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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

A theoretical approach for discriminating accurately intrinsic pattern of biological systems and recognizing three kind soybean proteomes

proteomics is able to reveal plentiful information related to different physiological and pathological states of biology. Further, the determination of accurately proteomic pattern is the essential platform for deeply proteomic research. While this has been somewhat ignored so far. In this article the quantitative standard Pg=61%, a biological similarity constant for discriminating accurately intrinsic proteomic patterns was established depending on biological common heredity and variation information equation in symmetric variation state. On the other hand, a novel theoretical method was proposed for linearly dividing nonlinear data sequence into linear segments. The proteomes of three kind soybeans were precisely distinguished from one another by analyzing their infrared fingerprint spectra relying on this theoretically systemic approach. Additionally, methods employed in this paper enable us to quickly, accurately and quantitatively determine the proteomic patterns without using any prior knowledge and learning samples, and without using electrophoresis, high performance liquid chromatography-mass spectrometry techniques, which are high cost, time-consuming. This approach provide us with an excellent one for quickly accurate determining biological species, physiological states and diagnosing pathological states based on proteomes.

bioinformatics↗

bioRxiv2017

MicrobiomeDB: a systems biology platform for integrating, mining and analyzing microbiome experiments.

MicrobiomeDB (http://microbiomeDB.org) is a data discovery and analysis platform that empowers researchers to fully leverage experimental variables to interrogate microbiome datasets. MicrobiomeDB was developed in collaboration with the Eukaryotic Pathogens Bioinformatics Resource Center (http://EuPathDB.org) and leverages the infrastructure and user interface of EuPathDB, which allows users to construct in silico experiments using an intuitive graphical strategy approach. The current release of the database integrates microbial census data with sample details for nearly 14,000 samples originating from human, animal and environmental sources, including over 9,000 samples from healthy human subjects in the Human Microbiome Project (http://portal.ihmpdcc.org/). Query results can be statistically analyzed and graphically visualized via interactive web applications launched directly in the browser, providing insight into microbial community diversity and allowing users to identify taxa associated with any experimental covariate.

microbiology↗

bioRxiv2018

A systems biology approach to explore the impact of maple tree dormancy release on sap variation and maple syrup quality

Maple sap is a complex nutrient matrix collected during Spring to produce maple syrup. The characteristics of sap change over the production period and its composition directly impacts syrup quality. This variability could in part be attributed to changes in tree metabolism following dormancy release, but little is known about these changes in deciduous trees. Therefore, understanding the variation in sap composition associated with dormancy release could help pinpoint the causes of some defects in maple syrup. In particular, a defect known as \"buddy\", is an increasing concern for the industry. This off-flavor appears around the time of bud burst, hence its name. To investigate sap variation related to bud burst and the buddy defect, we monitored sap variation with respect to a dormancy release index (Sbb) and syrup quality. First, we looked at variation in amino acid content during this period. We observed a shift in amino acid relative proportions associated with dormancy release and found that most of them increase rapidly near the point of bud burst, correlating with changes in syrup quality. Second, we identified biological processes that respond to variation in maple sap by performing a competition assay using the barcoded Saccharomyces cerevisiae prototroph deletion collection. This untargeted approach revealed that the organic sulfur content may be responsible for the development of the buddy off-flavor, and that dormancy release is necessary for the appearance of the defect, but other factors such as microbial activity may also be contributing.

plant biology↗

bioRxiv2018

A Toolbox for Discrete Modelling of Cell Signalling Dynamics

In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.\n\nAUTHOR SUMMARYOrdinary differential equation (ODE) based models are a popular approach for modelling biological networks. A limitation of ODE models is that they require complete networks and detailed kinetic parameterisation. An alternative is the use of discrete, executable models, in which nodes are assigned discrete value ranges, and the relationship between them defined with simple mathematical operations. One tool for constructing such models is the BioModelAnalyzer (BMA), an open source and publicly available (https://www.biomodelanalyzer.org) software, aimed to be fully usable by researchers without extensive computational or mathematical experience. A fundamental question for executable models is whether the high level of abstraction substantially reduces expressivity relative to continuous approaches. Here, we present a canonical library of biological signalling motifs, initially defined by Tyson et al (2003), translated for the first time into the BMA. We show that; 1) these motifs are easily and fully translatable from continuous to discrete models, 2) Combining these motifs in a computationally naive way generates a fully functional and predictive model of the yeast cell cycle.

