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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Functional pathways for metabolic network-based data analysis: the MetPath algorithm

Analyzing biological data using pathways helps identify trends in data tied to the function of a network. A large number of pathway-based analysis tools have been developed toward this goal. These pathways are often manually curated and thus associations are subject to the biases of the curator. A potentially attractive alternative is to define pathways based on the inherent functionality and connectivity of the network itself. Within metabolism, functionality is defined by the production and consumption of metabolites, and connectivity by metabolites participating in reactions through common enzymes. In this work, we present an algorithm, termed MetPath, that calculates pathways for production and consumption of metabolites. We show how these pathways have attractive properties, such as the ability to integrate multiple data types and weight contribution of genes within the pathway by their functional contribution to metabolite production/consumption. Pathways calculated in this manner are condition-specific and thus are custom tailored to the system of interest, in contrast to curated pathways. We find that these pathways predict gene expression correlation better compared to manually-curated pathways. Additionally, we find that these pathways can be used to understand gene expression changes between growth conditions and between cell types. This work serves to better understand the functional pathway structure underlying cell metabolism and helps to enable systems analyses of high-throughput data.

systems biology↗

bioRxiv2017

An extensible ontology for inference of emergent whole cell function from relationships between subcellular processes

Whole cell responses arise from coordinated interactions between diverse human gene products functioning within various pathways underlying sub-cellular processes (SCP). Lower level SCPs interact to form higher level SCPs, often in a context specific manner to give rise to whole cell function. We sought to determine if capturing such relationships enables us to describe the emergence of whole cell functions from interacting SCPs. We developed the \"Molecular Biology of the Cell\" ontology based on standard cell biology and biochemistry textbooks and review articles. Currently, our ontology contains 5,392 genes, 753 SCPs and 19,182 expertly curated gene-SCP associations. Our algorithm to populate the SCPs with genes enables extension of the ontology on demand and the adaption of the ontology to the continuously growing cell biological knowledge. Since whole cell responses most often arise from the coordinated activity of multiple SCPs, we developed a dynamic enrichment algorithm that flexibly predicts SCP-SCP relationships beyond the current taxonomy. This algorithm enables us to identify interactions between SCPs as a basis for higher order function in a context dependent manner, allowing us to provide a detailed description of how SCPs together can give rise to whole cell functions. We conclude that this ontology can, from omics data sets, enable the development of detailed multidimensional SCP networks for predictive modeling of emergent whole cell functions.

systems biology↗

bioRxiv2014

Long-read, whole genome shotgun sequence data for five model organisms

Single molecule, real-time (SMRT) sequencing from Pacific Biosciences is increasingly used in many areas of biological research including de novo genome assembly, structural-variant identification, haplotype phasing, mRNA isoform discovery, and base-modification analyses. High-quality, public datasets of SMRT sequences can spur development of analytic tools that can accommodate unique characteristics of SMRT data (long read lengths, lack of GC or amplification bias, and a random error profile leading to high consensus accuracy). In this paper, we describe eight high-coverage SMRT sequence datasets from five organisms (Escherichia coli, Saccharomyces cerevisiae, Neurospora crassa, Arabidopsis thaliana, and Drosophila melanogaster) that have been publicly released to the general scientific community (NCBI Sequence Read Archive ID SRP040522). Data were generated using two sequencing chemistries (P4C2 and P5C3) on the PacBio RS II instrument. The datasets reported here can be used without restriction by the research community to generate whole-genome assemblies, test new algorithms, investigate genome structure and evolution, and identify base modifications in some of the most widely-studied model systems in biological research.

