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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Integrated Human-Virus Metabolic Modelling Predicts Host-Based Antiviral Targets Against Chikungunya, Dengue and Zika Viruses

Current and reoccurring viral epidemic outbreaks such as those caused by Zika virus illustrate the need for rapid development of antivirals. Such development would be immensely facilitated by computational approaches that can provide experimentally testable predictions for possible antiviral strategies. A key factor that has not been considered fully to date in the study of antiviral targets is the high dependence of viruses to their host metabolism for reproduction. Here, we focus on this dependence and develop a stoichiometric, genome-scale metabolic model that integrates human macrophage cell metabolism with the biochemical demands arising from virus production. Focusing this approach to currently epidemic viruses Chikungunya, Dengue and Zika, we find that each virus causes specific alterations in the host metabolic flux towards fulfilling their individual biochemical demands as predicted by their genome and capsid structure. Subsequent analysis of this integrated model allows us to predict a set of host reactions, which when constrained can inhibit virus production. We show that this prediction recovers most of the known targets of existing antiviral drugs, while highlighting a set of hitherto unexplored reactions with either broad or virus specific antiviral potential. Thus, this computational approach allows rapid generation of experimentally testable hypotheses for novel antiviral targets within a host.\n\nSIGNIFICANCE STATEMENTA key challenge in combatting any new and emerging virus outbreaks is rapid drug development. In particular, generation of experimentally testable hypotheses through computational approaches is mostly lacking. Here, we address this gap by developing host-virus metabolic models for three viruses that cause current (or previously) epidemic viral outbreaks. We develop viral biomass functions using information from their genomes and physical structure, and incorporate these within a genome-scale metabolic model of human macrophage cells. The resulting integrated model allows us to predict host reactions, which when blocked, stop the system from attaining optimal viral production. These predictions recover currently known antiviral targets within human cells, and highlight a set of new reactions that are hitherto not explored for antiviral capacity.

systems biology↗

bioRxiv2017

Two Chinese medicine species constants and the accurate identification of Chinese medicines

Since the ancient times, all over the world, the identification of herbal medicines have to be based on empirical knowledge. In this article two species constants of traditional Chinese medicines(TCM) were discovered relying on the maximum information states of Dual index information theory equation, or common heredity and variation information theory. The two species constants, common peak ratios Pg = 61% and Pg = 70%, which corresponding to symmetry and asymmetry variation states, respectively, were used as two absolute quantitative criteria to identify complex biology systems-TCM. Considered the influences of many other factors on components and experiment processes, the practical theoretical identification standards should be established Pg[greater double equals]58~64% and Pg[greater double equals]67~73%, within the relativeerror within -3% and + 3% of information value around the maximum information states. Combining the maximum number of effective sample optimum method with this two theoretical standards, the optimized classification of a TCM sample set can be carried out correctly. 42 samples belonging to four species of combination Chinese medicines were tested. The infrared (IR) fingerprint spectra (FPS) of their powder were measured and analyzed by means of the approach provided above. Among the six pairs of four Chinese medicine species, five of them follow the species constant Pg=61%, one of them obeys the Pg = 70%. The correct recognition ratio of samples was 95.2%, and that of species was 100%.

pharmacology and toxicology↗

bioRxiv2017

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

Transcriptional profiling is a prevalent and powerful approach for capturing the response of crop plants to environmental stresses, e.g. response of rice to drought. However, functionally interpreting the resulting genome-wide gene expression changes is severely hampered by the large gaps in our genomic knowledge about which genes work together in cellular pathways/processes in rice. Here, we present a new web resource - RECoN - that relies on a network-based approach to go beyond currently limited annotations in delineating functional and regulatory perturbations in new rice stress transcriptome datasets generated by a researcher. To build RECoN, we first enumerated 1,744 stress-specific gene modules covering 28,421 rice genes (>72% of the genes in the genome). Each module contains a group of genes tightly coexpressed across a large number of environmental conditions and, thus, is likely to be functionally coherent. When a user provides a new differential expression profile, RECoN identifies modules substantially perturbed in their experiment and further suggests deregulated functional and regulatory mechanisms based on the enrichment of current annotations within the predefined modules. We demonstrate the utility of this resource by analyzing new drought transcriptomes of rice in three developmental stages, which revealed large-scale insights into the cellular processes and regulatory mechanisms involved in common and stage-specific drought responses. RECoN enables biologists to functionally explore new data from all abiotic stresses on a genome-scale and to uncover gene candidates, including those that are currently functionally uncharacterized, for engineering stress tolerance.