systems biology↗

bioRxiv2017

Predicting single-cell transcription dynamics even when the central limit theorem fails

Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. Although randomness and complexity of biological systems play roles in this concern, we hypothesize that significant and overlooked challenges arise due to specific features of single-molecule events that control crucial biological responses. Here we show that modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, don't apply to the discrete, positive, and non-symmetric distributions that characterize spatiotemporal mRNA fluctuations in single-cells. We demonstrate an alternate approach that fully captures discrete, non-normal effects within finite datasets. As an example, we integrate single-molecule measurements and these advanced computational analyses to explore Mitogen Activated Protein Kinase induction of multiple stress response genes. We discover and validate quantitatively precise, reproducible, and predictive understanding of diverse transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and degradation. Our model-data integration approach extends to any discrete dynamic process with rare events and realistically limited data.\n\nSignificance StatementSystems biology seeks to combine experiments with computation to predict complex biological behaviors. However, despite tremendous data and knowledge, most biological models make terrible predictions. By analyzing single-cell-single-molecule measurements of mRNA in yeast during stress response, we explore how prediction accuracy is controlled by experimental distributions shapes. We find that asymmetric data distributions, which arise in measurements of positive quantities, can cause standard modeling approaches to yield excellent fits but make meaningless predictions. We demonstrate advanced computational tools that solve this dilemma and achieve predictive understanding of many spatiotemporal mechanisms of transcription control including RNA polymerase initiation and elongation and mRNA accumulation, transport and decay. Our approach extends to any discrete dynamic process with rare events and realistically limited data.

systems biology↗

bioRxiv2017

Integrating TARA Oceans Datasets Using Unsupervised Multiple Kernel Learning

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.\n\nWe propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single KPCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of this two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system.\n\nProposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/.

bioinformatics↗

bioRxiv2018

Centrality and the shortest path approach on the human interactome

A network is one of the most convenient way to represent interactions between biological entities in systems biology. A network of molecular interactions is a graph in which the vertices are biological component and the edges correspond to the interactions between them. Many notions and approaches for network analysis came to systems biology from the theory of graphs - a field of mathematics that study graphs. We focused on the study of the shortest path approach in this work. We investigated whether this approach yields valid molecular paths. To perform this, the shortest paths in the human interactome (derived from HPRD and HIPPIE databases) were found between all relevant combinations of proteins taken from eight well-studied highly conserved signaling pathways from the KEGG database (NF-kappa B, MAPK, Jak-STAT, mTOR, ErbB, Wnt, TGF-beta and the signaling part of the apoptotic process). Canonical paths were systematically compared with the shortest counterparts and centrality of vertices and paths in subnetworks induced by the shortest paths were analyzed. We found that the sets of the shortest paths contain the canonical counterparts only for very short canonical paths (length 2-3 interactions). We also found that high centrality vertices tend to belong to canonical counterparts, to less extent this can also be said about high centrality paths.

systems biology↗

bioRxiv2018

Agronomic Linked Data (AgroLD): a Knowledge-based System to Enable Integrative Biology in Agronomy

Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD - www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources -such as Gramene.org and TropGeneDB- with 10 ontologies -such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLDs objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.

bioinformatics↗

bioRxiv2018

Dynamic Flux Balance Analysis Models in SBML

Computational models in systems biology and systems medicine are typically simulated using a single formalism such as ordinary differential equations (ODE). However, more complex models require the coupling of multiple formalisms since different biological phenomena are better described by different methods. For example, metabolism in steady state is often modeled using flux-balance analysis (FBA) whereas dynamic changes of model components are better described via ODEs. The coupling of FBA and ODE modeling formalisms results in dynamic FBA models. A major challenge is how to describe such hybrid models that couple multiple formalisms in a standardized way so that they can be exchanged between tools and simulated consistently in a reproducible manner. This paper presents a scheme for encoding and implementation of dynamic FBA models in the Systems Biology Markup Language (SBML), thereby enabling the exchange of multi-framework computational models between software tools. We demonstrate the feasibility of the approach using various example models and show that different tools are able to simulate the hybrid models and agree on the results. As part of this work, two independent implementations of a multi-framework simulation method for dynamic FBA have been developed supporting such models: iBioSim and sbmiutils.