Genomics↗

bioRxiv2017

Quantifying homologous proteins and proteoforms

Many proteoforms - arising from alternative splicing, post-translational modifications (PTMs), or paralogous genes - have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HIquant) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by HIquant, derived an algorithm for optimal inference, and demonstrated experimentally high accuracy in quantifying fractional PTM occupancy without using external standards, even in the challenging case of the histone modification code. A HIquant server is implemented at: https://web.northeastern.edu/slavov/2014_HIquant/

systems biology↗

bioRxiv2018

Deciphering the metabolic perturbation in hepatic alveolar echinococcosis: a 1H NMR-based metabolomics study

Hepatic alveolar echinococcosis (HAE) is a chronic and potentially lethal parasitic disease. It is caused by growth of Echinococcus multilocularis larvae in liver. To date, early-stage diagnosis for the disease is not mature due to its long asymptomatic incubation period. In this study, a proton nuclear magnetic resonance (1H NMR) -based metabolomics approach was applied in conjunction with multivariate statistical analysis to investigate the altered metabolic profiles in blood serum and urine samples from HAE patients and to identify characteristic metabolic markers associated with HAE. The current results identified 21 distinctive metabolic difference between the HAE patients and healthy individuals, which can be associated with perturbations in energy metabolism, amino acid metabolism, oxidative stress, and neurotransmitter imbalance. In addition, the Fischer ratio, which is the molar ratio of branched-chain amino acids to aromatic amino acids was found significantly lower (p<0.001) in blood serum from HAE patients. The ratio, together with changes in other metabolic pathways may provide new insight into mechanistic understanding of HAE pathogenesis, and may be useful for early-stage HAE diagnosis.\n\nAuthor SummaryHepatic alveolar echinococcosis (HAE) is a life-threatening disease caused by Echinococcus multilocularis infection. The disease has a long asymptomatic early stage (5~15 years), which complicates effective diagnosis of early-stage HAE even with advanced imaging techniques. Metabolomics is an emerging analytical platform that comprises of analysis of all small molecule metabolites that are present within an organism. The applications of metabolomics method on HAE may help to reveal the molecular biology mechanisms of HAE. In the current study, we had used 1H NMR-based metabolomics technique to investigate blood serum and urine samples from HAE patients. Altered metabolic responses and characteristic differential metabolites for HAE were identified. The metabolic profiling of human biofluids provided valuable information for early-stage HAE diagnosis and for therapeutic interventions, without having to extract HAE vesicles from patients. By featuring global and comprehensive metabolic status, the metabolomics approach holds considerable promise as a noninvasive, dynamic, and effective tool for probing the underlying mechanism of HAE.

systems biology↗

bioRxiv2018

An atlas of protein-protein interactions across mammalian tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the complete network of biologically relevant protein-protein interactions, the interactome, has therefore been a central objective of high-throughput biology. Yet, because widely used methods for high-throughput interaction discovery rely on heterologous expression or genetically manipulated cell lines, the dynamics of protein interactions across physiological contexts are poorly understood. Here, we use a quantitative proteomic approach combining protein correlation profiling with stable isotope labelling of mammals (PCP SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide the first proteome-scale survey of interactome dynamics across mammalian tissues, revealing over 27,000 unique interactions with an accuracy comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewiring of protein interactions across tissues is widespread, and is poorly predicted by gene expression or coexpression. Rewired proteins are tightly regulated by multiple cellular mechanisms and implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

systems biology↗

bioRxiv2017

Protein Profiling In Cancer Cell Lines And Tumor Tissue Using Reverse Phase Protein Arrays

Reverse phase protein array (RPPA) technology is an antibody-based high-throughput assay for protein profiling of biological specimens that allows for many measurements with very small amounts of cell lysate. Here, we report the sensitivity, reproducibility, and accuracy of a particular RPPA platform called Zeptosens. We customized the RPPA protocol for our in-house setup, and measured more than 80 total protein and phospho-protein levels in various cancer samples, including cell lines, organoids, tumor chunks, core needle biopsies, and laser-capture microdissected tissue samples. We discuss pros and cons of the RPPA platform, and describe results from profiling 15 cancer cell line cells using RPPA.

