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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Design Specifications for Cellular Regulation

A critical feature of all cellular processes is the ability to control the rate of gene or protein expression and metabolic flux in changing environments through regulatory feedback. We review the many ways that regulation is represented through causal, logical and dynamical components. Formalizing the nature of these components promotes effective comparison among distinct regulatory networks and provides a common framework for the potential design and control of regulatory systems in synthetic biology.

systems biology↗

bioRxiv2017

Classification Of Gene Signatures For Their Information Value And Functional Redundancy

Large collections of gene signatures play a pivotal role in interpreting results of omics data analysis but suffer from compositional (large overlap) and functional (redundant read-outs) redundancy, and many gene signatures rarely pop-up in statistical tests. Based on pan-cancer data analysis, here we define a restricted set of 962 so called informative signatures and demonstrate that they have more chances to appear highly enriched in cancer biology studies. We show that the majority of informative signatures conserve their weights for the composing genes (eigengenes) from one cancer type to another. We construct InfoSigMap, an interactive online map showing the structure of compositional and functional redundancies between informative signatures and charting the territories of biological functions accessible through transcriptomic studies. InfoSigMap can be used to visualize in one insightful picture the results of comparative omics data analyses and suggests reconsidering existing annotations of certain reference gene set groups.

systems biology↗

bioRxiv2018

Generation of human neural retina transcriptome atlas by single cell RNA sequencing

The retina is a highly specialized neural tissue that senses light and initiates image processing. Although the functional organisation of specific cells within the retina has been well-studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile cell types in the human retina, we performed single cell RNA-sequencing on 20,009 cells obtained post-mortem from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct clusters representing all known retinal cells: rod photoreceptors, cone photoreceptors, Muller glia cells, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, retinal astrocytes and microglia. Notably, our data captured molecular profiles for healthy and early degenerating rod photoreceptors, and revealed a novel role of MALAT1 in putative rod degeneration. We also demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Muller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to our understanding of retinal biology and disease.

systems biology↗

bioRxiv2018

Kinetic Model of Translational Autoregulation

We investigate dynamics of a kinetic model of inhibitory autoregulation as exemplified when a protein inhibits its own production by interfering with its messenger RNA, known in molecular biology as translational autoregulation. We first show how linear models without feedback set the stage with a nonequilibrium steady state that constitutes the target of the regulation. However, regulation in the simple linear model is far from optimal. The negative feedback mechanism whereby the protein \"jams\" the mRNA greatly enhances the effectiveness of the control, with response to perturbation that is targeted, rapid, and metabolically efficient. Understanding the full dynamics of the system phase space is essential to understanding the autoregulation process.

systems biology↗

bioRxiv2018

Causal interactions from proteomic profiles: molecular data meets pathway knowledge

Measurement of changes in protein levels and in post-translational modifications, such as phosphorylation, can be highly informative about the phenotypic consequences of genetic differences or about the dynamics of cellular processes. Typically, such proteomic profiles are interpreted intuitively or by simple correlation analysis. Here, we present a computational method to generate causal explanations for proteomic profiles using prior mechanistic knowledge in the literature, as recorded in cellular pathway maps. To demonstrate its potential, we use this method to analyze the cascading events after EGF stimulation of a cell line, to discover new pathways in platelet activation, to identify influential regulators of oncoproteins in breast cancer, to describe signaling characteristics in predefined subtypes of ovarian and breast cancers, and to highlight which pathway relations are most frequently activated across 32 cancer types. Causal pathway analysis, that combines molecular profiles with prior biological knowledge captured in computational form, may become a powerful discovery tool as the amount and quality of cellular profiling rapidly expands. The method is freely available at http://causalpath.org.

systems biology↗

bioRxiv2017

Evolution and Engineering of Allosteric Regulation in Protein Kinases

Allosteric regulation - the control of protein function by sites far from the active site - is a common feature of proteins that enables dynamic cellular responses. Reversible modifications such as phosphorylation are well suited to mediate such regulatory dynamics, yet the evolution of new allosteric regulation demands explanation. To understand this, we mutationally scanned the surface of a prototypical kinase to identify readily evolvable phosphorylation sites. The data reveal a set of spatially distributed \"hotspots\" that coevolve with the active site and preferentially modulate kinase activity. By engineering simple consensus phosphorylation sites at these hotspots we successfully rewired in vivo cell signaling. Beyond synthetic biology, the hotspots are frequently used by the diversity of natural allosteric regulatory mechanisms in the kinase family and exploited in human disease.\n\nONE SENTENCE SUMMARYCell signaling is easily rewired by introducing new phosphoregulation at latent allosteric surface sites.