bioinformatics↗

bioRxiv2018

Gene target discovery with network analysis in Toxoplasma gondii

Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasites life cycle. These new findings can contribute to understand of parasite pathogenesis.

systems biology↗

bioRxiv2017

Subharmonics And Chaos In Simple Periodically-Forced Biomolecular Models

This paper uncovers a remarkable behavior in two biochemical systems that commonly appear as components of signal transduction pathways in systems biology. These systems have globally attracting steady states when unforced, so they might have been considered \"uninteresting\" from a dynamical standpoint. However, when subject to a periodic excitation, strange attractors arise via a period-doubling cascade. Quantitative analyses of the corresponding discrete chaotic trajectories are conducted numerically by computing largest Lyapunov exponents, power spectra, and autocorrelation functions. To gain insight into the geometry of the strange attractors, the phase portraits of the corresponding iterated maps are interpreted as scatter plots for which marginal distributions are additionally evaluated. The lack of entrainment to external oscillations, in even the simplest biochemical networks, represents a level of additional complexity in molecular biology, which has previously been insufficiently recognized but is plausibly biologically important.

systems biology↗

bioRxiv2018

A probabilistic interpretation of PID controllers using active inference

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition within general mathematical frameworks derived from information theory, statistical physics and machine learning. Furthermore, it has been argued that one such proposal, active inference, combines both information and control theory and has its roots in cybernetics studies of the brain. The connections between information and control theory have been discussed since the 1950s by scientists like Shannon and Kalman and have recently risen to prominence in modern stochastic optimal control theory. How-ever, the implications of the confluence of these two theoretical frame-works for the biological sciences have been slow to emerge. Here we argue that if the active inference proposal is to be taken as a general process theory for biological systems, we need to consider how existing control theoretical approaches to biological systems relate to it. In this work we will focus on PID (Proportional-Integral-Derivative) controllers, one of the most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation once we use only simple linear generative models.

systems biology↗

bioRxiv2016

How domain growth is implemented determines the long term behaviour of a cell population through its effect on spatial correlations.

Domain growth plays an important role in many biological systems, and so the inclusion of domain growth in models of these biological systems is important to understanding how these biological systems function. In this work we present methods to include the effects of domain growth on the evolution of spatial correlations in a continuum approximation of a lattice-based model of cell motility and proliferation. We show that, depending on the way in which domain growth is implemented, different steady-state densities are predicted for an agent population. Furthermore, we demonstrate that the way in which domain growth is implemented can result in the evolution of the agent density depending on the size of the domain. Continuum approximations that ignore spatial correlations cannot capture these behaviours, while those that account for spatial correlations do. These results will be of interest to researchers in developmental biology, as they suggest that the nature of domain growth can determine the characteristics of cell populations.

Systems Biology↗

bioRxiv2014

Modelling reactions catalysed by carbohydrate-active enzymes

Carbohydrate polymers are ubiquitous in biological systems and their roles are highly diverse, ranging from energy storage over mechanical stabilisation to mediating cell-cell or cell-protein interactions. The functional diversity is mirrored by a chemical diversity that results from the high flexibility of how different sugar monomers can be arranged into linear, branched or cyclic polymeric structures. Mathematical models describing biochemical processes on polymers are faced with various difficulties. First, polymer-active enzymes are often specific to some local configuration within the polymer but are indifferent to other features. That is they are potentially active on a large variety of different chemical compounds, meaning that polymers of different size and structure simultaneously compete for enzymes. Second, especially large polymers interact with each other and form water-insoluble phases that restrict or exclude the formation of enzyme-substrate complexes. This heterogeneity of the reaction system has to be taken into account by explicitly considering processes at the, often complex, surface of the polymer matrix. We review recent approaches to theoretically describe polymer biochemical systems. All attempts address a particular challenge, which we discuss in more detail. We emphasise a recent attempt which draws novel analogies between polymer biochemistry and statistical thermodynamics and illustrate how this parallel leads to novel insights about non-uniform polymer reactant mixtures. Finally, we discuss the future challenges of the young and growing field of theoretical polymer biochemistry.