systems biology↗

bioRxiv2018

Dynamic compensation and homeostasis: a feedback control perspective

\"Dynamic compensation\" is a robustness property where a perturbed biological circuit maintains a suitable output [Karin O., Swisa A., Glaser B., Dor Y., Alon U. (2016). Mol. Syst. Biol., 12: 886]. In spite of several attempts, no fully convincing analysis seems now to be on hand. This communication suggests an explanation via \"model-free control\" and the corresponding \"intelligent\" controllers [Fliess M., Join C. (2013). Int. J. Contr., 86, 2228-2252], which are already successfully applied in many concrete situations. As a byproduct this setting provides also a slightly different presentation of homeostasis, or \"exact adaptation,\" where the working conditions are assumed to be \"mild.\" Several convincing, but academic, computer simulations are provided and discussed.

systems biology↗

bioRxiv2018

Estimating numbers of fluorescent molecules in single cells by analysing fluctuations in photobleaching

The impact of fluorescence microscopy has been limited by the difficulties of express-ing measurements of fluorescent proteins in numbers of molecules. Absolute numbers enable the integration of results from different laboratories, empower mathematical modelling, and are the bedrock for a quantitative, predictive biology. Here we develop a general algorithm to infer numbers of molecules from fluctuations in the photobleaching of proteins tagged with Green Fluorescent Protein. To untangle measurement noise from stochastic fluctuations, we use the linear noise approximation and Kalman filtering within a framework of Bayesian inference. Not only do our results agree with biochemical measurements for multiple proteins in budding yeast, but we also provide a statistically verified model of measurement noise for fluorescence microscopes. The experiments we require are straightforward and use only a wide-field fluorescence microscope. As such, our approach has the potential to become standard for those practising quantitative fluorescence microscopy.

systems biology↗

bioRxiv2018

Agent-Based Modeling Reveals Possible Mechanisms for Observed Aggregation Cell Behaviors in Myxococcus xanthus

Myxococcus xanthus is a soil bacterium that serves as a model system for biological self-organization. Cells form distinct, dynamic patterns depending on environmental conditions. An agent-based model (ABM) was used to understand how M. xanthus cells aggregate into multicellular mounds in response to starvation. In this model, each cell is modeled as an agent, represented by a point-particle and characterized by its position and moving direction. At low agent density, the model recapitulates the dynamic patterns observed by experiments and a previous biophysical model. To study aggregation at high cell density, we extended the model based on the recent experimental observation that cells exhibit biased movement towards aggregates. We tested two possible mechanisms for this biased movement and demonstrate that a chemotaxis model with adaptation can reproduce the observed experimental results leading to the formation of stable aggregates. Furthermore, our model reproduces the experimentally observed patterns of cell alignment around aggregates.\n\nAuthor summaryCollective self-organization of cells into multicellular structures is important for lifestyle of many bacterial species. Myxococcus xanthus bacterium is a model system for studying this self-organization. In this work, we investigate how in response to starvation M. xanthus cells aggregate into multicellular mounds. A recent study identified the key cellular behaviors that are necessarily for the aggregation but the mechanisms of these behaviors remained unclear. To uncover these mechanisms, we developed a computational model that simulates interactions among a large number of cells. The results demonstrate that the observed bias in the cell reversal times as they move towards the aggregates can be explained by chemotaxis model. In this model cells secrete a chemical signal and respond to it via a partially-adapting biochemical network. The resulting aggregation dynamics are in good agreement with the experiments. Furthermore, chemotaxis signaling model reproduces the experimentally observed patterns of cell alignment around aggregates. On the other hand, an alternative model, based on contact-dependent signaling between cells, fails to aid in aggregation. Thus our models make important predictions about the cellular interactions that drives multicellular aggregation and can serve as a basis to investigate a wider range of developmental mutant strains.

