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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Viral replication modes in single-peak fitness landscapes: a dynamical systems analysis

Positive-sense, single-stranded RNA viruses are important pathogens infecting almost all types of organisms. Experimental evidences from mutant distributions and amplification kinetics of viral RNA suggest that these pathogens may follow different RNA replication modes, ranging from the stamping machine replication (SMR) to the geometric replication (GR) modes. Despite previous theoretical works have focused on the evolutionary dynamics of RNA viruses amplifying their genomes with different strategies, few is known in terms of the bifurcations and transitions involving error thresholds (mutation-induced dominance of mutants) and lethal mutagenesis (mutation-induced extinction of all sequences). Here we analyze a dynamical system describing the intracellular amplification of viral RNA genomes evolving on a single-peak fitness landscape focusing on three cases considering neutral, deleterious, and lethal mutants spectra. In our model, the different replication modes are introduced with parameter : with {gtrsim} 0 for the SMR and = 1 for the GR. We analytically derive the critical mutation rates causing lethal mutagenesis and error catastrophe, governed by transcritical bifurcations that depend on parameters , k1 (replicative fitness of mutants), and on the spontaneous degradation rates of the sequences,{epsilon} . For the lethal case the critical mutation rate involving lethal mutagenesis is [Formula]. Here, the SMR involves lower critical mutation rates, being the system more robust to lethal mutagenesis replicating closer to the GR mode. This result is also found for the neutral and deleterious cases, but for these later cases lethal mutagenesis can shift to the error catastrophe once the replication mode surpasses a threshold given by [Formula].

systems biology↗

bioRxiv2017

Transcriptional dysregulation study reveals a core network involving the genesis for Alzheimer’s disease

BackgroundThe pathogenesis of Alzheimers disease is associated with dysregulation at different levels from transcriptome to cellular functioning. Such complexity necessitates investigations of disease etiology to be carried out considering multiple aspects of the disease and the use of independent strategies. The established works more emphasized on the structural organization of gene regulatory network while neglecting the internal regulation changes.\n\nMethodsApplying a strategy different from popularly used co-expression network analysis, this study investigated the transcriptional dysregulations during the transition from normal to disease states.\n\nResults97 genes were predicted as dysregulated genes, which were also associated with clinical outcomes of Alzheimers disease. Both the co-expression and differential co-expression analysis suggested these genes to be interconnected as a core network and that their regulations were strengthened during the transition to disease states. Functional studies suggested the dysregulated genes to be associated with aging and synaptic function. Further, we checked the evolutionary conservation of the gene co-expression and found that human and mouse brain might have divergent transcriptional co-regulation even when they had conserved gene expression profiles.\n\nConclusionOverall, our study reveals a profile of transcriptional dysregulation in the genesis of Alzheimers disease by forming a core network with altered regulation; the core network is associated with Alzheimers diseases by affecting the aging and synaptic functions related genes; the gene regulation in brain may not be conservative between human and mouse.

systems biology↗

bioRxiv2017

Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models

BackgroundMathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly obtain optimal solutions. Further, biochemical models often have many local extrema which further complicates parameter estimation. Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned search (DDS). DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search.\n\nResultsWe tested DOPS using classic optimization test functions, biochemical benchmark problems and real-world biochemical models. We performed [Formula] trials with [Formula] function evaluations per trial, and compared the performance of DOPS with other commonly used meta-heuristics such as differential evolution (DE), simulated annealing (SA) and dynamically dimensioned search (DDS). On average, DOPS outperformed other common meta-heuristics on the optimization test functions, benchmark problems and a real-world model of the human coagulation cascade.\n\nConclusionsDOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations. DOPS source code is available for download under a MIT license at http://www.varnerlab.org.