systems biology↗

bioRxiv2017

Maximal growth rate requires minimal metabolic complexity

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized -in a static environment under mass-conservation and enzyme expression constraints- we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.\n\nAuthor summaryThe microbial genome encodes for a large network of enzyme-catalyzed reactions. The reaction rates depend on concentrations of enzymes and metabolites, which in turn depend on those rates. Cells face a number of biophysical constraints on enzyme expression, for example due to a limited membrane area or cytosolic volume. Considering this complexity and nonlinearity of metabolism, how is it possible, that experimental data can often be described by simple linear models? We show that it is evolution itself that selects for simplicity. When reproductive rate is maximised, the number of active independent metabolic pathways is bounded by the number of growth-limiting enzyme constraints, which is typically small. A small number of pathways automatically generates the measured simple relations. We identify the importance of growth-limiting constraints in shaping microbial behaviour, by focussing on their mechanistic nature. We demonstrate that overflow metabolism - an important phenomenon in bacteria, yeasts, and cancer cells - is caused by two constraints on enzyme expression. We derive experimental guidelines for constraint identification in microorganisms. Knowing these constraints leads to increased understanding of metabolism, and thereby to better predictions and more effective manipulations.

systems biology↗

bioRxiv2017

Diversity of translation initiation mechanisms across bacterial species is driven by environmental conditions and growth demands

The Shine-Dalgarno (SD) sequence is often found upstream of protein coding genes across the bacterial kingdom, where it enhances start codon recognition via hybridization to the anti-SD (aSD) sequence on the small ribosomal subunit. Despite widespread conservation of the aSD sequence, the proportion of SD-led genes within a genome varies widely across species, and the evolutionary pressures shaping this variation remain largely unknown. Here, we conduct a phylogenetically-informed analysis and show that species capable of rapid growth have a significantly higher proportion of SD-led genes in their genome, suggesting a role for SD sequences in meeting the protein production demands of rapidly growing species. Further, we show that utilization of the SD sequence mechanism co-varies with: i) genomic traits that are indicative of efficient translation, and ii) optimal growth temperatures. In contrast to prior surveys, our results demonstrate that variation in translation initiation mechanisms across genomes is largely predictable, and that SD sequence utilization is part of a larger suite of translation-associated traits whose diversity is driven by the differential growth strategies of individual species.

systems biology↗

bioRxiv2017

Assessment of clonal kinetics reveals multiple trajectories of dendritic cell development

A thorough understanding of cellular development is incumbent on assessing the complexities of fate and kinetics of individual clones within a population. Here, we develop a system for robust periodical assessment of lineage outputs of thousands of transient clones and establishment of bona fide cellular trajectories. We appraise the development of dendritic cells (DCs) from barcode-labeled hematopoietic stem and progenitor cells (HSPCs) by serially measuring barcode signatures, and visualize this multidimensional data using novel developmental interpolated t-distributed stochastic neighborhood embedding (Di-SNE) time-lapse movies. We identify multiple cellular trajectories of DC development that are characterized by distinct fate bias and expansion kinetics, and determine that these are intrinsically programmed. We demonstrate that conventional DC and plasmacytoid DC trajectories are largely separated already at the HSPC stage. This framework allows systematic evaluation of clonal dynamics and can be applied to other steady-state or perturbed developmental systems.

systems biology↗

bioRxiv2017

Information topology of gene expression profile in dopaminergic neurons

SUMMARY PARAGRAPHExtracting high-degree interactions and dependences between variables (pairs, triplets, ... k-tuples) is a challenge posed by all omics approaches1, 2. Here we used multivariate mutual information (Ik) analysis3 on single-cell retro-transcription quantitative PCR (sc-RTqPCR) data obtained from midbrain neurons to estimate the k-dimensional topology of their gene expression profiles. 41 mRNAs were quantified and statistical dependences in gene expression levels could be fully described for 21 genes: Ik analysis revealed a complex combinatorial structure including modules of pairs, triplets (up to 6-tuples) sharing strong positive, negative or zero Ik, corresponding to co-varying, clustering and independent sets of genes, respectively. Therefore, Ik analysis simultaneously identified heterogeneity (negative Ik) of the cell population under study and regulatory principles conserved across the population (homogeneity, positive Ik). Moreover, maximum information paths enabled to determine the size and stability of such transcriptional modules. Ik analysis represents a new topological and statistical method of data analysis.

