological networks * System biology * Computational genomics\n\nAvailability and implementationThe source code is available on GitHub at https://github.com/bozdaglab/PhenoGeneRanker under Creative Commons Attribution 4.0 license\n\nContactcdursun@mcw.edu or serdar.bozdag@marquette.edu

bioinformatics↗

bioRxiv2019

Single-cell information analysis reveals small intra- and large intercellular variations increase cellular information capacity

Cells transmit information about extracellular stimulation through signaling pathways to control cellular function. A signaling pathway can be regarded as a communication channel. In the analysis of channels of cell populations, intercellular variation is considered noise. However, intercellular variation enables individual cells to encode different information. Therefore, at the single-cell level, each cell can be regarded as an independent channel. Thus, we propose that responses of cells of the same type in tissues, such as the fibers in a skeletal muscle, should be regarded as a multiple-cell channel composed of single-cell channels, in which intercellular variation contains information. Here, we applied electrical pulses to individual myotubes from cultured C2C12 cells or dissociated skeletal muscle fibers and measured Ca2+ responses or contraction, respectively, to estimate information capacity in a biological system. For each muscle cell system, we found that a single-cell channel transmitted more information than did a cell-population channel, indicating that the cellular response is consistent with each cell (low intracellular variation) but different among individual cells (high intercellular variation). As cell number and thus the number of single-cell channels increased, a multiple-cell channel transmitted more information by incorporating the differences among individual cells. Thus, a tissue with small intracellular and large intercellular variations has the capacity to distinguish differences in stimulation intensity to precisely control physiological function.\n\nOne Sentence SummarySmall intracellular and large intercellular variations increase information transmission for precise control of tissue function.

systems biology↗

bioRxiv2019

Challenges in the calibration of large-scale ordinary differential equation models

Mathematical models based on ordinary differential equations have been employed with great success to study complex biological systems. With soaring data availability, more and more models of increasing size are being developed. When working with these large-scale models, several challenges arise, such as high computation times or poor identifiability of model parameters. In this work, we review and illustrate the most common challenges using a published model of cellular metabolism. We summarize currently available methods to deal with some of these challenges while focusing on reproducibility and reusability of models, efficient and robust model simulation and parameter estimation.

systems biology↗

bioRxiv2019

REVERSE ENGINEERING GENE NETWORKS USING GLOBAL-LOCAL SHRINKAGE RULES

Inferring gene regulatory networks from high-throughput omics data has proven to be a computationally demanding task of critical importance. Frequently the classical methods breakdown due to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology utilizes global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which was shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient MCMC via Gibbs sampling in the Appendix. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

systems biology↗

bioRxiv2019

Approximate Bayesian computation of transcriptional pausing mechanisms

At a transcriptional pause site, RNA polymerase (RNAP) takes significantly longer than average to transcribe the nucleotide before moving on to the next position. At the single-molecule level this process is stochastic, while at the ensemble level it plays a variety of important roles in biological systems. The pause signal is complex and invokes interplay between a range of mechanisms. Among these factors are: non-canonical transcription events - such as backtracking and hypertranslocation; the catalytically inactive intermediate state hypothesised to act as a precursor to backtracking; the energetic configuration of basepairing within the DNA/RNA hybrid and of those flanking the transcription bubble; and the structure of the nascent mRNA. There are a variety of plausible models and hypotheses but it is unclear which explanations are better.\n\nWe performed a systematic comparison of 128 kinetic models of transcription using approximate Bayesian computation. Under this Bayesian framework, models and their parameters were assessed by their ability to predict the locations of pause sites in the E. coli genome.\n\nThese results suggest that the structural parameters governing the transcription bubble, and the dynamics of the transcription bubble during translocation, play significant roles in pausing. This is consistent with a model where the relative Gibbs energies between the pre and posttranslocated positions, and the rate of translocation between the two, is the primary factor behind invoking transcriptional pausing. Whereas, hypertranslocation, backtracking, and the intermediate state are not required to predict the locations of transcriptional pause sites. Finally, we compared the predictive power of these kinetic models to that of a non-explanatory statistical model. The latter approach has significantly greater predictive power (AUC = 0.89 cf. 0.73), suggesting that, while current models of transcription contain a moderate degree of predictive power, a much greater quantitative understanding of transcriptional pausing is required to rival that of a sequence motif.\n\nAuthor summaryTranscription involves the copying of a DNA template into messenger RNA (mRNA). This reaction is implemented by RNA polymerase (RNAP) successively incorporating nucleotides onto the mRNA. At a transcriptional pause site, RNAP takes significantly longer than average to incorporate the nucleotide. A model which can not only predict the locations of pause sites in a DNA template, but also explain how or why they are pause sites, is sought after.\n\nTranscriptional pausing emerges from cooperation between several mechanisms. These mechanisms include non-canonical RNAP reactions; and the thermodynamic properties of DNA and mRNA. There are many hypotheses and kinetic models of transcription but it is unclear which hypotheses and models are required to predict and explain transcriptional pausing.\n\nWe have developed a rigorous statistical framework for inferring model parameters and comparing hypotheses. By applying this framework to published pause-site data, we compared 128 kinetic models of transcription with the aim of finding the best models for predicting the locations of pause sites. This analysis offered insights into mechanisms of transcriptional pausing. However, the predictive power of these models lacks compared with non-explanatory statistical models - suggesting the data contains more information than can be satisfied by current quantitative understandings of transcriptional pausing.