systems biology↗

bioRxiv2017

The interaction landscape between transcription factors and the nucleosome

Nucleosomes cover most of the genome and are thought to be displaced by transcription factors (TFs) in regions that direct gene expression. However, the modes of interaction between TFs and nucleosomal DNA remain largely unknown. Here, we use nucleosome consecutive affinity-purification systematic evolution of ligands by exponential enrichment (NCAP-SELEX) to systematically explore interactions between the nucleosome and 220 TFs representing diverse structural families. Consistently with earlier observations, we find that the vast majority of TFs have less access to nucleosomal DNA than to free DNA. The motifs recovered from TFs bound to nucleosomal and free DNA are generally similar; however, steric hindrance and scaffolding by the nucleosome result in specific positioning and orientation of the motifs. Many TFs preferentially bind close to the end of nucleosomal DNA, or to periodic positions at its solvent-exposed side. TFs often also bind nucleosomal DNA in a particular orientation, because the nucleosome breaks the local rotational symmetry of DNA. Some TFs also specifically interact with DNA located at the dyad position where only one DNA gyre is wound, whereas other TFs prefer sites spanning two DNA gyres and bind specifically to each of them. Our work reveals striking differences in TF binding to free and nucleosomal DNA, and uncovers a rich interaction landscape between the TFs and the nucleosome.

systems biology↗

bioRxiv2018

Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages

The development of malaria parasites throughout their various life cycle stages is controlled by coordinated changes in gene expression. We previously showed that the three-dimensional organization of the P. falciparum genome is strongly associated with gene expression during its replication cycle inside red blood cells. Here, we analyzed genome organization in the P. falciparum and P. vivax transmission stages. Major changes occurred in the localization and interactions of genes involved in pathogenesis and immune evasion, erythrocyte and liver cell invasion, sexual differentiation and master regulation of gene expression. In addition, we observed reorganization of subtelomeric heterochromatin around genes involved in host cell remodeling. Depletion of heterochromatin protein 1 (PfHP1) resulted in loss of interactions between virulence genes, confirming that PfHP1 is essential for maintenance of the repressive center. Overall, our results suggest that the three-dimensional genome structure is strongly connected with transcriptional activity of specific gene families throughout the life cycle of human malaria parasites.

systems biology↗

bioRxiv2018

Glycolytic repression and reduced GLP-1 secretion by active Farnesoid X Receptor in enteroendocrine L cells achieved via PKLR

Rise in intestinal glucose increases GLP-1 secretion by enteroendocrine L cells. GLP-1, in turn, stimulates insulin secretion. Farnesoid X Receptor (FXR) represses this pathway by manipulating the L cell glycolysis, thus reducing insulin- and GLP-1 secretion. The mechanism by which FXR manipulates the L cell glycolysis is unclear. In this study, we construct an L cell specific protein-protein interactome and identify all significantly active protein complexes, inferred by co-expression scores, in FXR-activated and control L cells. Contrary to previous reports, we find extensive glycolytic enzyme activity in FXR-activated L cells. We present how FXRs repression of the glycolytic enzyme, pyruvate kinase (PKLR), is causing the reduction in glycolytic activity. This mechanistic insight may aid development of drugs targeting the GLP-1 pathway.

systems biology↗

bioRxiv2018

Comparative Network Reconstruction using Mixed Integer Programming

New anti-cancer drugs that specifically target oncogenes involved in signalling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. Addressing this problem requires an understanding of these networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells. Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on incomplete perturbation data, and to identify which edges differ quantitatively between two or more signalling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wildtype MAPK signaling, possibly through an alternative RAF-isoform.

systems biology↗

bioRxiv2018

BOFdat: generating biomass objective function stoichiometric coefficients from experimental data

Genome-scale models (GEMs) rely on a biomass objective function (BOF) to predict phenotype from genotype. Here we present BOFdat, a Python package that offers functions to generate biomass objective function stoichiometric coefficients (BOFsc) from macromolecular cell composition and relative abundances of macromolecules obtained from omic datasets. Growth-associated and non-growth associated maintenance (GAM and NGAM) costs can also be calculated by BOFdat.\n\nBOFdat is freely available on the Python Package Index (pip install BOFdat). The source code and an example usage (Jupyter Notebook and example files) are available on GitHub (https://github.com/jclachance/BOFdat). The documentation and API are available through ReadTheDocs (https://bofdat.readthedocs.io).\n\nContactjean-christophe.lachance@usherbrooke.ca, zaking@eng.ucsd.edu, pierre-etienne.jacques@usherbrooke.ca

systems biology↗

bioRxiv2018

Expression variation and covariation impair analog and enable binary signaling control

