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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 451 records · Page 25Linked to original sources

bioRxiv2016

Context-specificity in causal signaling networks revealed by phosphoprotein profiling

SummarySignaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks. However, it remains unclear whether signaling networks depend on biological context. Signaling networks encode causal influences - and not just correlations - between network components. Here, using a causal framework and systematic time-course assays of signaling proteins, we investigate the context-specificity of signaling networks in a cell line system. We focus on a well-defined set of signaling proteins profiled in four breast cancer cell lines under eight stimulus conditions and inhibition of specific kinases. The data, spanning multiple pathways and comprising approximately 70,000 phosphoprotein and 260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we validate in independent experiments. Furthermore, the data provide a resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.

Systems Biology↗

bioRxiv2016

Detecting differential growth of microbial populations with Gaussian process regression

Microbial growth curves are used to study differential effects of media, genetics, and stress on microbial population growth. Consequently, many modeling frameworks exist to capture microbial population growth measurements. However, current models are designed to quantify growth under conditions that produce a specific functional form. Extensions to these models are required to quantify the effects of perturbations, which often exhibit non-standard growth curves. Rather than fix expected functional forms of different experimental perturbations, we developed a general and robust model of microbial population growth curves using Gaussian process (GP) regression. GP regression modeling of high resolution time-series growth data enables accurate quantification of population growth, and can be extended to identify differential growth phenotypes due to genetic background or stress. Additionally, confounding effects due to experimental variation can be controlled explicitly. Our framework substantially outperforms commonly used microbial population growth models, particularly when modeling growth data from environmentally stressed populations. We apply the GP growth model to a collection of growth measurements for seven transcription factor knockout strains of a model archaeal organism, Halobacterium salinarum. Using these models fitted to growth data, two statistical tests were developed to quantify the differential effects of genetic and environmental perturbations on microbial growth. These statistical tests accurately identify known regulators and implicate novel regulators of growth under standard and stress conditions. Furthermore, the fitted GP regression models are interpretable, recapitulating biological knowledge of growth response while providing new insights into the relevant parameters affecting microbial population growth.

Systems Biology↗

bioRxiv2016

Systematic morphological profiling of human gene and allele function reveals Hippo-NF-κB pathway connectivity

We hypothesized that human genes and disease-associated alleles might be systematically functionally annotated using morphological profiling of cDNA constructs, via a microscopy-based Cell Painting assay. Indeed, 50% of the 220 tested genes yielded detectable morphological profiles, which grouped into biologically meaningful gene clusters consistent with known functional annotation (e.g., the RAS-RAF-MEK-ERK cascade). We used novel subpopulation-based visualization methods to interpret the morphological changes for specific clusters. This unbiased morphologic map of gene function revealed TRAF2/C-REL negative regulation of YAP 1/WWTR1-responsive pathways. We confirmed this discovery of functional connectivity between the O_SCPCAPNF-C_SCPCAP{kappa}O_SCPCAPBC_SCPCAP pathway and Hippo pathway effectors at the transcriptional level, thereby expanding knowledge of these two signaling pathways that critically regulate tumor initiation and progression. We make the images and raw data publicly available, providing an initial morphological map of major biological pathways for future study.

systems biology↗

bioRxiv2017

Integration of multiple data sources for gene network inference using genetic perturbation data

BackgroundThe inference of gene regulatory networks is of great interest and has various applications. The recent advances in high-throughout biological data collection have facilitated the construction and understanding of gene regulatory networks in many model organisms. However, the inference of gene networks from large-scale human genomic data can be challenging. Generally, it is difficult to identify the correct regulators for each gene in the large search space, given that the high dimensional gene expression data only provides a small number of observations for each gene.\n\nResultsWe present a Bayesian approach integrating external data sources with knockdown data from human cell lines to infer gene regulatory networks. In particular, we assemble multiple data sources including gene expression data, genome-wide binding data, gene ontology, known pathways and use a supervised learning framework to compute prior probabilities of regulatory relationships. We show that our integrated method improves the accuracy of inferred gene networks. We apply our method to two different human cell lines, which illustrates the general scope of our method.\n\nConclusionsWe present a flexible and systematic framework for external data integration that improves the accuracy of human gene network inference while retaining efficiency. Integrating various data sources of biological information also provides a systematic way to build on knowledge from existing literature.