systems biology↗

bioRxiv2019

Fly-QMA: Automated analysis of mosaic imaginal discs in Drosophila

Mosaic analysis provides a means to probe developmental processes in situ by generating loss-of-function mutants within otherwise wildtype tissues. Combining these techniques with quantitative microscopy enables researchers to rigorously compare RNA or protein expression across the resultant clones. However, visual inspection of mosaic tissues remains common in the literature because quantification demands considerable labor and computational expertise. Practitioners must segment cell membranes or cell nuclei from a tissue and annotate the clones before their data are suitable for analysis. Here, we introduce Fly-QMA, a computational framework that automates each of these tasks for confocal microscopy images of Drosophila imaginal discs. The framework includes an unsupervised annotation algorithm that incorporates spatial context to inform the genetic identity of each cell. We use a combination of real and synthetic validation data to survey the performance of the annotation algorithm across a broad range of conditions. By contributing our framework to the open-source software ecosystem, we aim to contribute to the current move toward automated quantitative analysis among developmental biologists.\n\nAuthor summaryBiologists use mosaic tissues to compare the behavior of genetically distinct cells within an otherwise equivalent context. The ensuing analysis is often limited to qualitative insight. However, it is becoming clear that quantitative models are needed to unravel the complexities of many biological systems. In this manuscript we introduce Fly-QMA, an open-source software framework that automates the quantification of mosaic analysis for Drosophila imaginal discs, a common setting for studies of developmental processes. The software automatically extracts quantitative measurements from confocal images of mosaic tissues, rectifies any cross-talk between fluorescent reporters, and identifies clonally-related subpopulations of cells. Together, these functions allow users to rigorously ascribe changes in gene expression to the presence or absence of particular genes. We validate the performance of our framework using both real and synthetic data. Through its publication, we aim to contribute to the current move toward automated quantitative analysis among biologists.

systems biology↗

bioRxiv2019

A Chemoenzymatic Method for Glycoproteomic N-glycan Type Quantitation

Glycosylation is one of the most important post-translational modifications in biological systems. Current glycoproteome methods mainly focus on qualitative identification of glycosylation sites or intact glycopeptides. However, the systematic quantitation of glycoproteins has remained largely unexplored. Here, we developed a chemoenzymatic method to quantitatively investigate N-glycoproteome based on the N-glycan types. Taking advantage of the specificity of different endoglycosidases and isotope dimethyl labeling, six N-glycan types of structures linked on each glycopeptide, including high-mannose/hybrid, bi-antennary and tri-antennary with/without core fucose, were quantified. As a proof of principle, the glycoproteomic N-glycan type quantitative (glyco-TQ) method was first used to determine the N-glycan type composition of immunoglobulin G1 (IgG1) Fc fragment. Then we applied the method to analyze the glycan type profile of proteins in the breast cancer cell line MCF7, and quantitatively revealed the N-glycan type micro-heterogeneity at both the glycopeptide and glycoprotein levels. The novel quantitative strategy to evaluate the relative intensity of the six states of N-glycan type glycosylation on each site provides a new avenue to investigate function of glycoproteins in broad areas, such as cancer biomarker research, pharmaceuticals characterization and anti-glycan vaccine development.

systems biology↗

bioRxiv2019

KOPTIC: A novel approach for in silico prediction of enzyme kinetics and regulation

Kinetic models of metabolism (kMMs) provide not only a more accurate method for designing novel biological systems but also characterization of system regulations; however, the multi- omics data required is prohibitive to their development and widespread use. Here, we introduce a new approach named Kinetic OPTimization using Integer Conditions (KOPTIC), which can circumvent the omics data requirement and semi-automate kMM construction using in silico reaction flux data and metabolite concentration estimates derived from a metabolic network model to return plausible reaction mechanisms, regulations, and kinetic parameters (defined as reactomics) using an optimization-based approach. As a benchmark for the performance of KOPTIC, a previously published, four-tissue (leaf, root, seed, and stem) metabolic model of Arabidopsis thaliana was used, consisting of major primary carbon metabolism pathways, named p-ath780 (1015 reactions, 901 metabolites, and 780 genes). Data required for KOPTIC was derived from an Arabidopsis lifecycle of 61 days. Nine separate regulator restriction sets (allowing multiple solutions) defining KOPTIC runs hypothesized 3577 total regulatory interactions involving metabolic, allosteric, and transcriptional regulatory mechanisms (with nearly 40 verified by existing literature) with a median fit error of 13.44%. Flux rates of most KOPTIC fits were found to be significantly correlated with (93.6% with p < 0.05) and approximately 1:1 (r = 0.775, p << 0.001) to the input time-series data. Thus, KOPTIC can hypothesize maps the regulatory landscape for a specific reaction, out of which the most relevant regulatory interaction(s) can be defined by the desired growth/stress conditions or the desired genetic interventions for use in the creation of kMMs.

systems biology↗