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Results for “Systems Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 19 recordsLinked to original sources

bioRxiv2015

Current systems biology approaches in hazard assessment of nanoparticles

The amount of nanoparticles (NPs) in human environment is increasing. The main sources are the increased introduction in consumer products and air pollution (diesel exhaust). It is meanwhile common knowledge that NPs behave differently as bulk material because of their nano-size. This leads in general to a higher reactivity and some other changed properties, e.g. solubility, surface potential, conductivity, and, to different effects on biological systems. The main impacts of NPs on a cellular and organism level are meanwhile well known: release of toxic ions, increased oxidative stress, and inflammation. Beside these, there is increasing evidence that NPs, especially in low dose/long exposure scenarios, affect biological systems in a broader way, interact with drugs, and exacerbate the effects of diseases. To investigate these effects systems biology approaches are the method of choice. This review summarizes the state of the art of nanoparticle effects on cells and organisms and demonstrate the add value of systems biology investigations to NP hazard assessment.\n\nAbbreviations: nanoparticle (NP), carbon nanotube (CNT), single walled carbon nanotube (SWCNT), multi walled carbon nanotube (MWCNT), ADME (adsorption, distribution, metabolization, excretion), PAMAM (poly(amidoamine)),

Systems Biology↗

bioRxiv2017

Deciphering the regulation mechanism in biochemical networks by a systems-biology approach

To decipher molecular mechanisms in biological systems from system-level input-output data is challenging especially for complex processes that involve interactions among multiple components. Here, we study regulation of the multi-domain (P1-5) histidine kinase CheA by the MCP chemoreceptors. We develop a network model to describe dynamics of the system treating the receptor complex with CheW and P3P4P5 domains of CheA as a regulated enzyme with two substrates, P1 and ATP. The model enables us to search the hypothesis space systematically for the simplest possible regulation mechanism consistent with the available data. Our analysis reveals a novel dual regulation mechanism wherein besides regulating ATP binding the receptor activity has to regulate one other key reaction, either P1 binding or phosphotransfer between P1 and ATP. Furthermore, our study shows that the receptors only control kinetic rates of the enzyme without changing its equilibrium properties. Predictions are made for future experiments to distinguish the remaining two dual-regulation mechanisms. This systems-biology approach of combining modeling and a large input-output data-set should be applicable for studying other complex biological processes.

systems biology↗

bioRxiv2015

Systems Biology Approach to Model the Life Cycle of Trypanosoma cruzi

Due to recent advances in reprogramming cell phenotypes, many efforts have been dedicated to developing reverse engineering procedures for the identification of gene regulatory networks that emulate dynamical properties associated with the cell fates of a given biological system. In this work, we propose a systems biology approach for the reconstruction of the gene regulatory network underlying the dynamics of the Trypanosoma cruzis life cycle. By means of an optimisation procedure, we embedded the steady state maintenance, and the known phenotypic transitions between these steady states in response to environmental cues, into the dynamics of a gene network model. In the resulting network architecture we identified a small subnetwork, formed by seven interconnected nodes, that controls the parasites life cycle. The present approach could be useful for better understanding other single cell organisms with multiple developmental stages.\n\nAbbreviations

Systems Biology↗

bioRxiv2015

Hybrid Systems Modeling for (Cancer) Systems Biology

The advent of biological data of increasingly higher resolution in space and time has triggered the use of dynamic models to explain and predict the evolution of biological systems over space and time. Computer-aided system modeling and analysis in biology has led to many new discoveries and explanations that would otherwise be intractable to articulate without the available data and computing power. Nevertheless, the complexity in biology still challenges many labs in capturing studied phenomena in models that are tractable and simple enough to analyze. Moreover, the popular use of ordinary differential equation models have their limitations in that they solely capture continuous dynamics, while we observe many discrete dynamic phenomena in biology such as gene switching or mutations. Hybrid systems modeling provides a framework in which both continuous and discrete dynamics can be simulated and analyzed. Moreover, it provides techniques to develop approximations and abstractions of complex dynamics that are tractable to analyze.