Systems Biology↗

bioRxiv2014

Inference of cell-cell interactions from population density characteristics and cell trajectories on static and growing domains

A key feature of cell migration is how cell movement is affected by cell-cell interactions. Furthermore, many cell migratory processes such as neural crest stem cell migration [1, 2] occur on growing domains or in the presence of a chemoattractant. Therefore, it is important to study interactions between migrating cells in the context of domain growth and directed motility. Here we compare discrete and continuum models describing the spatial and temporal evolution of a cell population for different types of cell-cell interactions on static and growing domains. We suggest that cell-cell interactions can be inferred from population density characteristics in the presence of motility bias, and these population density characteristics for different cell-cell interactions are conserved on both static and growing domains. We also study the expected displacement of a tagged cell, and show that different types of cell-cell interactions can give rise to cell trajectories with different characteristics. These characteristics are conserved in the presence of domain growth, however, they are diminished in the presence of motility bias. Our results are relevant for researchers who study the existence and role of cell-cell interactions in biological systems, so far as we suggest that different types of cell-cell interactions could be identified from cell density and trajectory data.

Systems Biology↗

bioRxiv2014

Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank Matrices

With the growth of high-throughput proteomic data, in particular time series gene expression data from various perturbations, a general question that has arisen is how to organize inherently heterogenous data into meaningful structures. Since biological systems such as breast cancer tumors respond differently to various treatments, little is known about exactly how these gene regulatory networks (GRNs) operate under different stimuli. For example, when we apply a drug-induced perturbation to a target protein, we often only know that the dynamic response of the specific protein may be affected. We do not know by how much, how long and even whether this perturbation affects other proteins or not. Challenges due to the lack of such knowledge not only occur in modeling the dynamics of a GRN but also cause bias or uncertainties in identifying parameters or inferring the GRN structure. This paper describes a new algorithm which enables us to estimate bias error due to the effect of perturbations and correctly identify the common graph structure among biased inferred graph structures. To do this, we retrieve common dynamics of the GRN subject to various perturbations. We refer to the task as \"repairing\" inspired by \"image repairing\" in computer vision. The method can automatically correctly repair the common graph structure across perturbed GRNs, even without precise information about the effect of the perturbations. We evaluate the method on synthetic data sets and demonstrate advantages over C-regularized graph inference by advancing our understanding of how these networks respond across different targeted therapies. Also, we demonstrate an application to the DREAM data sets and discuss its implications to experiment design.

Systems Biology↗

bioRxiv2015

A reductionist approach to model photosynthetic self-regulation in eukaryotes in response to light

Along with the development of several large-scale methods such as mass spectrometry or micro arrays, genome wide models became not only a possibility but an obvious tool for theoretical biologists to integrate and analyse complex biological data. Nevertheless, incorporating the dynamics of photosynthesis remains one of the major challenges while reconstructing metabolic networks of plants and other photosynthetic organisms. In this review, we aim to provide arguments that small-scale models are still a suitable choice when it comes to discover organisational principles governing the design of biological systems. We give a brief overview of recent modelling efforts in understanding the interplay between rapid, photoprotective mechanisms and the redox balance within the thylakoid membrane, discussing the applicability of a reductionist approach in modelling self-regulation in plants, and outline possible directions for further research.

Systems Biology↗

bioRxiv2016

A new computational model captures fundamental architectural features of diverse biological networks

Complex biological systems are often represented by network graphs; however, their structural features are not adequately captured by existing computational graph models, perhaps because the datasets used to assemble them are incomplete and contain elements that lack shared functions. Here, we analyze three large, near-complete networks that produce specific cellular or behavioral outputs: a molecular yeast mitochondrial regulatory protein network, and two anatomical networks of very different scale, the mouse brain mesoscale connectivity network, and the C. elegans neuronal network. Surprisingly, these networks share similar characteristics. All consist of large communities composed of modules with general functions, and topologically distinct subnetworks spanning modular boundaries responsible for their more specific phenotypical outputs. We created a new model, SBM-PS, which generates networks by combining communities, followed by adjustment of connections by a path selection mechanism. This model captures fundamental architectural features that are common to the three networks.

Systems Biology↗