systems biology↗

bioRxiv2017

CINNA: Deciphering Central Informative Nodes in Network Analysis

In network science, usually there is a critical step known as centrality analysis. This is an important step, since by using centrality measures, a large number of vertices with low priority are set aside and only a few ones remain to be used for further inferential outcomes. In the other words, these measures help us to sieve our large network and distinguish coarse vertices. By that, important decisions could be made based on the circumstances of these vertices on the overall behavior of networks. These vertices are potentially assumed as central or essential nodes. However, the centrality analysis has always been accompanied by a series of ambiguities, since there are a large number of well-known centrality measures, with different algorithms pointing to these essential nodes and there is no well-defined preference. Which measure explore more information in a given network about node essentiality according to the topological features? While here, we tried to provide a pipeline to have a comparison among all proper centrality measures regarding the network structure and choose the most informative one according to dimensional reduction methods. Central Informative Nodes in Network Analysis (CINNA) package is prepared to gather all required function for centrality analysis in the weighted/unweighted and directed/undirected networks.\n\nAvailability and implementationCINNA is available in CRAN, including a tutorial. URL: https://cran.r-proiect.org/web/packages/CINNA/index.html\n\nContact: miafari@pasteur.ac.ir

systems biology↗

bioRxiv2017

Trade-off and flexibility in the dynamic regulation of the cullin-RING ubiquitin ligase repertoire

Cullin-RING ubiquitin ligases (CRLs) catalyze the ubiquitylation of substrates many of which are degraded by the 26S proteasome. Their modular architecture enables recognition of numerous substrates via exchangeable substrate receptors that competitively bind to a cullin scaffold with high affinity. Due to the plasticity of these interactions there is ongoing uncertainty how cells maintain a flexible CRL repertoire in view of changing substrate loads. Based on a series of in vivo and in vitro studies, different groups proposed that the exchange of substrate receptors is mediated by a protein exchange factor named cullin-associated and neddylation-dissociated 1 (Cand1). Here, we have performed quantitative mathematical modeling to support this hypothesis. To this end we first show that the exchange activity of Cand1 necessarily leads to a trade-off between high ligase activity and fast receptor exchange. Supported by previous in vivo studies we argue that this trade-off yields an optimal Cand1 concentration where the time scale for substrate degradation becomes minimal. In a second step we show through simulations that (i) substrates bias the CRL repertoire leading to preferential assembly of ligases for which substrates are available and (ii) differences in binding affinities create a temporal hierarchy for the degradation of substrates. Together, our results provide general constraints for the operating regimes of molecular exchange systems and suggest that Cand1 endows the CRL network with the properties of an \"on demand\" system allowing cells to dynamically adjust their CRL repertoire to fluctuating substrate abundances.\n\nAuthor summaryCullin-RING ubiquitin ligases (CRLs) are multisubunit protein complexes where exchangeable substrate receptors (SRs) assemble on a cullin scaffold to mediate ubiquitylation and subsequent degradation of a large variety of substrates. In humans there are hundreds of different CRLs having potentially thousands of substrates. Due to the high affinity of cullin-SR interactions, it has long been a mystery how cells would maintain flexibility to sample the entire SR repertoire in order to match fluctuating substrate loads. Recent experiments indicate that the exchange of different SRs is mediated by a novel protein exchange factor (Cand1). However, the proposed biochemical function of Cand1 as a promoter of CRL activity remained difficult to reconcile with previous reports of Cand1 acting as an inhibitor of CRL activity in vitro. Here we show that these two findings are not contradictory, but that the exchange activity of Cand1 necessarily leads to a trade-off between high ligase activity and fast receptor exchange which leads us to predict an optimal Cand1 concentration and a temporal hierarchy for substrate degradation. Our results support the view that Cand1 endows the CRL network with the flexibility of an \"on demand\" system where relative CRL abundances are dictated by substrate availability.

systems biology↗

bioRxiv2017

Piper longum Linn: A review of its phytochemicals and their network pharmacological evaluation

