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Integrative approaches for signalling and metabolic networks

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

Reconstruction of genome-scale human metabolic models using omics data

Investigating Moorella thermoacetica metabolism with a genome-scale constraint-based metabolic model

Invariants reveal multiple forms of robustness in bifunctional enzyme systems

An entropy-like index of bifurcational robustness for metabolic systems

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Predicting genetic interactions from Boolean models of biological networks

Sub-optimal phenotypes of double-knockout mutants of Escherichia coli depend on the order of gene deletions

Dynamic metabolic models in context: biomass backtracking

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Predicting genetic interactions from Boolean models of biological networks

Content Provider	Royal Society of Chemistry (RSC)
Author	Zinovyev, Andrei Barillot, Emmanuel Calzone, Laurence
Copyright Year	2015
Abstract	Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.
Starting Page	921
Ending Page	929
Page Count	9
File Format	HTM / HTML PDF
ISSN	17579694
Volume Number	7
Issue Number	8
Journal	Integrative Biology
DOI	10.1039/c5ib00029g
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Markov Gene Mutation Statistical model Boolean network Mathematical model Biological network Markov chain Stochastic process Phenotype Epistasis Dependent and independent variables
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry Biophysics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Predicting genetic interactions from Boolean models of biological networks

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Predicting genetic interactions from Boolean models of biological networks

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