Systems Biology↗

bioRxiv2018

Constructing predictive cancer systems biology models

Systems biology combines computational modeling with quantitative experimental measurements to study complex biological processes. Here, we outline an approach for parameterizing and validating a systems biology model to yield predictive tool that can generate testable hypotheses and expand biological understanding.

systems biology↗

bioRxiv2017

Building Applications For Interactive Data Exploration In Systems Biology

As the systems biology community generates and collects data at an unprecedented rate, there is a growing need for interactive data exploration tools to explore the datasets. These tools need to combine advanced statistical analyses, relevant knowledge from biological databases, and interactive visualizations in an application with clear user interfaces. To answer specific research questions tools must provide specialized user interfaces and visualizations. While these are application-specific, the underlying components of a data analysis tool can be shared and reused later. Application developers can therefore compose applications of reusable services rather than implementing a single monolithic application from the ground up for each project.\n\nOur approach for developing data exploration applications in systems biology builds on the microservice architecture. Microservice architectures separates an application into smaller components that communicate using language-agnostic protocols. We show that this design is suitable in bioinformatics applications where applications often use different tools, written in different languages, by different research groups. Packaging each service in a software container enables re-use and sharing of key components between applications, reducing development, deployment, and maintenance time.\n\nWe demonstrate the viability of our approach through a web application, MIxT blood-tumor, for exploring and comparing transcriptional profiles from blood and tumor samples in breast cancer patients. The application integrates advanced statistical software, up-to-date information from biological databases, and modern data visualization libraries.\n\nThe web application for exploring transcriptional profiles, MIxT, is online at mixt-blood-tumor.bci.mcgill.ca and open-sourced at github.com/fjukstad/mixt. Packages to build the supporting microservices are open-sourced as a part of Kvik at github.com/fjukstad/kvik.

bioinformatics↗

bioRxiv2018

Open source tools and standardized data in cancer systems biology

To reach the full potential of multicellular systems biology, mathematical and computational modelers must pool their efforts to share and curate biophysical measurements, create and combine mathematical models, analyze and visualize model predictions, and validate and refine against shared data. An ecosystem of open source software that reads standardized data is essential. We review the state-of-the-art in open source software and data standards in multicellular systems biology, and point out areas of needed growth to move beyond isolated models to community-driven frameworks that shed light on complex problems in multicellular systems biology.

systems biology↗

bioRxiv2016

Petri Net-Based Graphical and Computational Modelling of Biological Systems

In silico modelling of biological pathways is a major endeavour of systems biology. Here we present a methodology for construction of pathway models from the literature and other sources using a biologist-friendly graphical modelling system. The pathway notation scheme, called mEPN, is based on the principles of the process diagrams and Petri nets, and facilitates both the graphical representation of complex systems as well as dynamic simulation of their activity. The protocol is divided into four sections: 1) assembly of the pathway in the yEd software package using the mEPN scheme, 2) conversion of the pathway into a computable format, 3) pathway visualisation and in silico simulation using the BioLayout Express3D software, 4) optimisation of model parameterisation. This method allows reconstruction of any metabolic, signalling and transcriptional pathway as a means of knowledge management, as well as supporting the systems level modelling of their dynamic activity.

Systems Biology↗

bioRxiv2016

The DOE Systems Biology Knowledgebase (KBase)

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology -- predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

bioinformatics↗

bioRxiv2018

Scalable Nonlinear Programming Framework for Parameter Estimation in Dynamic Biological System Models

We present a nonlinear programming (NLP) framework for the scalable solution of parameter estimation problems that arise in dynamic modeling of biological systems. Such problems are computationally challenging because they often involve highly nonlinear and stif differential equations as well as many experimental data sets and parameters. The proposed framework uses cutting-edge modeling and solution tools which are computationally efficient, robust, and easy-to-use. Specifically, our framework uses a time discretization approach that: i) avoids repetitive simulations of the dynamic model, ii) enables fully algebraic model implementations and computation of derivatives, and iii) enables the use of computationally efficient nonlinear interior point solvers that exploit sparse and structured linear algebra techniques. We demonstrate these capabilities by solving estimation problems for synthetic human gut microbiome community models. We show that an instance with 156 parameters, 144 differential equations, and 1,704 experimental data points can be solved in less than 3 minutes using our proposed framework (while an off-the-shelf simulation-based solution framework requires over 7 hours). We also create large instances to show that the proposed framework is scalable and can solve problems with up to 2,352 parameters, 2,304 differential equations, and 20,352 data points in less than 15 minutes. Competing methods reported in the computational biology literature cannot address problems of this level of complexity. The proposed framework is flexible, can be broadly applied to dynamic models of biological systems, and enables the implementation of sophisticated estimation techniques to quantify parameter uncertainty, to diagnose observability/uniqueness issues, to perform model selection, and to handle outliers.\n\nAuthor summaryConstructing and validating dynamic models of biological systems spanning biomolecular networks to ecological systems is a challenging problem. Here we present a scalable computational framework to rapidly infer parameters in complex dynamic models of biological systems from large-scale experimental data. The framework was applied to infer parameters of a synthetic microbial community model from large-scale time series data. We also demonstrate that this framework can be used to analyze parameter uncertainty, to diagnose whether the experimental data are sufficient to uniquely determine the parameters, to determine the model that best describes the data, and to infer parameters in the face of data outliers.