systems biology↗

bioRxiv2018

Fold-change detection of NF-κB at target genes with different transcript outputs

The transcription factor NF-{kappa}B promotes inflammatory and stress-responsive gene transcription across a range of cell types in response to the cytokine tumor necrosis factor- (TNF). Although NF-{kappa}B signaling exhibits significant variability across single cells, some target genes exhibit fold-change detection of NF-{kappa}B, which may buffer against stochastic variation in signaling molecules. However, this observation was made at target genes supporting high levels of TNF-inducible transcription. It is unknown if fold-change detection is maintained at NF-{kappa}B target genes with low levels of TNF-inducible transcription, for which stochastic promoter events may be more pronounced. Here we used a microfluidic cell-trapping device to measure how TNF-induced activation of NF-{kappa}B controls transcription in single Jurkat T cells at the promoters of integrated HIV and the endogenous cytokine gene IL6, which produce only a few transcripts per cell. We tracked TNF-stimulated NF-{kappa}B RelA nuclear translocation by live-cell imaging and then quantified transcript number by RNA FISH in the same cell. We found that TNF-induced transcription correlates with fold change in nuclear NF-{kappa}B with similar strength at low versus high abundance target genes. A computational model of TNF-NF-{kappa}B signaling, which implements fold-change detection from competition for binding to {kappa}B motifs, was sufficient to reproduce fold-change detection across the experimentally measured range of transcript outputs. Nevertheless, we found that gene-specific trends in transcriptional noise and levels of promoter-bound NF-{kappa}B predicted by the model were inconsistent with our experimental observations at low abundance gene targets. Our results reveal a gap in our understanding of RelA-mediated transcription for low abundance transcripts and suggest that cells use additional biological mechanisms to maintain robustness of NF-{kappa}B fold-change detection while tuning transcriptional output.

systems biology↗

bioRxiv2017

Simulating The Impact Of Sensorimotor Deficits On Reaching Performance

The healthy human nervous system accurately and robustly controls movements despite nonlinear dynamics, noise, and delays. After a stroke, motor ability frequently becomes impaired. To provide insight into the relative impact of specific sensorimotor deficits on motor performance, we modeled neural control of reaching with the human upper limb as a near-optimally feedback-controlled two-degree-of-freedom system with biologically based parameters. We added three sensorimotor impairments commonly associated with post-stroke hemiparesis--abnormal joint coupling, increased noise on internally modeled dynamics, and muscular weakness-- and examined the impact on reaching performance. We found that abnormal joint coupling unknown to the systems internal model caused systematic perturbations to trajectories, longer reach durations, and target overshoot. Increasing internal model noise and muscular weakness had little impact on motor performance unless model noise was increased by several orders of magnitude. Many reaches performed by our perturbed models replicate features commonly observed in reaches by hemiparetic stroke survivors. The sensitivity to unmodeled abnormal joint coupling agrees with experimental findings that abnormal coupling (possibly related to internal model errors) is the main cause of post-stroke motor impairment.

neuroscience↗

bioRxiv2017

Multi-scale model of the proteomic and metabolic consequences of reactive oxygen species

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can completely inhibit cell growth when unmanaged and thus elicits an essential stress response that is universal and fundamental in biology. We develop a computable multi-scale description of the ROS stress response in Escherichia coli. We show that this quantitative framework allows for the understanding and prediction of ROS stress responses at three levels: 1) pathways: amino acid auxotrophies, 2) networks: the systemic response to ROS stress, and 3) genetic basis: adaptation to ROS stress during laboratory evolution. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

systems biology↗

bioRxiv2014

Biological Organization, Biological Information, and Knowledge

A concept of information designed to handle information conveyed by organizations is introduced. This concept of information may be used at all biological scales: from molecular and intracellular to multi-cellular organisms and human beings, and further on to collectivities, societies and culture. In this short account, two ground concepts necessary for developing the definition will also be introduced: whole-part graphs, a model for biological organization, and synexions, their immersion into space-time. This definition of information formalizes perception, observers and interpretation; allowing for considering information-exchange as a basic form of biological interaction. Some of its elements will be clarified by arguing and explaining why the immersion of whole-part graphs in (the physical) space-time is needed.

Systems Biology↗

bioRxiv2015

DeDaL: Cytoscape 3.0 app for producing and morphing data-driven and structure-driven network layouts

BackgroundVisualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistical insights into biological functions. Currently, high-throughput data are usually visualized on top of predefined network layouts which are not always adapted to a given data analysis task. We developed a Cytoscape app which allows to construct biological network layouts based on the data from molecular profiles imported as values of nodes attributes.\n\nResultsDeDaL is a Cytoscape 3.0 app which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. Combining these possibilities facilitates creating insightful network layouts representing both structural network features and the correlation patterns in multivariate data.\n\nConclusionsDeDaL is the first method allowing to construct biological network layouts from high-throughput data. DeDaL is freely available for downloading together with step-by-step tutorial at http://bioinfo-out.curie.fr/projects/dedal/.

Systems Biology↗

bioRxiv2018

Noise-induced bistability in the quasineutral coexistence of viral RNA under different replication modes

Evolutionary and dynamical investigations on real viral populations indicate that RNA replication can range between two extremes given by so-called stamping machine replication (SMR) and geometric replication (GR). The impact of asymmetries in replication for single-stranded, (+) sense RNA viruses has been up to now studied with deterministic models. However, viral replication should be better described by including stochasticity, since the cell infection process is typically initiated with a very small number of RNA macromolecules, and thus largely influenced by intrinsic noise. Under appropriate conditions, deterministic theoretical descriptions of viral RNA replication predict a quasineutral coexistence scenario, with a line of fixed points involving different strands equilibrium ratios depending on the initial conditions. Recent research on the quasineutral coexistence in two competing populations reveals that stochastic fluctuations fundamentally alters the mean-field scenario, and one of the two species outcompetes the other one. In this manuscript we study this phenomenon for RNA viral replication modes by means of stochastic simulations and a diffusion approximation. Our results reveal that noise has a strong impact on the amplification of viral RNA, also causing the emergence of noise-induced bistability. We provide analytical criteria for the dominance of (+) sense strands depending on the initial populations on the line of equilibria, which are in agreement with direct stochastic simulation results. The biological implications of this noise-driven mechanism are discussed within the framework of the evolutionary dynamics of RNA viruses with different modes of replication.

systems biology↗

bioRxiv2018

Controlling false discoveries in Bayesian gene networks with lasso regression p-values

MotivationBayesian networks can represent directed gene regulations and therefore are favored over co-expression networks. However, hardly any Bayesian network study concerns the false discovery control (FDC) of network edges, leading to low accuracies due to systematic biases from inconsistent false discovery levels in the same study.\n\nResultsWe design four empirical tests to examine the FDC of Bayesian networks from three p-value based lasso regression variable selections -- two existing and one we originate. Our method, lassopv, computes p-values for the critical regularization strength at which a predictor starts to contribute to lasso regression. Using null and Geuvadis datasets, we find that lassopv obtains optimal FDC in Bayesian gene networks, whilst existing methods have defective p-values. The FDC concept and tests extend to most network inference scenarios and will guide the design and improvement of new and existing methods. Our novel variable selection method with lasso regression also allows FDC on other datasets and questions, even beyond network inference and computational biology.\n\nAvailabilityLassopv is implemented in R and freely available at https://github.com/lingfeiwang/lassopv and https://cran.r-project.org/package=lassopv.\n\nContactLingfei.Wang@roslin.ed.ac.uk\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2018

A comparative analysis of label-free liquid chromatography-mass spectrometry liver proteomic profiles highlights metabolic differences between pig breeds

Liver is a complex organ governing several physiological processes that define biological mechanisms affecting growth, feed efficiency and performance traits in all livestock species, including the pig. Proteomics may contribute to better understand the relationship between liver functions and complex production traits in pigs and to characterize this specie as biomedical model. This study applied, for the first time, a label-free liquid chromatography-mass spectrometry (LC-MS) proteomic approach to compare the liver proteome profiles of two important heavy pig breeds, Italian Duroc (IDU) and Italian Large White (ILW). Liver specimens were collected (after slaughtering) from performance tested pigs of these two breeds, raised in standard conditions. The label-free LC-MS method captured a total of 501 proteins of which 200 were subsequently considered in the between breeds comparison. A statistical pipeline based on the sparse Partial Least Squares Discriminant Analysis (sPLS-DA), coupled with stability and significance tests, was applied for the identification of up or down regulated proteins between breeds. Analyses revealed a total of 25 proteins clearly separating IDU and ILW pigs. Among the top proteins differentiating the two breeds ACAA2 and CES3 were up-regulated in ILW and HIST2H2BF and KHK were up-regulated in IDU. FASN, involved in fatty acid metabolism and encoded by a gene located in a QTL region for fatty acid composition, was up-regulated in ILW. Protein interaction analysis showed that 16 of these proteins were connected in one big module. Functional analyses indicated that differentially expressed proteins were involved in several biological processes related to the metabolism of lipids, amino-acids, carbohydrates, cofactors and antibiotics/drugs, supporting that these functions might distinguish IDU and ILW pigs. This comparative proteomic analysis of the porcine liver highlighted several biological factors that could determine the peculiar production potentials of these two heavy pig breeds, derived by their different genetic backgrounds.

systems biology↗

bioRxiv2015

Sequence-based model of gap gene regulatory network

BackgroundThe detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency.\n\nResultsWe developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and the set, from which the site of interest was left out.\n\nConclusionsThe reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3) functional important sites are not exclusively located in cis-regulatory elements, but are rather dispersed through regulatory region. It is of importance that some of the sites with high functional impact in hb, Kr and kni regulatory regions coincide with strong sites annotated and verified in Dnase I footprint assays.

Systems Biology↗

bioRxiv2016

Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks

Summary/AbstractSpatial Analysis of Functional Enrichment (SAFE) is a systematic quantitative approach for annotating large biological networks. SAFE detects network regions that are statistically overrepresented for functional groups or quantitative phenotypes of interest, and provides an intuitive visual representation of their relative positioning within the network. In doing so, SAFE determines which functions are represented in a network, which parts of the network they are associated with and how they are potentially related to one another.\n\nHere, I provide a detailed stepwise description of how to perform a SAFE analysis. As an example, I use SAFE to annotate the genome-scale genetic interaction similarity network from Saccharomyces cerevisiae with Gene Ontology (GO) biological process terms. In addition, I show how integrating GO with chemical genomic data in SAFE can recapitulate known modes-of-action of chemical compounds and potentially identify novel drug mechanisms.

systems biology↗

bioRxiv2017

Large-scale interpretation of chemical-genetic interaction profiles using a genetic interaction network

Chemical-genetic interactions - observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes - contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.

systems biology↗

bioRxiv2017

Composite measurements and molecular compressed sensing for highly efficient transcriptomics

RNA profiling is an excellent phenotype of cellular responses and tissue states, but can be costly to generate at the massive scale required for studies of regulatory circuits, genetic states or perturbation screens. Here, we draw on a series of advances over the last decade in the field of mathematics to establish a rigorous link between biological structure, data compressibility, and efficient data acquisition. We propose that very few random composite measurements - in which gene abundances are combined in a random linear combination - are needed to approximate the high-dimensional similarity between any pair of gene abundance profiles. We then show how finding latent, sparse representations of gene expression data would enable us to \"decompress\" a small number of random composite measurements and recover high-dimensional gene expression levels that were not measured (unobserved). We present a new algorithm for finding sparse, modular structure, which improves the ability to interpret samples in terms of small numbers of active modules, and show that the modular structure we find is sufficient to recover gene expression profiles from composite measurements (with ~100-fold fewer composite measurements than genes). Moreover, the knowledge that sparse, modular structures exist allows us to recover expression profiles from composite measurements, even without access to any training data. Finally, we present a proof-of-concept experiment for making composite measurements in the laboratory, involving the measurement of linear combinations of RNA abundances. Altogether, our results suggest new compressive modalities in experimental biology that can form a foundation for massive scaling in high-throughput measurements, while also offering new insights into the interpretation of high-dimensional data.

systems biology↗

bioRxiv2017

Stability of subsystem solutions in agent-based models

The fact that relatively simple entities, such as particles or neurons, or even ants or bees or humans, give rise to fascinatingly complex behavior when interacting in large numbers is the hallmark of complex systems science. Agent-based models are frequently employed for modeling and obtaining a predictive understanding of complex systems. Since the sheer number of equations that describe the behavior of an entire agent-based model often makes it impossible to solve such models exactly, Monte Carlo simulation methods must be used for the analysis. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among agents that describe systems in biology, sociology or the humanities often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. This begets the question: When can we be certain that an observed simulation outcome of an agent-based model is actually stable and valid in the large system-size limit? The latter is key for the correct determination of phase transitions between different stable solutions, and for the understanding of the underlying microscopic processes that led to these phase transitions. We show that a satisfactory answer can only be obtained by means of a complete stability analysis of subsystem solutions. A subsystem solution can be formed by any subset of all possible agent states. The winner between two subsystem solutions can be determined by the average moving direction of the invasion front that separates them, yet it is crucial that the competing subsystem solutions are characterized by a proper composition and spatiotemporal structure before the competition starts. We use the spatial public goods game with diverse tolerance as an example, but the approach has relevance for a wide variety of agent-based models.

evolutionary biology↗

bioRxiv2018

The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs

The relation between design principles of signaling network motifs and their robustness against intrinsic noise still remains illusive. In this work we investigate the role of cascading for coping with intrinsic noise due to stochasticity in molecular reactions. We use stochastic approaches to quantify fluctuations in the terminal kinase of phosphorylation-dephosphorylation cascade motifs and demonstrate that cascading highly affects these fluctuations. We show that this purely stochastic effect can be explained by time-varying sequestration of upstream kinase molecules. In particular, we discuss conditions on time scales and parameter regimes which lead to a reduction of output fluctuations. Our results are put into biological context by adapting rate parameters of our modeling approach to biologically feasible ranges for general binding-unbinding and phosphorylation-dephosphorylation mechanisms. Overall, this study reveals a novel role of stochastic sequestration for dynamic noise filtering in signaling cascade motifs.

systems biology↗