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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Dynamic modeling of Streptococcus pneumoniae competence provides regulatory mechanistic insights

In the human pathogen Streptococcus pneumoniae, the gene regulatory circuit leading to the transient state of competence for natural transformation is based on production of an auto-inducer that activates a positive feedback loop. About one hundred genes are activated in two successive waves linked by a central alternative sigma factor ComX. This mechanism appears to be fundamental to the biological fitness of S. pneumoniae. We have developed a knowledge-based model of the competence cycle that describes average cell behavior. It reveals that the expression rates of the two competence operon, comAB and comCDE, involved in the positive feedback loop must be coordinated to elicit spontaneous competence. Simulations revealed the requirement for an unknown late com gene product that shuts of competence by impairing ComX activity. Further simulations led to the predictions that the membrane protein ComD bound to CSP reacts directly to pH change of the medium and that blindness to CSP during the post-competence phase is controlled by late DprA protein. Both predictions were confirmed experimentally.

systems biology↗

bioRxiv2017

Defining the relevant combinatorial space of the PKC/CARD-CC signal transduction nodes

Biological signal transduction typically display a so-called bow-tie or hour glass topology: Multiple receptors lead to multiple cellular responses but the signals all pass through a narrow waist of central signaling nodes. One such critical signaling node for several inflammatory and oncogenic signaling pathways in humans are the CARD-CC / Bcl10 / MALT1 (CBM) complexes, which get activated by upstream protein kinase C (PKC). In humans, there are four phylogenetically distinct CARD-CC family (CARD9, -10, -11 and -14) proteins and 9 true PKC isozymes ( to {iota}). At this moment, less than a handful of PKC/CARD-CC relationships are known from experimental evidence. In order to explore the biologically relevant combinatorial space out of all 36 potential permutations in this two-component signaling event, we made use of CRISPR/Cas9 genome-edited HEK293T cells to mutate CARD10 for subsequent pairwise cotransfections of all CARD-CC family members and activated mutants of all true PKCs. By quantitative reporter gene expression readout, we could define specific strong and weak PKC/CARD-CC relationships. Surprisingly as many as 21 PKC/CARD-CC combinations were found to have synergistic effects. We also discovered heterodimerization between different CARD-CC proteins, and that this can influence their PKC response profile. This information will be valuable for future studies of novel signaling pathways dependent on the CBM complex signaling nodes.

systems biology↗

bioRxiv2017

Semblance Of Heterogeneity In Collective Cell Migration

Cell population heterogeneity is increasingly a focus of inquiry in biological research. For example, cell migration studies have investigated the heterogeneity of invasiveness and taxis in development, wound healing, and cancer. However, relatively little effort has been devoted to explore when heterogeneity is mechanistically relevant and how to reliably measure it. Statistical methods from the animal movement literature offer the potential to analyse heterogeneity in collections of cell tracking data. A popular measure of heterogeneity, which we use here as an example, is the distribution of delays in directional cross-correlation. Using a suitably generic, yet minimal, model of collective cell movement in three dimensions, we show how using such measures to quantify heterogeneity in tracking data can result in the inference of heterogeneity where there is none. Our study highlights a potential pitfall in the statistical analysis of cell population heterogeneity, and we argue this can be mitigated by the appropriate choice of null models.\n\nHighlightsO_LIgroups of identical cells appear heterogeneous due to limited sampling and experimental repeatability\nC_LIO_LIheterogeneity bias increases with attraction/repulsion between cells\nC_LIO_LImovement in confined environments decreases apparent heterogeneity\nC_LIO_LIhypothetical applications in neural crest and in vitro cancer systems\nC_LI\n\nIn BriefWe use a mathematical model to show how cell populations can appear heterogeneous in their migratory characteristics, even though they are made up of identically-behaving individual cells. This has important consequences for the study of collective cell migration in areas such as embryo development or cancer invasion.

systems biology↗

bioRxiv2015

Comprehensive mapping of mammalian transcriptomes identifies conserved genes associated with different cell differentiation states