bioinformatics↗

bioRxiv2018

Advanced Graph Traversal used in Protein Interactions Network and Systems Biology

A methodological framework of graph traversal in Systems Biology is presented here. At present there is need to investigate system rather individual component. The proposed analysis generalizes the various idea of network representations of protein interactions. This approach highlights various methods used in construction of protein interaction graph or network using suitable algorithm. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the importance of each protein for its interactions. Furthermore, the determination of the pattern of edge between residues yields insights into the function prediction of an interaction. This is of special interest to investigate intrinsically disordered proteins, since it is difficult to determine structural (three-dimensional) architecture of each proteins in protein interactions network. In present work various approaches for protein interactions network construction, models and methods along with graph theories has been discussed which can be used to reveal hidden properties and features of a network. Further effective algorithm for visualization of protein interactions is suggested. As construction of Biological network is dependent on various properties of graph. A holistic approach such as Systems Biology approach can better solve the problem. This network profiling combined with knowledge extraction will help biologist to explore hidden information in genome as well as in proteome..

bioinformatics↗

bioRxiv2017

A Metabolic Reaction Balancing Web Service for Computational Systems Biology

BackgroundIn metabolic network reconstruction the stoichiometric balancing of reactions is essential to create realistic constraint-based models. At the genome scale, balancing is a repetitive task that consumes valuable curator resource that could be deployed elsewhere. Automatic reaction balancing is possible and could be useful across computational systems biology, but widespread use of the appropriate code has been limited by the diversity of non-interoperable programming languages used in the field. RESTful web services offer a language-agnostic way of binding services together.\n\nResultsReaction balancing can be posed as a mixed integer linear programming problem to identify stoichiometric coefficients and infer commonly missing components. This functionality has been exposed as a web service that consumes a list of reactions as JSON or SBML. The reaction balancing web service has been deployed at http://www.nactem.ac.uk/balancer. Code is available via Github. By way of demonstration the service has been applied to a Chinese hamster ovary cell metabolic reconstruction to bring a further 219 reactions into balance.\n\nConclusionsThe majority of systems biology software cannot access existing automatic reaction balancing tools due to a lack of language-specific bindings. Web services bridge different languages by using widely-spoken web communication protocols, meaning that one binding works for almost all languages. Automatic reaction balancing can now be consumed by any systems biology software via a RESTful web service.

systems biology↗

bioRxiv2017

Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems

Advances in tissue clearing and molecular labelling methods are enabling unprecedented optical access to large intact biological systems. These advances fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While Light Sheet Microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light-sheet illumination. To address this fundamental limitation, we have developed Light Sheet Theta Microscopy (LSTM), which uniformly illuminates samples from same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing imaging resolution, depth and speed. We present detailed characterization of LSTM, and show that this approach achieves rapid high-resolution imaging of large intact samples with superior uniform high-resolution than LSM. LSTM is a significant step in high-resolution quantitative mapping of structure and function of large intact biological systems.

neuroscience↗

bioRxiv2015

A cell-free framework for biological systems engineering

SUMMARYWhile complex dynamic biological networks control gene expression and metabolism in all living organisms, engineering comparable synthetic networks remains challenging1,2. Conducting extensive, quantitative and rapid characterization during the design and implementation process of synthetic networks is currently severely limited due to cumbersome molecular cloning and the difficulties associated with measuring parts, components and systems in cellular hosts. Engineering gene networks in a cell-free environment promises to be an efficient and effective approach to rapidly develop novel biological systems and understand their operating regimes3-5. However, it remains questionable whether complex synthetic networks behave similarly in cells and a cell-free environment, which is critical for in vitro approaches to be of significance to biological engineering. Here we show that synthetic dynamic networks can be readily implemented, characterized, and engineered in a cell-free framework and consequently transferred to cellular hosts. We implemented and characterized the \"repressilator\"6, a three-node negative feedback oscillator in vitro. We then used our cell-free framework to engineer novel three-node, four-node, and five-node negative feedback architectures going from the characterization of circuit components to the rapid analysis of complete networks. We validated our cell-free approach by transferring these novel three-node and five-node oscillators to Escherichia coli, resulting in robust and synchronized oscillations reflecting the in vitro observation. We demonstrate that comprehensive circuit engineering can be performed in a cell-free system and that the in vitro results have direct applicability in vivo. Cell-free synthetic biology thus has the potential to drastically speed up design-build-test cycles in biological engineering and enable the quantitative characterization of synthetic and natural networks.