systems biology↗

bioRxiv2014

Module organizational principles and dynamics in biological networks

A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying organizational principles of a module remain unclear. In this study, we collected CORUM module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores (PPIES) and interface evolution scores (IES) to infer module elements, including core and ring components. Functions of core components were highly correlated (Pearsons r = 0.98) with those of 11,384 essential genes. In comparison with ring components, core proteins and PPIs were conserved in multiple species. Subsequently, protein dynamics and module dynamics of biological networks and functional diversities confirmed that core components form dynamic biological network hubs and play key roles in various biological functions. PPIES and IES can reflect module organization principles and protein/module dynamics in biological networks. On the basis of the analyses of gene essentiality, module dynamics, network topology, and gene co-expression, the module organizational principles can be described as follows: 1) a module consists of core and ring components; 2) the core components play major roles in biological functions and collaborate with ring components to perform certain functions in some cases; 3) the core components are conserved and essential in module dynamics in time and space.

Systems Biology↗

bioRxiv2016

Notions of similarity for computational biology models

Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of \"similarity\" may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users intuition about model similarity, and to support complex model searches in databases.

Systems Biology↗

bioRxiv2018

On the Social Structure behind Biological Collections

In this paper we describe two network models as a base for understanding the relevance of social processes involving collectors for shaping the composition of biological collections. Species-Collector Networks (SCNs) represent the interests of collectors towards particular species, while Collector CoWorking Networks (CWNs) represent collaborative ties between collectors during fieldwork. We demonstrate the practical use of our models with species occurrence data from the University of Brasilia Herbarium.

systems biology↗

bioRxiv2018

Advances in Gene Ontology Utilization Improve Statistical Power of Annotation Enrichment

Gene-annotation enrichment is a common method for utilizing ontology-based annotations in these gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through the ontology through edges in the ontological graph, referred to as relations. However, some relations are semantically problematic with respect to scope, necessitating their omission lest erroneous term mappings occur. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. Here, we demonstrate the improvements in annotation enrichment by re-interpreting edges that would otherwise be omitted by traditional ancestor path-tracing methods.\n\nWe demonstrate that GOcats unique handling of relations improves enrichment over conventional methods in the analysis of two different gene-expression datasets: a breast cancer microarray dataset and several horse cartilage development RNAseq datasets. With the breast cancer microarray dataset, we observed significant improvement (one-sided binomial test p-value=1.86E-25) in 182 of 217 significantly enriched GO terms identified from the conventional path traversal method when GOcats path traversal was used. We also found new significantly enriched terms using GOcats, whose biological relevancy has been experimentally demonstrated elsewhere. Likewise, on the horse RNAseq datasets, we observed a significant improvement in GO term enrichment when using GOcats path traversal: one-sided binomial test p-values range from 1.32E-03 to 2.58E-44.

systems biology↗

bioRxiv2016

The effect of domain growth on spatial correlations

Mathematical models describing cell movement and proliferation are important research tools for the understanding of many biological processes. In this work we present methods to include the effects of domain growth on the evolution of spatial correlations between agent locations in a continuum approximation of a one-dimensional lattice-based model of cell motility and proliferation. This is important as the inclusion of spatial correlations in continuum models of cell motility and proliferation without domain growth has previously been shown to be essential for their accuracy in certain scenarios. We include the effect of spatial correlations by deriving a system of ordinary differential equations that describe the expected evolution of individual and pair density functions for agents on a growing domain. We then demonstrate how to simplify this system of ordinary differential equations by using an appropriate approximation. This simplification allows domain growth to be included in models describing the evolution of spatial correlations between agents in a tractable manner.