microbiology↗

bioRxiv2018

MasterPATH: network analysis of functional genomics screening data

Functional genomics employs several experimental techniques to investigate gene functions. These techniques such as loss-of-function screening and transcriptome profiling performed in a high-throughput manner give as result a list of genes involved in the biological process of interest. There exist several computational methods for analysis and interpretation of the list. The most widespread methods aim at investigation of biological processes significantly represented in the list or at extracting significantly represented subnetworks. Here we present a new exploratory network analysis method that employs the shortest path approach and centrality measure to uncover members of active molecular pathways leading to the studied phenotype based on the results of functional genomics screening data. We present the method and we demonstrate what data can be retrieved by its application to the terminal muscle differentiation miRNA loss-of-function screening and transcriptomic profiling data and to the druggable loss-of-function RNAi screening data of the DNA repair process.

systems biology↗

bioRxiv2018

Assessing key decisions for transcriptomic data integration in biochemical networks

MotivationTo gain insights into complex biological processes, genome-scale data (e.g., RNA-Seq) are often overlaid on biochemical networks. However, many networks do not have a one-to-one relationship between genes and network edges, due to the existence of isozymes and protein complexes. Therefore, decisions must be made on how to overlay data onto networks. For example, for metabolic networks, these decisions include (1) how to integrate gene expression levels using gene-protein-reaction rules, (2) the approach used for selection of thresholds on expression data to consider the associated gene as \"active\", and (3) the order in which these steps are imposed. However, the influence of these decisions has not been systematically tested.\n\nResultsWe compared 20 decision combinations using a transcriptomic dataset across 32 tissues and showed that definition of which reaction may be considered as active is mainly influenced by thresholding approach used. To determine the most appropriate decisions, we evaluated how these decisions impact the acquisition of tissue-specific active reaction lists that recapitulate organ-system tissue groups. These results will provide guidelines to improve data analyses with biochemical networks and facilitate the construction of context-specific metabolic models.\n\nContactnlewisres@ucsd.edu

systems biology↗

bioRxiv2018

Prediction of Mutations to Control Pathways Enabling Tumour Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial)

Boolean and multi-valued logical formalisms are increasingly used to model complex cellular networks. To ease the development and analysis of logical models, a series of software tools have been proposed, often with specific assets. However, combining these tools typically implies a series of cumbersome software installation and model conversion steps. In this respect, the CoLoMoTo Interactive Notebook provides a joint distribution of several logical modelling software tools, along with an interactive web Python interface easing the chaining of complementary analyses. In this protocol, we demonstrate the assets of this approach through the analysis of a computational model of biological network. Our computational workflow combines (1) the importation of a GINsim model and its display, (2) its format conversion using the Java library BioLQM, (3) the formal prediction of mutations using the OCaml software Pint, (4) the model checking using the C++ software NuSMV, (5) quantitative stochastic simulations using the C++ software MaBoSS, and (6) the visualisation of results using the Python library matplotlib. Starting with a recent Boolean model of the signalling network controlling tumour cell invasion and migration, our model analysis culminates with the prediction of sets of mutations presumably involved in a metastatic phenotype.

systems biology↗

bioRxiv2017

Application of Atlas of Cancer Signalling Network in pre-clinical studies

Initiation and progression of cancer involve multiple molecular mechanisms. The knowledge on these mechanisms is expanding and should be converted into guidelines for tackling the disease. We discuss here formalization of biological knowledge into a comprehensive resource Atlas of Cancer Signalling Network (ACSN) and Google Maps-based tool NaviCell that supports map navigation. The application of maps for omics data visualisation in the context of signalling maps is possible using NaviCell Web Service module and NaviCom tool for generation of network-based molecular portraits of cancer using multi-level omics data. We review how these resources and tools are applied for cancer pre-clinical studies among others for rationalizing synergistic effect of drugs and designing complex disease stage-specific druggable interventions following structural analysis of the maps together with omics data. Modules and maps of ACSN as signatures of biological functions, can help in cancer data analysis and interpretation. In addition, they can also be used to find association between perturbations in particular molecular mechanisms to the risk of a specific cancer type development. These approaches and beyond help to study interplay between molecular mechanisms of cancer, deciphering how gene interactions govern hallmarks of cancer in specific context. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other human diseases.

systems biology↗

bioRxiv2018

On the Biological Signalling, Information and Estimation Limits of Birth Processes