Cell identity (or cell state) is established via gene expression programs, represented by \"associated genes\" with dynamic expression across cell identities. Here we integrate RNA-seq data from 40 tissues and cell types from human, chimpanzee, bonobo, and mouse to investigate the conservation and differentiation of cell states. We employ a statistical tool, \"Transcriptome Overlap Measure\" (TROM) to first identify cell-state-associated genes, both protein-coding and non-coding. Next, we use TROM to comprehensively map the cell states within each species and also between species based on the cell-state-associated genes. The within-species mapping measures which cell states are similar to each other, allowing us to construct a human cell differentiation tree that recovers both known and novel lineage relationships between cell states. Moreover, the between-species mapping summarizes the conservation of cell states across the four species. Based on these results, we identify conserved associated genes for different cell states and annotate their biological functions. Interestingly, we find that neural and testis tissues exhibit distinct evolutionary signatures in which neural tissues are much less enriched in conserved associated genes than testis. In addition, our mapping demonstrate that besides protein-coding genes, long non-coding RNAs serve well as associated genes to indicate cell states. We further infer the biological functions of those non-coding associated genes based on their co-expressed protein-coding associated genes. Overall, we provide a catalog of conserved and species-specific associated genes that identifies candidates for downstream experimental studies of the roles of these candidates in controlling cell identity.\n\nHighlightsO_LIComprehensive transcriptome mapping of cell states across four mammalian species\nC_LIO_LIBoth protein-coding genes and long non-coding RNAs serve as good markers of cell identity\nC_LIO_LIDistinct evolutionary signatures of neural and testis tissues\nC_LIO_LIA catalog of conserved associated protein-coding genes and lncRNAs in different mammalian tissues and cell types\nC_LI

Systems Biology↗

bioRxiv2018

Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis

MotivationParameter estimation methods for ordinary differential equation (ODE) models of biological processes can exploit gradients and Hessians of objective functions to achieve convergence and computational efficiency. However, the computational complexity of established methods to evaluate the Hessian scales linearly with the number of state variables and quadratically with the number of parameters. This limits their application to low-dimensional problems.\n\nResultsWe introduce second order adjoint sensitivity analysis for the computation of Hessians and a hybrid optimization-integration based approach for profile likelihood computation. Second order adjoint sensitivity analysis scales linearly with the number of parameters and state variables. The Hessians are effectively exploited by the proposed profile likelihood computation approach. We evaluate our approaches on published biological models with real measurement data. Our study reveals an improved computational efficiency and robustness of optimization compared to established approaches, when using Hessians computed with adjoint sensitivity analysis. The hybrid computation method was more than two-fold faster than the best competitor. Thus, the proposed methods and implemented algorithms allow for the improvement of parameter estimation for medium and large scale ODE models.\n\nAvailabilityThe algorithms for second order adjoint sensitivity analysis are implemented in the Advance MATLAB Interface CVODES and IDAS (AMICI, https://github.com/ICB-DCM/AMICI/). The algorithm for hybrid profile likelihood computation is implemented in the parameter estimation toolbox (PESTO, https://github.com/ICB-DCM/PESTO/). Both toolboxes are freely available under the BSD license.\n\nContactjan.hasenauer@helmholtz-muenchen.de\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2016

Fundamental principles governing sporulation efficiency: A network theory approach

Integrating network theory approaches over longitudinal genome-wide gene expression data is a robust approach to understand the molecular underpinnings of a dynamic biological process. Here, we performed a network-based investigation of longitudinal gene expression changes during sporulation of a yeast strain, SK1. Using global network attributes, viz. clustering coefficient, degree distribution of a node, degree-degree mixing of the connected nodes and disassortativity, we observed dynamic changes in these parameters indicating a highly connected network with inter-module crosstalk. Analysis of local attributes, such as clustering coefficient, hierarchy, betweenness centrality and Granovetters weak ties showed that there was an inherent hierarchy under regulatory control that was determined by specific nodes. Biological annotation of these nodes indicated the role of specifically linked pairs of genes in meiosis. These genes act as crucial regulators of sporulation in the highly sporulating SK1 strain. An independent analysis of these network properties in a less efficient sporulating strain helped to understand the heterogeneity of network profiles. We show that comparison of network properties has the potential to identify candidate nodes contributing to the phenotypic diversity of developmental processes in natural populations. Therefore, studying these network parameters as described in this work for dynamic developmental processes, such as sporulation in yeast and eventually in disease progression in humans, can help in identifying candidate factors which are potential regulators of differences between normal and perturbed processes and can be causal targets for intervention.