Due to noise in the synthesis and degradation of proteins, the concentrations of individual vertebrate signaling proteins were estimated to vary with a coefficient of variation (CV) of approximately 25% between cells. This high variation enables population-level regulation of cell functions but abolishes accurate single-cell signal transmission. Here we measure cell-to-cell variability of relative protein abundance using quantitative proteomics of individual Xenopus laevis eggs and cultured human cells and show that variation is typically much lower, in the range of 5-15%, compatible with accurate single-cell transmission. Furthermore, we show that MEK and ERK expression covary which improves controllability of the fraction of cells that activate bimodal ERK signaling, arguing that covariation has a role in facilitating population-level control of binary cell-fate decisions. Together, our experimental and model data argues for a control principle whereby low covariation limits signaling noise for accurate control analog single-cell signaling. In contrast, increased covariation widens the stimulus-range over which external inputs can regulate binary cell activation, thereby enabling accurate control of the fraction of activated cells at the population level.

systems biology↗

bioRxiv2018

A Portable Structural Analysis Library for Reaction Networks

The topology of a reaction network can have a significant influence on the networks dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loops. These effects are due entirely to mass constraints imposed by the network configuration and are important considerations before any dynamical analysis is made. Most established simulation software tools usually carry out some kind of structural analysis of a network before any attempt is made at dynamic simulation. In this paper we describe a portable software library, libStructural, that can carry out a variety of popular structural analyses that includes conservation analysis, flux dependency analysis and enumerating elementary modes. The library employs robust algorithms that allow it to be used on large networks with more than a two thousand nodes. The library accepts either a raw or fully labeled stoichiometry matrix or models written in SBML format. The software is written in standard C/C++ and comes with documentation and a test suite. The software is available for Windows, Mac OS X, and can be compiled easily on any Linux operating system. A language binding for Python is also available through the pip package manager making it trivial to install on any standard Python distribution. As a second example, we also create a new libStructural plugin for PathwayDesigner that allows solutions to be viewed graphically. The source code is licensed under the open source BSD license and is available on GitHub (https://github.com/sys-bio/Libstructural)

systems biology↗

bioRxiv2018

Neuraminidase inhibitors - is it time to call it a day?

Stockpiling neuraminidase inhibitors (NAIs) such as oseltamivir and zanamivir is part of a global effort to be prepared for an influenza pandemic. However, the contribution of NAIs for treatment and prevention of influenza and its complications is largely debatable. Here, we developed a transparent mathematical modelling setting to analyse the impact of NAIs on influenza disease at within-host and population level. Analytical and simulation results indicate that even assuming unrealistically high efficacies for NAIs, drug intake starting on the onset of symptoms has a negligible effect on an individual's viral load and symptoms score. Increasing NAIs doses does not provide a better outcome as is generally believed. Considering Tamiflu's pandemic regimen for prophylaxis, different multiscale simulation scenarios reveal modest reductions in epidemic size despite high investments in stockpiling. Our results question the use of NAIs in general to treat influenza as well as the respective stockpiling by regulatory authorities.

systems biology↗

bioRxiv2018

Time-scale separation and stochasticity conspire to impact phenotypic dynamics in the canonical and inverted Bacillus subtilis core genetic regulation circuits.

In this work, we study two seemingly unrelated aspects of core genetic nonlinear dynamical control of the competence phenotype in Bacillus subtilis, a common Gram-positive bacterium living in the soil. We focus on hitherto unchartered aspects of the dynamics by exploring the effect of time scale separation between transcription and translation and, as well, the effect of intrinsic molecular stochasticity. We consider these aspects of regulatory control as two possible evolutionary handles. Hence, using theory and computations, we study how the onset of oscillations breaks the excitability-based competence phenotype in two topologically close evolutionary competing circuits: the canonical \"wild-type\" regulation circuit selected by Evolution and, the indirect-feedback inverted circuit that failed to be selected by Evolution, as was shown elsewhere, due to dynamical reasons.

systems biology↗

bioRxiv2018

Noise propagation in an integrated model of bacterial gene expression and growth

In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing > 1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.

systems biology↗

bioRxiv2018

Hierarchical optimization for the efficient parametrization of ODE models

Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems. In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness, and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches.