Piper longum L. (P. longum, also called as long pepper) is one of the common culinary herb and has been extensively used as an important constituent of various indigenous medicines, specifically in traditional Indian medicinal system known as Ayurveda. Towards obtaining a global regulatory framework of P. longums constituents, in this work we first reviewed phytochemicals present in this herb and then studied their pharmacological and medicinal features using network pharmacology approach. We developed high-confidence level tripartite networks consisting of phytochemicals - protein targets - disease association and explain the role of its phytochemicals to various chronic diseases. 7 drug-like phytochemicals in this herb were found as the potential regulators of 5 FDA approved drug targets; and 28 novel drug targets were also reported. 105 phytochemicals were linked with immunomodulatory potency by pathway level mapping in human metabolic network. A sub-network of human PPI regulated by its phytochemicals was derived and various modules in this sub-network were successfully associated with specific diseases.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC=\"FIGDIR/small/169763_ufig1.gif\" ALT=\"Figure 1000\">\nView larger version (37K):\norg.highwire.dtl.DTLVardef@1254f37org.highwire.dtl.DTLVardef@73aeb9org.highwire.dtl.DTLVardef@d416cborg.highwire.dtl.DTLVardef@246ced_HPS_FORMAT_FIGEXP M_FIG C_FIG Abbreviations

systems biology↗

bioRxiv2017

A Framework to Simulate Cortical microtubule Dynamics inArbitrary Shaped Plant Cells

Plant morphogenesis is strongly dependent on the directional growth and the subsequent oriented division of individual cells. It has been shown that the plant cortical microtubule array plays a key role in controlling both these processes. This ordered structure emerges as the collective result of stochastic interactions between large numbers of dynamic microtubules. To elucidate this complex self-organization process a number of analytical and computational approaches to study the dynamics of cortical microtubules have been proposed. To date, however, these models have been restricted to 2D planes or geometrically simple surfaces in 3D, which strongly limits their applicability as plant cells display a wide variety of shapes. This limitation is even more acute, as both local as well as global geometrical features of cells are expected to influence the overall organization of the array. Here we describe a framework for efficiently simulating microtubule dynamics on triangulated approximations of arbitrary three dimensional surfaces. This allows the study of microtubule array organization on realistic cell surfaces obtained by segmentation of microscopic images. We validate the framework against expected or known results for the spherical and cubical geometry. We then use it to systematically study the individual contributions of global geometry, edge-induced catastrophes and cell face-induced stability to array organization in a cuboidal geometry. Finally, we apply our framework to analyze the highly non-trivial geometry of leaf pavement cells of Nicotiana benthamiana and Hedera helix. We show that our simulations can predict multiple features of the array structure in these cells, revealing, among others, strong constraints on the orientation of division planes.

systems biology↗

bioRxiv2017

Role of duplicate genes in determining the tissue-selectivity of hereditary diseases

A longstanding puzzle in human genetics is what limits the clinical manifestation of hundreds of hereditary diseases to certain tissues or cell types, while their causal genes are present and expressed throughout the human body. Here we considered a possible role for paralogs of causal genes in affecting this tissue selectivity. It has been shown across organisms that paralogs can compensate for the loss of each other. We hypothesized that specifically in the disease tissue causal genes and their paralogs are imbalanced, leading to insufficient compensation and to the emergence of disease phenotypes. While demonstrated previously in the context of few specific diseases, this hypothesis was never assessed quantitatively at large-scale. For this, we analyzed functional relationships between causal genes and their paralogs associated with 112 tissue-selective hereditary diseases. To test our hypothesis we used several large-scale omics datasets, including RNA sequencing profiles of over 30 different human tissues. Indeed, the expression of causal genes and their paralogs was significantly imbalanced in their disease tissues compared to unaffected tissues. Imbalanced expression was evident across different disease tissues, and was common to causal genes with single or multiple paralogs. This imbalance was driven by significant upregulation of the causal gene in its disease tissue, often combined with significant down-regulation of a paralog. Nevertheless, in additional 20% of the causal genes, a paralog alone was significantly down-regulated in the disease tissue. Our results suggest that dosage relationships between paralogs affect the phenotypic outcome of germline aberrations, adding paralogs as important modifiers of disease manifestation.

systems biology↗

bioRxiv2017

Joint regulation of growth and division timing drives size homeostasis in proliferating animal cells