Systems Biology↗

bioRxiv2017

Modeling heterogeneous populations using Boolean networks

Certain biological processes such as cancer development and immune activation are controlled by rare cellular events that are difficult to capture computationally through simulations of individual cells. Here we show that when cellular states are described using a Boolean network model, one can exactly simulate the dynamics of non-interacting, highly heterogeneous populations directly, without having to model the various subpopulations. This strategy captures even the rarest outcomes of the model with no sampling error. Our method can incorporate heterogeneity in both cell state and, by augmenting the model, the underlying rules of the network as well (i.e. mutations). We demonstrate our method by using it to simulate a heterogeneous population of Boolean networks modeling the T-cell receptor, spanning ~ 1020 distinct cellular states and mutational profiles.

systems biology↗

bioRxiv2017

Spatial modeling of the membrane-cytosolic interface in protein kinase signal transduction

The spatial architecture of signaling pathways and the inter-action with cell size and morphology are complex but little understood. With the advances of single cell imaging and single cell biology it becomes crucial to understand intracel-lular processes in time and space. Activation of cell surface receptors often triggers a signaling cascade including the activation of membrane-attached and cytosolic signaling components, which eventually transmit the signal to the cell nucleus. Signaling proteins can form steep gradients in the cytosol, which cause strong cell size dependence. We show that the kinetics at the membrane-cytosolic interface and the ratio of cell membrane area to the enclosed cytosolic volume change the behavior of signaling cascades significantly. We present a mathematical analysis of signal transduction in time and space by providing analytical solutions for different spatial arrangements of linear signaling cascades. These investigations are complemented by numerical simulations of non-linear cascades and asymmetric cell shapes.

systems biology↗

bioRxiv2018

Algebraic Shortcuts for Leave-One-Out Cross-Validation in Supervised Network Inference

MotivationSupervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using the model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.\n\nResultsWe present a series of leave-one-out cross-validation shortcuts to rapidly estimate the performance of state-of-the-art kernel-based network inference techniques.\n\nAvailabilityThe machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package.

systems biology↗

bioRxiv2016

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced REgulation And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAMs enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.\n\nAuthor SummaryThe integration of gene regulatory and metabolic network models is an important goal in computational biology, in order to develop methods that can identify the underlying mechanistic links in biological networks and advance metabolic engineering techniques. In this paper, we develop a framework called Integrated Deduced REgulation And Metabolism (IDREAM) that can improve our ability to predict phenotypes of microorganisms, and particularly it can address the challenges in evaluating phenotypic consequence of perturbing transcriptional regulation of metabolism in a eukaryotic cell. We compare the predictive performance of an IDREAM S. cerevisiae model with a PROM model using a TRN available from the YEASTRACT database. IDREAM outperforms PROM using any of three popular yeast metabolic models and across three experimental growth conditions, making it possible to identify subtle synthetic growth defects, and a new role for Oaf1 in the regulation of acetyl-CoA biosynthesis.

systems biology↗

bioRxiv2018

NECorr, a Tool to Rank Gene Importance in Biological Processes using Molecular Networks and Transcriptome Data

The challenge of increasing crop yield while decreasing plants susceptibility to various stresses can be lessened by understanding plant regulatory processes in a tissue-specific manner. Molecular network analysis techniques were developed to aid in understanding gene inter-regulation. However, few tools for molecular network mining are designed to extract the most relevant genes to act upon. In order to find and to rank these putative regulator genes, we generated NECorr, a computational pipeline based on multiple-criteria decision-making algorithms. With the objective of ranking genes and their interactions in a selected condition or tissue, NECorr uses the molecular network topology as well as global gene expression analysis to find hub genes and their condition-specific regulators. NECorr was applied to Arabidopsis thaliana flower tissue and identifies known regulators in the developmental processes of this tissue as well as new putative regulators. NECorr will accelerate translational research by ranking candidate genes within a molecular network of interest.

systems biology↗

bioRxiv2016

A Novel Algorithm for the Maximal Fit Problem in Boolean Networks

Gene regulatory networks (GRNs) are increasingly used for explaining biological processes with complex transcriptional regulation. A GRN links the expression levels of a set of genes via regulatory controls that gene products exert on one another. Boolean networks are a common modeling choice since they balance between detail and ease of analysis. However, even for Boolean networks the problem of fitting a given network model to an expression dataset is NP-Complete. Previous methods have addressed this issue heuristically or by focusing on acyclic networks and specific classes of regulation functions. In this paper we introduce a novel algorithm for this problem that makes use of sampling in order to handle large datasets. Our algorithm can handle time series data for any network type and steady state data for acyclic networks. Using in-silico time series data we demonstrate good performance on large datasets with a significant level of noise.