Synthetic Biology↗

bioRxiv2018

Building mathematical models of biological systems with modelbase, a Python package for semi-automatic ODE assembly and construction of isotope-specific models

The modelbase package is a free expandable Python package for building and analysing dynamic mathematical models of biological systems. Originally it was designed for the simulation of metabolic systems, but it can be used for virtually any deterministic chemical processes. modelbase provides easy construction methods to define reactions and their rates. Based on the rates and stoichiometries, the system of differential equations is assembled automatically. modelbase minimises the constraints imposed on the user, allowing for easy and dynamic access to all variables, including derived ones, in a convenient manner. A simple incorporation of algebraic equations is, for example, convenient to study systems with rapid equilibrium or quasi steady-state approximations. Moreover, modelbase provides construction methods that automatically build all isotope-specific versions of a particular reaction, making it a convenient tool to analyse non-steady state isotope-labelling experiments.

systems biology↗

bioRxiv2018

Systems biology reveals NR2F6 and TGFB1 as key regulators of feed efficiency in beef cattle

Systems biology approaches are used as strategy to uncover tissue-specific perturbations and regulatory genes related to complex phenotypes. We applied this approach to study feed efficiency (FE) in beef cattle, an important trait both economically and environmentally. Poly-A selected RNA of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle and pituitary) of eighteen young bulls, selected for high and low FE, were sequenced (100bp, pared-end). From the 17,354 expressed genes, 1,317 were prioritized by five selection categories (differentially expressed, harbouring SNPs associated with FE, tissue-specific, secreted in plasma and key regulators) and used for network construction. NR2F6 and TGFB were identified and validated by motif discovery as key regulators of hepatic inflammatory response and muscle tissue development, respectively, two biological processes demonstrated to be associated to FE. Moreover, we indicated potential biomarkers of FE which are related to hormonal control of metabolism and sexual maturity. By using robust methodologies and validation strategies, we confirmed main biological processes related to FE in Bos indicus and indicated candidate genes as regulators or biomarkers of superior animals.

systems biology↗

bioRxiv2018

Identification of Key Proteins Involved in Axon Guidance Related Disorders: A Systems Biology Approach

Axon guidance is a crucial process for growth of the central and peripheral nervous systems. In this study, 3 axon guidance related disorders, namely-Duane Retraction Syndrome (DRS), Horizontal Gaze Palsy with Progressive Scoliosis (HGPPS) and Congenital fibrosis of the extraocular muscles type 3 (CFEOM3) were studied using various Systems Biology tools to identify the genes and proteins involved with them to get a better idea about the underlying molecular mechanisms including the regulatory mechanisms. Based on the analyses carried out, 7 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with DRS, HGPPS and CFEOM3 associated genes. From the PPI network, 3 have been identified as hub proteins-DRD2, UBC and CUL3.

systems biology↗

bioRxiv2018

An integrative systems biology and experimental approach identifies convergence of epithelial plasticity, metabolism, and autophagy to promote chemoresistance

The evolution of therapeutic resistance is a major cause of death for patients with solid tumors. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure induced by the host immune system. These ecological and selective forces often lead to evolutionary convergence on one or more pathways or hallmarks that drive progression. These hallmarks are, in turn, intimately linked to each other through gene expression networks. Thus, a deeper understanding of the evolutionary convergences that occur at the gene expression level could reveal vulnerabilities that could be targeted to treat therapy-resistant cancer. To this end, we used a combination of phylogenetic clustering, systems biology analyses, and wet-bench molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during TGF-{beta}-mediated epithelial-mesenchymal transition in a lung cancer model system. Phylogenetics analyses of gene expression data from TGF-{beta} treated cells revealed evolutionary convergence of cells toward amine-metabolic pathways and autophagy during TGF-{beta} treatment. Using high-throughput drug screens, we found that knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-{beta}-induced resistance, implicating autophagy as a TGF-{beta}-mediated chemoresistance mechanism. Analysis of publicly-available clinical data sets validated the adverse prognostic importance of ATG16L expression in multiple cancer types including kidney, lung, and colon cancer patients. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities.

cancer biology↗