SEARCH · Search bioRxiv

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 973 records · Page 54Linked to original sources

bioRxiv2017

Population-based mechanistic modeling allows for quantitative predictions of drug responses across cell types

Quantitative mismatches between human physiology and experimental models can present serious limitations for the development of effective therapeutics. We addressed this issue, in the context of cardiac electrophysiology, through mechanistic mathematical modeling combined with statistical analyses. Physiological metrics were simulated in heterogeneous populations describing cardiac myocytes from adult ventricles and those derived from induced pluripotent stem cells (iPSC-CMs). These simulated measures were used to construct a cross-cell type regression model that predicts adult myocyte drug responses from iPSC-CM behaviors. We found that quantitatively accurate predictions of responses to selective or non-selective drugs could be generated based on iPSC-CM responses and that the method can be extended to predict drug responses in diseased as well as healthy cells. This cross-cell type model can be of great value in drug development, and the approach, which can be applied to other fields, represents an important strategy for overcoming experimental model limitations.

systems biology↗

bioRxiv2017

Investigating the existence of periodicity in activity of neural network by novel neural signal processing technique - quantifying induced learning in cell culture

The network forming ability of neurons are huge for their sparking ability to form new connections and break existing ones. This sheer ability allows dynamic nature of the network for which this network are ever changing. The neurons being cells that are chemically and electrically excitable, electrical excitation of these cells cause variation of voltage in vicinity of the active neurons. These variation captured through electrical recording device records to activity points in the network. Cultured neuron cells on Multi electrode array dish is used to study disassociated cultures. A novel integrative model of neural signal processing termed as Activity Index is applied. AI variation is plotted graphically to show the evidence in periodicity of network analysis. The finding on periodicity are discussed along with how could it be used as a potential parameter to quantify learning ability of a cell culture.\n\nIndex Termsneurons, dynamic, variation of voltage, Multi electrode array, Activity Index train, Periodicity, learning

systems biology↗

bioRxiv2017

WDR88, CCDC11, and ARPP21 genes indulge profoundly in the desmoplastic retort to prostate and breast cancer metastasis.

Abstract.Microarray technology has unlocked doors to a multitude of open analysis problems that if conceived with efficacy may uncover varied genotypic and phenotypic traits. Algorithms belonging to different cultures in computer science have been applied to gene expression data to derive correlation and stratification parameters. While most outcomes are subject to clinical validation, majority of which get declined, the search for the precisely targeted therapeutic agents is still on. This paper is an effort in the similar direction and strives to delineate genes with significant stromal signatures. We suggest a corroborative indulgence of a human laterality disorder gene, CCDC11 in the metastasis, in addition to the role of WDR88 and ARPP21 genes has been further materialized in the analysis. Another standout aspect of the study has been the associated implications of the genes in rare disorders of male breast and female prostate cancers. There is also a threshold proposal that stratifies \"safe\" expression space for genes. Complimentarily, the manuscript serves as an expedient protocol for anyone seeking microarray data analysis, particularly in R.

systems biology↗

bioRxiv2017

Single-cell RNA-seq reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress

From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single S. cerevisiae cells treated with and without salt stress, to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells decouple expression of ribosomal protein genes from the environmental stress response, in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results reveal that cells maintain a cellular filter against decoupled bursts of transcription-factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.

systems biology↗

bioRxiv2017

A genome-scale study of metabolic complementation in endosymbiotic consortia: the case of the cedar aphid

Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism -which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca. Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome scale metabolic network (GEM) for the bacterial consortium from the cedar aphid iBSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e. tryptophan and biotin biosynthesis) and the proposal of a hitherto unnoticed event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with iBSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is more significant than previously thought.

systems biology↗

bioRxiv2017

A Mathematical model to predict Trichodesmiun spp. bloom during a sewage burst

A modified version of the Monod equation is proposed to understand the bloom emergence of Trichodesmiun spp. in presence of sewage outburst. The mathematical model predicts that the bacterial population does not affect the bloom growth. Also, the quotient between the bacteria during the sewage outburst and before is proposed to be equal to the quotient between the influx of limiting resources during burst and the influx before the burst.

systems biology↗

bioRxiv2017

Systemic post-translational control of bacterial metabolism regulates adaptation in dynamic environments

Across all domains of life, elaborate control mechanisms regulate proteins, pathways, and cell phenotypes as organisms adapt to ever-changing environments. Post-translational modifications (PTMs) allow cells to rapidly and reversibly regulate molecular pathways, but it remains unclear how individual PTMs regulate fitness. Here, we studied >130 PTM sites in Escherichia coli to unravel how PTMs regulate cell metabolism and fitness in response to environmental changes, such as the glucose-acetate diauxie. Using a new metabolic modeling approach, we found a significant fraction of post-translationally modified enzymes are predicted to control shifts in pathway usage following evolutionarily-important environmental changes. Genetic screens using Multiplex Automated Genome Engineering confirmed that these PTMs impact cellular fitness, especially under dynamically changing environments. Finally, mechanisms of how individual PTMs impact protein function were detailed using molecular dynamics simulations and enzyme assays for enolase, transaldolase, and serine hydroxymethyltransferase. Thus, by integrating whole-cell data and pathway modeling with detailed biochemical analysis, we unraveled how individual PTMs regulate enzymes, pathways, and phenotypes to adapt to sudden environmental changes.

systems biology↗

bioRxiv2017

A Portable Software Library that Implements the Gillespie Direct Method

A common approach to simulating chemical reaction systems is to formulate the problem as a set of ordinary differential equation and solve them using standard numerical procedures. Such a formulation works well when the number of molecules involved in the simulation is large and assumes a deterministic formulation for the underling chemical kinetics. However for systems such as gene regulatory networks where the number of molecules of a specific species can be of the order of 10s of molecules, the deterministic approach fails to capture important dynamical features. At low molecular numbers stochastic events dominate the dynamics. As a result much research has been devoted to developing numerical algorithms for computing the stochastic trajectories. One of the most common approaches is based on Gillespies stochastic simulation algorithm, for example the direct method. Due to its simplicity the direct method has been implemented many times in software. However, unusually, there does not appear to be a C based reusable software library. This article describes the development of such a library. All source code is licensed under the liberal Apache 2.0 open source license and is available at Github: https://github.com/sys-bio/libStochastic

systems biology↗

bioRxiv2017

Mapping DNA damage-dependent genetic interactions in yeast via orgy mating and barcode fusion genetics

Condition-dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State-of-the-art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double mutant strains, does not scale readily to multi-condition studies. Here we describe Barcode Fusion Genetics to map Genetic Interactions (BFG-GI), by which double mutant strains generated via en masse party mating can also be monitored en masse for growth and genetic interactions. By using site-specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG-GI enables multiplexed quantitative tracking of double mutants via next-generation sequencing. We applied BFG-GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4-nitroquinoline 1-oxide (4NQO), bleomycin, zeocin, and three other DNA-damaging environments. BFG-GI recapitulated known genetic interactions and yielded new condition-dependent genetic interactions. We validated and further explored a subnetwork of condition-dependent genetic interactions involving MAG1, SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to a decrease in the activation or activity of the checkpoint protein kinase Rad53.

systems biology↗

bioRxiv2017

A Mathematical Model of the Phosphoinositide Pathway

Phosphoinositides are signaling lipids that constitute a complex network regulating many cellular processes. We propose a computational model that accounts for all known species of phosphoinositides in the plasma membrane of mammalian cells. The model replicates the steady-state of the phosphoinositide pathway and most known dynamic phenomena. Furthermore, sensitivity analysis demonstrates model robustness to moderate alterations in any of the parameters. Model analysis suggest that the greatest contributor to PI(4,5)P2 production is a flux representing the direct transformation of PI into PI(4,5)P2 and is also responsible for the maintenance of this pool when PI(4)P is decreased. PI(5)P is also shown to be a significant source for PI(4,5)P2 production. The model was validated with data from siRNA screens that knocked down the expression of several enzymes in the pathway. The screen monitored the activity of the epithelium sodium channel, ENaC, which is activated by PI(4,5)P2. Moderating ENaC activity can have a therapeutic effect in Cystic Fibrosis (CF) patients. Our model suggests control strategies where the activities of the enzyme PIP5KI or the PI4K+PIP5KI+DVL protein complex are decreased and cause an efficacious reduction in PI(4,5)P2 levels while avoiding undesirable alterations in other phosphoinositide pools.\n\nAbbreviations

systems biology↗

bioRxiv2017

Fitting mathematical models of biochemical pathways to steady state perturbation response data without simulating perturbation experiments

A common experimental approach for studying signal transduction networks (STNs) is to measure the steady state concentrations of their components following perturbations to individual components. Such data is frequently used to reconstruct topological models of STNs, but, are rarely used for calibrating kinetic models of these networks. This is because, existing calibration algorithms operate by assigning different sets of values to the parameters of the kinetic models, and for each set of values simulating all perturbations performed in the biochemical experiments. This process is highly computation intensive and may be infeasible when molecular level information of the perturbation experiments is unavailable. Here, I propose an algorithm which can calibrate ordinary differential equation (ODE) based kinetic models of STNs using steady-state perturbation responses (SSPRs) without simulating perturbation experiments. The proposed algorithm uses modular response analysis (MRA) to calculate the scaled Jacobian matrix of the ODE model of an STN using SSPR data. The model parameters are then calibrated to fit the scaled Jacobian matrix calculated in the above step. This procedure does not require simulating the perturbation experiments. Therefore, it is significantly less computation intensive than existing algorithms and can be implemented without molecular level knowledge of the mechanism of perturbations. It is also parallelizable, i.e. can explore multiple sets of parameter values simultaneously, and therefore is scalable. The capabilities and shortcomings of the proposed algorithm are demonstrated using both simulated and real perturbation responses of Mitogen Activated Protein Kinase (MAPK) STN.\n\nAvailabilityAll source codes and data needed to replicate the results in this manuscript are available from https://github.com/SBIUCD/MRA_SMC_ABC1

systems biology↗

bioRxiv2017

A minimal model of protein allocation during phototrophic growth

Photoautotrophic growth depends upon an optimal allocation of finite cellular resources to diverse intracellular processes. Commitment of a certain mass fraction of the proteome to a specific cellular function, typically reduces the proteome available for other cellular functions. Here, we develop a minimal semi-quantitative kinetic model of cyanobacterial phototrophic growth to describe such trade-offs of cellular protein allocation. The model is based on coarse-grained descriptions of key cellular processes, in particular carbon uptake, metabolism, photosynthesis, and protein translation. The model is parametrized using literature data and experimentally obtained growth curves. Of particular interest are the resulting cyanobacterial growth laws as fundamental characteristics of cellular growth. We show that the model gives rise to similar growth laws as observed for heterotrophic organisms, with several important differences due to the distinction between light energy and carbon uptake. We discuss recent experimental data supporting the model results and show that minimal growth models have implications for our understanding of the limits of phototrophic growth and bridge a gap between molecular physiology and ecology.

systems biology↗

bioRxiv2017

Cell size control driven by the circadian clock and environment in cyanobacteria

How cells maintain their size has been extensively studied under constant conditions. In the wild, however, cells rarely experience constant environments. Here, we examine how the 24-hour circadian clock and environmental cycles modulate cell size control and division timings in the cyanobacterium Synechococcus elongatus using single-cell time-lapse microscopy. Under constant light, wild type cells follow an apparent sizer-like principle. Closer inspection reveals that the clock generates two subpopulations, with cells born in the subjective day following different division rules from cells born in subjective night. A stochastic model explains how this behaviour emerges from the interaction of cell size control with the clock. We demonstrate that the clock continuously modulates the probability of cell division throughout day and night, rather than solely applying an on-off gate to division as previously proposed. Iterating between modelling and experiments, we go on to show that the combined effects of the environment and the clock on cell division are explained by an effective coupling function. Under naturally graded light-dark cycles, this coupling shifts cell division away from dusk and dawn, when light levels are low and cell growth is reduced. Our analysis allows us to disentangle, and predict the effects of, the complex interactions between the environment, clock, and cell size control.

systems biology↗

bioRxiv2017

Mechanistic causes of constrained phenotypic variation revealed by synthetic gene regulatory networks

Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms behind such restrictions are still poorly understood. Gene regulatory networks have been proposed as one cause of constrained phenotypic variation. However, most of the evidence for this is theoretical rather than experimental. Here, we study evolutionary biases in two synthetic gene regulatory circuits expressed in E. coli that produce a gene expression stripe - a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. Consequently, seemingly equivalent networks can indeed be distinct in how they constrain the outcome of further evolution.