Systems Biology↗

bioRxiv2017

Molecular characterization of breast cancer cell response to metabolic drugs

Metabolic reprogramming is a hallmark of cancer. We and other authors have previously shown that breast cancer subtypes present metabolism differences. In this study, breast cancer cell lines were treated with metformin and rapamycin. The response was heterogeneous across various breast cancer cells, leading to cell cycle disruption in specific conditions. The molecular effects of these treatments were characterized using sublethal doses, SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes, providing insights into cell responses to metabolism drugs. Moreover, a flux balance analysis approach using protein expression values was applied, showing that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these various approaches provide complementary information, which can be used to suggest hypotheses about the drugs mechanisms of action and the response to drugs that target metabolism.

systems biology↗

bioRxiv2018

Extrinsic Noise Suppression in Micro RNA Mediated Incoherent Feedforward Loops

MicroRNA mediated incoherent feed forward loops (IFFLs) are recurrent network motifs in mammalian cells and have been a topic of study for their noise rejection and buffering properties. Previous work showed that IFFLs can adapt to varying promoter activity and are less prone to noise than similar circuits without the feed forward loop. Furthermore, it has been shown that microRNAs are better at rejecting extrinsic noise than intrinsic noise. This work studies the biological mechanisms that lead to extrinsic noise rejection for microRNA mediated feed forward network motifs. Specifically, we compare the effects of microRNA-induced mRNA degradation and translational inhibition on extrinsic noise rejection, and identify the parameter regimes where noise is most efficiently rejected. In the case of static extrinsic noise, we find that translational inhibition can expand the regime of extrinsic noise rejection. We then analyze rejection of dynamic extrinsic noise in the case of a single-gene feed forward loop (sgFFL), a special case of the IFFL motif where the microRNA and target mRNA are co-expressed. For this special case, we demonstrate that depending on the time-scale of fluctuations in the extrinsic variable compared to the mRNA and microRNA decay rates, the feed forward loop can both buffer or amplify fluctuations in gene product copy numbers.

systems biology↗

bioRxiv2016

Knowledge Formalization and High-Throughput Data Visualization Using Signaling Network Maps

Generation and usage of high-quality molecular signalling network maps can be augmented by standardising notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps, and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.

systems biology↗

bioRxiv2018

A computational framework identifying concordant gene expression-neuropathology associations reveals Complex I as a potential Alzheimer’s disease therapeutic target

Identifying gene expression markers for Alzheimers disease (AD) neuropathology through meta-analysis is a complex undertaking because available data are often from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. Here we introduce a novel probabilistic model-based framework, DECODER, leveraging these discrepancies to identify robust biomarkers for complex phenotypes. Our experiments present: (1) DECODERs potential as a general meta-analysis framework widely applicable to various diseases (e.g., AD and cancer) and phenotypes (e.g., Amyloid-{beta} (A{beta}) pathology, tau pathology, and survival), (2) our results from a meta-analysis using 1,746 human brain tissue samples from nine brain regions in three studies -- the largest expression meta-analysis for AD, to our knowledge --, and (3) in vivo validation of identified modifiers of A{beta} toxicity in a transgenic Caenorhabditis elegans model expressing AD-associated A{beta}, which pinpoints mitochondrial Complex I as a critical mediator of proteostasis and a promising pharmacological avenue toward treating AD.

systems biology↗

bioRxiv2017

Receptor-Based Mechanism of Relative Sensing in Mammalian Signaling Networks

Detecting relative rather than absolute changes in external signals enables cells to make decisions in fluctuating environments and diverse biological contexts. However, how mammalian signaling networks store the memories of past stimuli and use them to compute relative signals is not well understood. Using the growth factor-activated PI3K-Akt signaling pathway, we develop computational and analytical models, and experimentally validate a novel mechanism of relative sensing in mammalian cells. This non-transcriptional mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. We show the robustness and specificity of the relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. The described memory and sensing mechanism could play a role in multiple other sensory cascades where stimulation leads to a proportional reduction in the abundance of cell surface receptors.

systems biology↗

bioRxiv2018

On the Number of Driver Nodes for Controlling a Boolean Network to Attractors

It is known that many driver nodes are required to control complex biological networks. Previous studies imply that O(N) driver nodes are required in both linear complex network and Boolean network models with N nodes if an arbitrary state is specified as the target. In this paper, we mathematically prove under a reasonable assumption that the expected number of driver nodes is only O(log2 N + log2 M) for controlling Boolean networks if the targets are restricted to attractors, where M is the number of attractors. Since it is expected that M is not very large in many practical networks, this is a significant improvement. This result is based on discovery of novel relationships between control problems on Boolean networks and the coupon collectors problem, a well-known concept in combinatorics. We also provide lower bounds of the number of driver nodes as well as simulation results using artificial and realistic network data, which support our theoretical findings.

systems biology↗