Dataset Information

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks.

ABSTRACT: In analyzing and mathematical modeling of complex (bio)chemical reaction networks, formal methods that connect network structure and dynamic behavior are needed because often, quantitative knowledge of the networks is very limited. This applies to many important processes in cell biology. Chemical reaction network theory allows for the classification of the potential network behavior-for instance, with respect to the existence of multiple steady states-but is computationally limited to small systems. Here, we show that by analyzing subnetworks termed elementary flux modes, the applicability of the theory can be extended to more complex networks. For an example network inspired by cell cycle control in budding yeast, the approach allows for model discrimination, identification of key mechanisms for multistationarity, and robustness analysis. The presented methods will be helpful in modeling and analyzing other complex reaction networks.

SUBMITTER: Conradi C

PROVIDER: S-EPMC2148257 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks.

Conradi Carsten C Flockerzi Dietrich D Raisch Jörg J Stelling Jörg J

Proceedings of the National Academy of Sciences of the United States of America 20071127 49

In analyzing and mathematical modeling of complex (bio)chemical reaction networks, formal methods that connect network structure and dynamic behavior are needed because often, quantitative knowledge of the networks is very limited. This applies to many important processes in cell biology. Chemical reaction network theory allows for the classification of the potential network behavior-for instance, with respect to the existence of multiple steady states-but is computationally limited to small sys ...[more]

PMID: 18042723

Similar Datasets

Project description:The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them.Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups.The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Project description:The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered-perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

Project description:We report the analysis of a complex enveloped human virus, herpes simplex virus (HSV), assembled after in vivo incorporation of bio-orthogonal methionine analogues homopropargylglycine (HPG) or azidohomoalanine (AHA). We optimised protocols for the production of virions incorporating AHA (termed HSVAHA), identifying conditions which resulted in normal yields of HSV and normal particle/pfu ratios. Moreover we show that essentially every single HSVAHA capsid-containing particle was detectable at the individual particle level by chemical ligation of azide-linked fluorochromes to AHA-containing structural proteins. This was a completely specific chemical ligation, with no capsids assembled under normal methionine-containing conditions detected in parallel. We demonstrate by quantitative mass spectrometric analysis that HSVAHA virions exhibit no qualitative or quantitative differences in the repertoires of structural proteins compared to virions assembled under normal conditions. Individual proteins and AHA incorporation sites were identified in capsid, tegument and envelope compartments, including major essential structural proteins. Finally we reveal novel aspects of entry pathways using HSVAHA and chemical fluorochrome ligation that were not apparent from conventional immunofluorescence. Since ligation targets total AHA-containing protein and peptides, our results demonstrate the presence of abundant AHA-labelled products in cytoplasmic macrodomains and tubules which no longer contain intact particles detectable by immunofluorescence. Although these do not co-localise with lysosomal markers, we propose they may represent sites of proteolytic virion processing. Analysis of HSVAHA also enabled the discrimination from primary entering from secondary assembling virions, demonstrating assembly and second round infection within 6 hrs of initial infection and dual infections of primary and secondary virus in spatially restricted cytoplasmic areas of the same cell. Together with other demonstrated applications e.g., in genome biology, lipid and protein trafficking, this work further exemplifies the utility and potential of bio-orthogonal chemistry for studies in many aspects of virus-host interactions.

Dataset Information

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks.

Publications

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets