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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

ATP homeostasis underlies optimal glucose consumption by Saccharomyces cerevisiae

Glycolysis is central to energy metabolism in most organisms, and is highly regulated to enable optimal growth. In the yeast Saccharomyces cerevisiae, feedback mechanisms that control flux through glycolysis span transcriptional control to metabolite levels in the cell. Using a cellobiose consumption pathway, we decoupled glucose sensing from carbon utilization, revealing new modular layers of control that induce ATP consumption to drive rapid carbon fermentation. Alterations of the beta subunit of phosphofructokinase (PFK2), H+-plasma membrane ATPase (PMA1), and glucose sensors (SNF3, RGT2) revealed the importance of coupling extracellular glucose sensing to manage ATP levels in the cell. Controlling the upper bound of cellular ATP levels may be a general mechanism used to regulate energy levels in cells, via a regulatory network that can be uncoupled from ATP concentrations under perceived starvation conditions.\n\nImportanceLiving cells are fine-tuned through evolution to thrive in their native environments. Genome alterations to create organisms for specific biotechnological applications may result in unexpected and undesired phenotypes. We used a minimal synthetic biological system in the yeast Saccharomyces cerevisiae as a platform to reveal novel connections between carbon sensing, starvation conditions and energy homeostasis.

Systems Biology↗

bioRxiv2017

High-throughput, image-based screening of genetic variant libraries

Image-based, high-throughput, high-content screening of pooled libraries of genetic perturbations will greatly advance our understanding biological systems and facilitate many biotechnology applications. Here we introduce a high-throughput screening method that allows highly diverse genotypes and the corresponding phenotypes to be imaged in numerous individual cells. To facilitate genotyping by imaging, barcoded genetic variants are introduced into the cells, each cell carrying a single genetic variant connected to a unique, nucleic-acid barcode. To identify the genotype-phenotype correspondence, we perform live-cell imaging to determine the phenotype of each cell, and massively multiplexed FISH imaging to measure the barcode expressed in the same cell. We demonstrated the utility of this approach by screening for brighter and more photostable variants of the fluorescent protein YFAST. We imaged 20 million cells expressing ~60,000 YFAST mutants and identified novel YFAST variants that are substantially brighter and/or more photostable than the wild-type protein.

systems biology↗

bioRxiv2017

Proteomics of phosphorylation and protein dynamics during fertilization and meiotic exit in the Xenopus egg

Fertilization triggers release from meiotic arrest and initiates events that prepare for the ensuing developmental program. Protein degradation and phosphorylation are known to regulate protein activity during this process. However, the full extent of protein loss and phospho-regulation is still unknown. We examined absolute protein and phospho-site dynamics after fertilization by mass spectrometry-based proteomics. To do this, we developed a new approach for calculating the stoichiometry of phospho-sites from multiplexed proteomics that is compatible with dynamic, stable and multi-site phosphorylation. Overall, the data suggest that degradation is limited to a few low abundance proteins. However, this degradation promotes extensive dephosphorylation that occurs over a wide range of abundances during meiotic exit. We also show that eggs release a large amount of protein into the medium just after fertilization, most likely related to the blocks to polyspermy. Concomitantly, there is a substantial increase in phosphorylation likely tied to calcium activated kinases. We identify putative degradation targets as well as new components of the block to polyspermy. The analytical approaches demonstrated here are broadly applicable to studies of dynamic biological systems.\n\nSignificance StatementProtein phosphorylation and degradation drive critical events in early embryogenesis and the cell cycle; however, comprehensive and accurate analysis of these changes is currently difficult. Using a mass spectrometric approach, we present a quantitative view of the protein and posttranslational economy of the fertilization response in the frog egg. Protein degradation affects a small but very important class of proteins, while regulatory phosphorylation and protein release occur on a far larger scale. We have developed new, broadly applicable analytical methods for phosphorylation that provide absolute quantification with confidence intervals for improved interpretability of post-translational modification analysis.

systems biology↗

bioRxiv2017

Impact of division rate and cell size on gene expression noise

The cell division rate, size, and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli d other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persistors cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding of circuits robustness across growth conditions is key for the effective design of synthetic biological systems.

systems biology↗

bioRxiv2017

Learning the pattern of epistasis linking genotype and phenotype in a protein

Understanding the pattern of epistasis - the non-independence of mutations - is critical for relating genotype and phenotype in biological systems. However, the complexity of potential epistatic interactions has limited approaches to this problem at any level. To develop practical strategies, we carried out a comprehensive experimental study of epistasis between all mutations that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein. The data demonstrate significant high-order epistatic interactions between mutations, but also reveals extraordinary sparsity, enabling novel experimental strategies and sequence-based statistical methods for learning the relevant epistasis. The sequence space linking the parental fluorescent proteins is functionally connected through paths of single mutations; thus, high-order epistasis in proteins is consistent with evolution through stepwise variation and selection. This work initiates a path towards characterizing epistasis in proteins in general.

systems biology↗

bioRxiv2017

Network modelling unravels mechanisms of crosstalk between ethylene and salicylate signalling in potato

To provide means for novel crop breeding strategies, it is crucial to understand the mechanisms underlying the interaction between plants and their pathogens. Network modelling represents a powerful tool that can unravel properties of complex biological systems. Here, we build on a reliable Arabidopsis (Arabidopsis thaliana L.) immune signalling model, extending it with the information from diverse publically available resources. The resulting prior knowledge network (20,012 nodes, 70,091 connections) was then translated to potato (Solanum tuberosum L.) and superimposed with an ensemble network inferred from potato time-resolved transcriptomics data. We used different network modelling approaches to generate specific hypotheses of potato immune signalling mechanisms. An interesting finding was the identification of a string of molecular events, illuminating the ethylene pathway modulation of the salicylic acid pathway through NPR1 gene expression. Functional validations confirmed this modulation, thus confirming the potential of our integrative network modelling approach for unravelling molecular mechanisms in complex systems.\n\nOne-sentence summaryAnalysis of integrated prior knowledge and ensemble networks highlights a novel connection between ethylene and salicylic acid signalling modules in potato.

systems biology↗

bioRxiv2018

Complexity-Aware Simple Modeling

Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach -- complexity-aware simple modeling -- that can bridge the gap between the small- and large-scale approaches.\n\nHighlightsO_LISimple or small-scale models allow deduction of fundamental principles of biological systems\nC_LIO_LIDetailed or large-scale models can be quantitatively accurate but difficult to analyze\nC_LIO_LIComplexity-aware simple models can extract principles that are robust to the presence of unknown complex interactions\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC=\"FIGDIR/small/248419_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@1cc6c8borg.highwire.dtl.DTLVardef@1ea1eb3org.highwire.dtl.DTLVardef@185a0b4org.highwire.dtl.DTLVardef@1f9c6b9_HPS_FORMAT_FIGEXP M_FIG Graphical abstract\n\nC_FIG

systems biology↗

bioRxiv2018

Deciphering noise amplification and reduction in open chemical reaction networks

The impact of fluctuations on the dynamical behavior of complex biological systems is a longstanding issue, whose understanding would elucidate how evolutionary pressure tends to modulate intrinsic noise. Using the It[o] stochastic differential equation formalism, we performed analytic and numerical analyses of model systems containing different molecular species in contact with the environment and interacting with each other through mass-action kinetics. For networks of zero deficiency, which admit a detailed- or complex-balanced steady state, all molecular species are uncorrelated and their Fano factors are Poissonian. Systems of higher deficiency have non-equilibrium steady states and non-zero reaction fluxes flowing between the complexes. When they model homooligomerization, the noise on each species is reduced when the flux flows from the oligomers of lowest to highest degree, and amplified otherwise. In the case of hetero-oligomerization systems, only the noise on the highest-degree species shows this behavior.

systems biology↗

bioRxiv2018

A bacterial growth law out of steady-state

Bacterial growth depends on numerous reactions, and yet follows surprisingly simple laws that inspired biologists for decades. Growth laws until now primarily dealt with steady-state exponential growth in constant conditions. However, bacteria in nature often face fluctuating environments, with nutritional upshifts and downshifts. We therefore ask whether there are growth laws that apply to changing environments. We derive a law for strong upshifts using an optimal resource-allocation model that was previously calibrated at steady-state growth: the post-shift growth rate equals the geometrical mean of the pre-shift growth rate and the growth rate on saturating carbon. We test this using chemostat and robotic batch culture experiments, as well as previous data from several species, and find good agreement with the model predictions. The increase in growth rate after an upshift indicates that ribosomes have spare capacity. We demonstrate theoretically that spare ribosomal capacity has the cost of slow steady-state growth, but is beneficial in fluctuating environments because it prevents large overshoots in intracellular metabolites after an upshift and allows rapid response to change. We also provide predictions for downshifts for future experimental tests. Spare capacity appears in diverse biological systems, and the present study quantifies the optimal degree of spare capacity, which rises the slower the growth rate, and suggests that it can be precisely regulated.

systems biology↗

bioRxiv2018

PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

Due to the complexity of biological systems, their heterogeneity, and the internal regulation of each cell and its surrounding, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful to understand such complex systems. However, very few of these tools, freely available and computationally efficient, are currently available. To fill this gap, we present here our open-source software, PhysiBoSS, which is built on two available software packages that focus on different scales: intracellular signalling using continuous-time markovian Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell).\n\nThe multi-scale feature of PhysiBoSS - its agent-based structure and the possibility to integrate any Boolean network to it - provide a flexible and computationally efficient framework to study heterogeneous cell population growth in diverse experimental set-ups. This tool allows one to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from genotype to phenotype. PhysiBoSS thus becomes very useful when studying population response to treatment, mutations effects, cell modes of invasion or isomorphic morphogenesis events.\n\nTo illustrate potential use of PhysiBoSS, we studied heterogeneous cell fate decisions in response to TNF treatment in a 2-D cell population and in a tumour cell 3-D spheroid. We explored the effect of different treatment regimes and the behaviour and selection of several resistant mutants. We highlighted the importance of spatial information on the population dynamics by considering the effect of competition for resources like oxygen. PhysiBoSS is freely available on GitHub (https://github.com/gletort/PhysiBoSS), and is distributed open source under the BSD 3-clause license. It is compatible with most Unix systems, and a Docker package (https://hub.docker.com/r/gletort/physiboss/) is provided to ease its deployment in other systems.

systems biology↗

bioRxiv2018

MoonFit, a minimal interface for fitting ODE dynamical models, bridging simulation by experimentalists and customization by C++ programmers

The modelling of biological systems often consists into differential equation models that need to be fitted to experimental data. During this complex process, the practical experience of the biologist and the theoretical abstraction of the modeller require back-and-forth refinements of the model, design of new experiments and inclusion of more data-points into the fitting procedure. Available optimization interfaces rarely simultaneously allow customizations by the programmer and the capacity for the biologist to perform simulations or optimizations with a simple interface.\n\nHere, we provide the C++ code of a graphical user interface based on a user defined minimal C++ ODE model class. The graphical interface allows to perform simulations and optimizations without any knowledge in programming. The code was designed minimal and modular to be easily modified, with maximal freedom to link customized optimization libraries, solver or hand-made scripts. Moonfit is powerful enough to fit and compare models with high dimensionality, multiple datasets, to automatize optimizations, and to perform iterative fittings using data interpolation. We believe this will ease the interaction between modellers and experimental partners.\n\nAvailability: Moonfit is freely available via c++ source code and accompanying scripts from gitlab.com/Moonfit/MoonLight.

systems biology↗

bioRxiv2018

Multilayer view of pathogenic SNVs in human interactome through in-silico edgetic profiling

Non-synonymous mutations linked to the complex diseases often have a global impact on a biological system, affecting large biomolecular networks and pathways. However, the magnitude of the mutation-driven effects on the macromolecular network is yet to be fully explored. In this work, we present an systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring, \"edgetic\", effects on the human interactome. Our in-silico analysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. We show that pathogenic SNVs are more likely to cause gene pleiotropy than pathogenic frameshift mutations and are enriched on the protein interaction interfaces. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction-disrupting mutations in type 2 diabetes mellitus, and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the patient-specific genetic variation. The current and future in-silico edgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations.

systems biology↗

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↗