systems biology↗

bioRxiv2018

Chronic inflammation delays cell migration to villi in the intestinal epithelium

The intestinal epithelium is a single layer of cells which provides the first line of defence of the intestinal mucosa to bacterial infection. Cohesion of this physical barrier is supported by renewal of epithelial stem cells, residing in invaginations called crypts, and by crypt cell migration onto protrusions called villi; dysregulation of such mechanism may render the gut susceptible to chronic inflammation. The impact that excessive or misplaced epithelial cell death may have on villus cell migration is currently unknown. We integrated cell-tracking methods with computational models to determine how epithelial homeostasis is affected by acute and chronic inflammatory cell death. Parameter inference reveals that acute inflammatory cell death has a transient effect on epithelial cell dynamics, whereas cell death caused by chronic inflammation causes a delay in the accumulation of labelled cells onto the villus compared to control. Such a delay may be reproduced by using a cell-based model to simulate the dynamics of each cell in a crypt-villus geometry, showing that a prolonged increase in cell death slows the migration of cells from the crypt to the villus. This investigation highlights which injuries (acute or chronic) may be regenerated and which cause disruption of healthy epithelial homeostasis.

systems biology↗

bioRxiv2018

Reimplementation of the Potjans-Diesmann cortical microcircuit model: from NEST to Brian

1This work targets the replicability of computational models to provide the community with tested and proven open-source models to be used in new studies and implementations. The Potjans-Diesmann model describes a cortical microcircuit containing two cell types (excitatory and inhibitory) distributed in four layers, and represents the cortical network below a surface of 1 mm2. The original implementation of the Potjans-Diesmann model was based on the NEST simulator and our goal here was to re-implement the model in the Brian 2 simulator and obtain the same results presented in the reference article. We did not replicate analyses that involve changes in the network structure. Our replicated network model presents activity dynamic patterns very similar to the ones observed in the original model, with comparisons made in terms of firing rates and synchrony and irregularity measures. In conclusion, the Potjans-Diesmann model was successfully replicated in a different platform than the one in which it was originally implemented.

systems biology↗

bioRxiv2018

Antagonistic regulation with a unique setpoint, integral and double integral action

Several biochemical species are in organisms controlled in a pairwise manner i.e., two different species (e.g., hormone, enzyme, transporter protein) work to control the concentration of a third chemical species. Such pairs are often antagonistic, meaning that one of the controller species acts to increase whereas the other controller species acts to decrease the amount of the controlled species. How antagonistic systems interact to achieve regulation and to avoid competing against each other is not fully understood. An issue is how two antagonistic hormones can agree upon one common setpoint. We present here a new type of antagonistic regulatory system that has a single unique setpoint inherently defined by the system. The regulatory system controls the concentration of a chemical species with both integral and double integral action, achieving tight control. We show by the use of an analytical stability analysis, using the principle of vanishing perturbations, that the setpoint is asymptotically stable. Finally the prospect of treating the presented system as a part of a larger family of antagonistic regulatory systems with unique setpoints, integral and double integral action is discussed.

systems biology↗

bioRxiv2018

Loss of Trem2 in microglia leads to widespread disruption of cell co-expression networks in mouse brain

Rare heterozygous coding variants in the Triggering Receptor Expressed in Myeloid cells 2 (TREM2) gene, conferring increased risk of developing late-onset Alzheimer's disease, have been identified. We examined the transcriptional consequences of the loss of Trem2 in mouse brain to better understand its role in disease using differential expression and coexpression network analysis of Trem2 knockout and wild-type mice. We generated RNA-Seq data from cortex and hippocampus sampled at 4 and 8 months. Using brain cell type markers and ontology enrichment, we found subnetworks with cell type and/or functional identity. We primarily discovered changes in an endothelial-gene enriched subnetwork at 4 months, including a shift towards a more central role for the Amyloid Precursor Protein (App) gene, coupled with widespread disruption of other cell-type subnetworks, including a subnetwork with neuronal identity. We reveal an unexpected potential role of Trem2 in the homeostasis of endothelial cells that goes beyond its known functions as a microglial receptor and signalling hub, suggesting an underlying link between immune response and vascular disease in dementia.

systems biology↗