How organisms maintain cell size homeostasis is a long-standing problem that remains unresolved, especially in multicellular organisms. Recent experiments in diverse animal cell types demonstrate that within a cell population the extent of growth and cellular proliferation (i.e., fitness) is low for small and large cells, but high at intermediate sizes. Here we use mathematical models to explore size-control strategies that drive such a non-monotonic fitness profile resulting in an optimal cell size. Our analysis reveals that if cell size grows exponentially or linearly over time, then fitness always varies monotonically with size irrespective of how timing of division is regulated. Furthermore, if the cell divides upon attaining a critical size (as in the Sizer or size-checkpoint model), then fitness always increases with size irrespective of how growth rate is regulated. These results show that while several size control models can maintain cell size homeostasis, they fail to predict the optimal cell size, and hence unable to explain why cells prefer a certain size. Interestingly, fitness maximization at an optimal size requires two key ingredients: 1) The growth rate decreases with increasing size for large enough cells; and 2) The cell size at the time of division is a function of the newborn size. The latter condition is consistent with the Adder paradigm for division control (division is triggered upon adding a fixed size from birth), or a Sizer-Adder combination. Consistent with theory, Jurkat T cell growth rates, as measured via oxygen consumption or mitochondrial activity, increase with size for small cells, but decrease with size for large cells. In summary, regulation of both growth and cell division timing is critical for size control in animal cells, and this joint-regulation leads to an optimal size where cellular fitness is maximized. Address inquires to A. Singh, E-mail: absingh@udel.edu.

systems biology↗

bioRxiv2017

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

The response of cancer cells to drugs is determined by various factors, including the cells mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs.

systems biology↗

bioRxiv2017

Spatially varying cis-regulatory divergence in Drosophila embryos elucidates cis-regulatory logic

Spatial patterning of gene expression is a key process in development--responsible for the incredible diversity of animal body plans--yet how it evolves is still poorly understood. Both cis- and trans-acting changes could accumulate and participate in complex interactions, so to isolate the cis-regulatory component of patterning evolution, we measured allele-specific spatial gene expression patterns in D. melanogaster x D. simulans hybrid embryos. RNA-seq of cryosectioned slices revealed 55 genes with strong spatially varying allele-specific expression, and several hundred more with weaker but significant spatial divergence. For example, we found that hunchback (hb), a major regulator of developmental patterning, had reduced expression specifically in the anterior tip of D. simulans embryos. Mathematical modeling of hb cis-regulation suggested that a mutation in a Bicoid binding site was responsible, which we verified using CRISPR-Cas9 genome editing. In sum, even comparing morphologically near-identical species we identified a substantial amount of spatial variation in gene expression, suggesting that development is robust to many such changes, but also that natural selection may have ample raw material for evolving new body plans via cis-regulatory divergence.

systems biology↗

bioRxiv2017

Hybrid approach for parameter estimation in agent-based models

Agent-based models are valuable in cancer research to show how different behaviors emerge from individual interactions between cells and their environment. However, calibrating such models can be difficult, especially if the parameters that govern the underlying interactions are hard to measure experimentally. Herein, we detail a new method to converge on parameter sets that fit an agent-based model to multiscale data using a model of glioblastoma as an example.

systems biology↗

bioRxiv2017

SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site

The DNA-binding interfaces of the androgen (AR) and glucocorticoid (GR) receptors are virtually identical, yet these transcription factors share only about a third of their genomic binding sites and regulate similarly distinct sets of target genes. To address this paradox, we determined the intrinsic specificities of the AR and GR DNA binding domains using a refined version of SELEX-seq. We developed an algorithm, SelexGLM, that quantifies binding specificity over a large (31 bp) binding-site by iteratively fitting a feature-based generalized linear model to SELEX probe counts. This analysis revealed that the DNA binding preferences of AR and GR homodimers differ significantly, both within and outside the 15bp core binding site. The relative preference between the two factors can be tuned over a wide range by changing the DNA sequence, with AR more sensitive to sequence changes than GR. The specificity of AR extends to the regions flanking the core 15bp site, where isothermal calorimetry measurements reveal that affinity is augmented by enthalpy-driven readout of poly-A sequences associated with narrowed minor groove width. We conclude that the increased specificity of AR is correlated with more enthalpy-driven binding than GR. The binding models help explain differences in AR and GR genomic binding, and provide a biophysical rationale for how promiscuous binding by GR allows functional substitution for AR in some castration-resistant prostate cancers.

systems biology↗

bioRxiv2017

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. The method uses integer linear programming (ILP) to integrate stress-responsive phospho-proteome responses in wild-type and mutant strains, predicted phosphorylation motifs on groups of coregulated peptides, and published protein interaction data. A key advance is that by grouping peptides into submodules before inference, the method can overcome missing protein interactions in published datasets to predict novel, stress-dependent protein interactions and phosphorylation events. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

systems biology↗