Understanding and uncovering the mechanisms or motifs that molecular networks employ to regulate noise is a key problem in cell biology. As it is often difficult to obtain direct and detailed insight into these mechanisms, many studies instead focus on assessing the best precision attainable on the signalling pathways that compose these networks. Molecules signal one another over such pathways to solve noise regulating estimation and control problems. Quantifying the maximum precision of these solutions delimits what is achievable and allows hypotheses about underlying motifs to be tested without requiring detailed biological knowledge. The pathway capacity, which defines the maximum rate of transmitting information along it, is a widely used proxy for precision. Here it is shown, for estimation problems involving elementary yet biologically relevant birth-process networks, that capacity can be surprisingly misleading. A time-optimal signalling motif, called birth-following, is derived and proven to better the precision expected from the capacity, provided the maximum signalling rate constraint is large and the mean one above a certain threshold. When the maximum constraint is relaxed, perfect estimation is predicted by the capacity. However, the true achievable precision is found highly variable and sensitive to the mean constraint. Since the same capacity can map to different combinations of rate constraints, it can only equivocally measure precision. Deciphering the rate constraints on a signalling pathway may therefore be more important than computing its capacity.

systems biology↗

bioRxiv2016

Network structural features affect stability of Calvin Bassham Benson (CBB) pathway in plants

The stability of the Calvin Bassham Benson (CBB) cycle remains an area of active computational research. Our understanding of biology and the prospect for bioengineered plants with higher productivity may both be impacted by a greater understanding of this area. Here we use the ensemble modelling robustness analysis (EMRA) framework to show that the action of the phosphate/G3P antiporter is much more significant for maintenance of stability than a recently proposed G6P shunt. Additionally, we interpret recent results suggesting that overexpression of RuBiSCO does not improve growth rate of plants but overexpression of SBPase does. Our simulations reproduce this result, but only in models which do not include the G6P shunt. Taken together, these results may suggest a situational role for the G6P shunt, possibly in dynamic situations under starvation or other stress conditions.

Systems Biology↗

bioRxiv2017

Enzymes And Substrates Are Balanced At Minimal Combined Mass Concentration In Vivo

A fundamental problem in biology is how cells organize their resource investment. Cellular metabolism, for example, typically involves hundreds of enzymes and metabolites, but it is unclear according to which principles their concentrations are set. Reasoning that natural selection will drive cells towards achieving a given physiological state at minimal cost, we derive a general equation that predicts the concentration of a metabolite from the concentration of the most abundant and costly enzyme consuming it. Simulations of cellular growth as well as experimental data demonstrate that costs are approximately proportional to molecular masses. For effectively irreversible reactions, the cell maximizes its metabolic efficiency by investing equally into substrate and unbound enzyme molecules. Without fitting any free parameters, the resulting model predicts in vivo substrate concentrations from enzyme concentrations and substrate affinities with high accuracy across data from E. coli and diverse eukaryotes (R2=0.79, geometric mean fold-error 1.74). The corresponding organizing principle - the minimization of the summed mass concentrations of solutes - may facilitate reducing the complexity of kinetic models and will contribute to the design of more efficient synthetic cellular systems.

systems biology↗

bioRxiv2017

On The Principles Of Cell Decision-Making: Intracellular Coupling Improves Cell Responses Fidelity Of Noisy Signals

Reliability of cell fate decision-making is crucial to biological development. Until today it has not been clear what are biologys design principles that allow for reliable cell decision making under the influence of noise. Here, we attempt to answer this question by drawing an analogy between cell decision-making and information theory. We show that coupling of intracellular signalling pathway networks makes cell phenotypic responses reliable for noisy signals. As a proof of concept, we show how cis-interaction of the Notch-Delta pathway allows for increased performance under the influence of noise. Interestingly, in this case, the coupling principle leads to an efficient energy management. Finally, our postulated principle offers a compelling argument why cellular encoding is organized in a non-linear and non-hierarchical manner.

systems biology↗