systems biology↗

bioRxiv2017

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -- one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -- for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL). These data are available for download via the Gene Expression Omnibus (accession GSE101406), and web apps for interacting with this resource are available at https://clue.io/proteomics.\n\nHighlightsO_LIFirst-of-its-kind public resource of proteomic responses to systematically administered perturbagens\nC_LIO_LIDirect proteomic profiling of phosphosignaling and chromatin states in cells for 90 drugs in six different cell lines\nC_LIO_LIExtends Connectivity Map concept to proteomic data for integration with transcriptional data\nC_LIO_LIEnables recognition of unexpected, cell type-specific activities and potential translational therapeutic opportunities\nC_LI

systems biology↗

bioRxiv2017

Hsf1 phosphorylation generates cell-to-cell variation in Hsp90 levels and promotes phenotypic plasticity

Clonal populations of cells exhibit cell-to-cell variation in the transcription of individual genes. In addition to this \"noise\" in gene expression, heterogeneity in the proteome and the proteostasis network expands the phenotypic diversity of a population. Heat shock transcription factor (Hsf1) regulates chaperone gene expression, thereby coupling transcriptional noise to proteostasis. Here we show that cell-to-cell variation in Hsf1 activity is an important determinant of phenotypic plasticity. Budding yeast cells with high Hsf1 activity were enriched for the ability to acquire resistance to an antifungal drug, and this enrichment depended on Hsp90 - a known \"phenotypic capacitor\" and canonical Hsf1 target. We show that Hsf1 phosphorylation promotes cell-to-cell variation, and this variation - rather than absolute Hsf1 activity - promotes antifungal resistance. We propose that Hsf1 phosphorylation enables differential tuning of the proteostasis network in individual cells, allowing populations to access a wide range of phenotypic states.

systems biology↗

bioRxiv2017

Replication Timing Networks: a novel class of gene regulatory networks

DNA replication occurs in a defined temporal order known as the replication-timing (RT) program and is regulated during development, coordinated with 3D genome organization and transcriptional activity. However, transcription and RT are not sufficiently coordinated to predict each other, suggesting an indirect relationship. Here, we exploit genome-wide RT profiles from 15 human cell types and intermediate differentiation stages derived from human embryonic stem cells to construct different types of RT regulatory networks. First, we constructed networks based on the coordinated RT changes during cell fate commitment to create highly complex RT networks composed of thousands of interactions that form specific functional sub-network communities. We also constructed directional regulatory networks based on the order of RT changes within cell lineages and identified master regulators of differentiation pathways. Finally, we explored relationships between RT networks and transcriptional regulatory networks (TRNs), by combining them into more complex circuitries of composite and bipartite networks, revealing novel trans interactions between transcription factors and downstream RT changes that were validated with ChIP-seq data. Our findings suggest a regulatory link between the establishment of cell type specific TRNs and RT control during lineage specification.

systems biology↗

bioRxiv2017

Trans-omic analysis reveals fed and fasting insulin signal across phosphoproteome, transcriptome, and metabolome

The concentration and temporal pattern of insulin selectively regulate multiple cellular functions. To understand how insulin dynamics are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells from three networks--a phosphorylation-dependent cellular functions regulatory network using phosphoproteomic data, a transcriptional regulatory network using phosphoproteomic and transcriptomic data, and a metabolism regulatory network using phosphoproteomic and metabolomic data. With the trans-omic regulatory network, we identified selective regulatory networks that mediate differential responses to insulin. Akt and Erk, hub molecules of insulin signaling, encode information of a wide dynamic range of dose and time of insulin. Down-regulated genes and metabolites in glycolysis had high sensitivity to insulin (fasting insulin signal); up-regulated genes and dicarboxylic acids in the TCA cycle had low sensitivity (fed insulin signal). This integrated analysis enables molecular insight into how cells interpret physiologically fed and fasting insulin signals.\n\nHighlightsO_LIWe constructed a trans-omic network of insulin action using multi-omic data.\nC_LIO_LIThe trans-omic network integrates phosphorylation, transcription, and metabolism.\nC_LIO_LIWe classified signaling, transcriptome, and metabolome by sensitivity to insulin.\nC_LIO_LIWe identified fed and fasting insulin signal flow across the trans-omic network.\nC_LI

systems biology↗