Systems Biology↗

bioRxiv2017

Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides

The basidomycete yeast Rhodosporidium toruloides (a.k.a. Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions into the R. toruloides genome. We identified 1337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We found that both mitochondrial and peroxisomal enzymes were required for growth on fatty acids, with different peroxisomal enzymes required on different fatty acids. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted function in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, as well as genes of unknown function. These results greatly advance our understanding of lipid metabolism in this oleaginous species, identify key biological processes to be further explored and optimized for production of lipid-based bioproducts, and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi.

systems biology↗

bioRxiv2015

Molecular Mechanisms for the Biological Storage of Renewable Energy

Recent and ongoing discoveries in the field of extracellular electron transport offer the potential to electrically power highly flexible, carbon-fixing microbial metabolisms and generate a rich variety of chemicals and fuels. This process, electrosynthesis, creates the opportunity to use biology for the low cost storage of renewable electricity and the synthesis of fuels that produce no net carbon dioxide. This article highlights recent discoveries on the molecular machinery underpinning electrosynthesis and reviews recent work on the energy conversion efficiency of photosynthesis to begin to establish a framework to quantify the overall energy storage and conversion efficiency of electrosynthesis.

Systems Biology↗

bioRxiv2016

NaviCom: A web application to create interactive molecular network portraits using multi-level omics data

Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of molecular functions encompassed by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing to address specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signaling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease.

systems biology↗

bioRxiv2018

Model Classification for Uncertainty Reduction in Biochemical Kinetic Models

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.\n\nAuthor SummaryKinetic models are the most promising tool for understanding the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and thus we need large-scale models for dependable in silico analyses of metabolism. However, uncertainty in kinetic parameters impedes the development of kinetic models, and uncertainty levels increase with the model size. Tools that will address the issues with parameter uncertainty and that will be able to reduce the uncertainty propagation through the system are therefore needed. In this work, we applied a method called iSCHRUNK that combines parameter sampling and machine learning techniques to characterize the uncertainties and uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network. The proposed method allowed us to identify a small number of parameters that determine the responses in the network regardless of the values of other parameters. As a consequence, in future studies of metabolism, it will be sufficient to explore a reduced kinetic space, and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable.

systems biology↗

bioRxiv2017

Tree-ensemble analysis tests for presence of multifurcations in single cell data

We introduce TreeTop, an algorithm for single-cell data analysis to identify and assess statistical significance of branch points in biological processes with possibly multi-level branching hierarchies. We demonstrate branch point identification for processes with varying topologies, including T cell maturation, B cell differentiation and hematopoiesis. Our analyses are consistent with recent experimental studies suggesting a shallow hierarchy of differentiation events in hematopoiesis, rather than the classical multi-level hierarchy.

systems biology↗

bioRxiv2017

Identification of Cell States Using Super-Enhancer RNA

A new class of regulatory elements called super-enhancers, comprised of multiple neighboring enhancers, have recently been reported to be the key transcriptional drivers of cellular, developmental, and disease states. Here, we propose to define super-enhancer RNA as highly expressed enhancer RNAs that are transcribed from a cluster of localized genomic regions. Using the cap analysis of gene expression sequencing data from FANTOM5, we systematically explored the enhancer and messenger RNA landscapes in hundreds of different cell types in response to various environments. Applying non-negative matrix factorization (NMF) to super-enhancer RNA profiles, we found that different cell types were well classified. In addition, through the NMF of individual time-course profiles from a single cell-type, super-enhancer RNAs were clustered into several states with progressive patterns. We further investigated the enriched biological functions of the proximal genes involved in each pattern, and found that they were associated with the corresponding developmental process. The proposed super-enhancer RNAs can act as a good alternative, without the complicated measurement of histone modifications, for identifying important regulatory elements of cell type specification and identifying dynamic cell states.

systems biology↗

bioRxiv2018

Targeting comorbid diseases via network endopharmacology

The traditional drug discovery paradigm has shaped around the idea of \"one target, one disease\". Recently, it has become clear that not only it is hard to achieve single target specificity but also it is often more desirable to tinker the complex cellular network by targeting multiple proteins, causing a paradigm shift towards polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is targeting common biological pathways involved in diseases, giving rise to \"endopharmacology\" (multiple targets, multiple diseases). In this study, leveraging powerful network medicine tools, we describe a recipe for first, identifying common pathways pertaining to diseases and then, prioritizing drugs that target these pathways towards endopharmacology. We present proximal pathway enrichment analysis (PxEA) that uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs for their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. As a proof of principle, we focus on nine autoimmune disorders and using PxEA, we show that many drugs indicated for these conditions are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide the high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimers disease, two phenotypes that have recently gained attention due to the increased comorbidity among patients.

systems biology↗