scholarly journals Predicting genetic interactions from Boolean models of biological networks

2015 ◽  
Author(s):  
Laurence Calzone ◽  
Emmanuel Barillot ◽  
Andrei Zinovyev
Keyword(s):  
Biological Networks ◽  
Genetic Interaction ◽  
Initial Conditions ◽  
Genetic Interactions ◽  
Interaction Networks ◽  
Stochastic Simulations ◽  
Model Organisms ◽  
Loss Of Function ◽  
Boolean Models ◽  
Computational Tools

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 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 phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

scholarly journals Predicting genetic interactions from Boolean models of biological networks

2015 ◽  
Vol 7 (8) ◽  
pp. 921-929 ◽  
Author(s):  
Laurence Calzone ◽  
Emmanuel Barillot ◽  
Andrei Zinovyev
Keyword(s):  
Cell Fate ◽  
Biological Networks ◽  
Decision Model ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interactions ◽  
Cell Fate Decision ◽  
Boolean Models ◽  
Genetic Interaction Network ◽  
Fate Decision

The network representation of the cell fate decision model (Calzoneet al., 2010) is used to generate a genetic interaction network for the apoptosis phenotype. Most genetic interactions are epistatic, single nonmonotonic, and additive (Dreeset al., 2005).

scholarly journals Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions

2019 ◽  
Author(s):  
Christopher J. Lord ◽  
Niall Quinn ◽  
Colm J. Ryan
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interactions ◽  
Molecular Heterogeneity ◽  
Loss Of Function ◽  
Driver Genes ◽  
Lethal Effects ◽  
Synthetic Lethal

AbstractGenetic interactions, such as synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Understanding which genetic interactions are robust in the face of the molecular heterogeneity observed in tumours and what factors influence this robustness could streamline the identification of therapeutic targets. Here, we develop a computational approach to identify robust genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. We used this approach to evaluate >140,000 potential genetic interactions involving cancer driver genes and identified 1,520 that are significant in at least one study but only 220 that reproduce across multiple studies. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions in cancer are enriched for gene pairs whose protein products physically interact. This suggests that protein-protein interactions can be used not only to understand the mechanistic basis of genetic interaction effects, but also to prioritise robust targets for further development. To explore the utility of this approach, we used a protein-protein interaction network to guide the search for robust synthetic lethal interactions associated with passenger gene alterations and validated two novel robust synthetic lethalities.

scholarly journals Towards an Integrated Map of Genetic Interactions in Cancer Cells

2017 ◽  
Author(s):  
Benedikt Rauscher ◽  
Florian Heigwer ◽  
Luisa Henkel ◽  
Thomas Hielscher ◽  
Oksana Voloshanenko ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Genetic Variants ◽  
Genetic Interaction ◽  
Human Cancer ◽  
Genetic Network ◽  
Genetic Interactions ◽  
Model Organisms ◽  
Genetic Screens ◽  
New Genotype ◽  
Analytical Approaches

ABSTRACTCancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype-dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/β-catenin signaling identifiedGANABandPRKCSHas new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

scholarly journals Detecting genetic interactions using parallel evolution in experimental populations

2019 ◽  
Vol 374 (1777) ◽  
pp. 20180237 ◽  
Author(s):  
Kaitlin J. Fisher ◽  
Sergey Kryazhimskiy ◽  
Gregory I. Lang
Keyword(s):  
Experimental Evolution ◽  
Genetic Interaction ◽  
Parallel Evolution ◽  
Genetic Interactions ◽  
Computational Method ◽  
Loss Of Function ◽  
Systematic Analysis ◽  
Individual Gene ◽  
Double Deletion ◽  
Functional Variants

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae . Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-loss-of-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84 , genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. This article is part of the theme issue ‘Convergent evolution in the genomics era: new insights and directions’.

scholarly journals Hierarchical organization of the human cell from a cancer coessentiality network

2018 ◽  
Author(s):  
Eiru Kim ◽  
Merve Dede ◽  
Walter F. Lenoir ◽  
Gang Wang ◽  
Sanjana Srinivasan ◽  
...  
Keyword(s):  
Cell Lines ◽  
Gene Function ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Hierarchical Organization ◽  
Model Organisms ◽  
Function Modules ◽  
Strong Inference ◽  
Highly Correlated ◽  
Insight Into

AbstractGenetic interactions mediate the emergence of phenotype from genotype. Systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Systematic surveys of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains, and similarly implies shared biological function. We constructed a network of genes with correlated fitness profiles across 400 CRISPR knockout screens in cancer cell lines into a “coessentiality network,” with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of human gene function. Modules in the network are connected in a layered web that gives insight into the hierarchical organization of the cell.

scholarly journals Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interaction networks

2017 ◽  
Author(s):  
Scott W. Simpkins ◽  
Justin Nelson ◽  
Raamesh Deshpande ◽  
Sheena C. Li ◽  
Jeff S. Piotrowski ◽  
...  
Keyword(s):  
Biological Process ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Chemical Compounds ◽  
Genetic Interactions ◽  
Interaction Networks ◽  
Biological Processes ◽  
Functional Information ◽  
Chemical Genetic ◽  
Genetic Interaction Network

AbstractChemical-genetic interactions – observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes – contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.

scholarly journals Exploring a local genetic interaction network using evolutionary replay experiments

2021 ◽  
Author(s):  
Ryan C. Vignogna ◽  
Sean W. Buskirk ◽  
Gregory I. Lang
Keyword(s):  
Experimental Evolution ◽  
Gene Deletion ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interactions ◽  
Loss Of Function ◽  
Positive Allele ◽  
Genetic Interaction Network ◽  
Allele Specific ◽  
Yeast Genetic

ABSTRACTUnderstanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally-evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, and sheds light on a previously under-explored region of the yeast genetic interaction network.

scholarly journals A network of human functional gene interactions from knockout fitness screens in cancer cells

2019 ◽  
Vol 2 (2) ◽  
pp. e201800278 ◽  
Author(s):  
Eiru Kim ◽  
Merve Dede ◽  
Walter F Lenoir ◽  
Gang Wang ◽  
Sanjana Srinivasan ◽  
...  
Keyword(s):  
Cell Lines ◽  
Gene Function ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Functional Gene ◽  
Model Organisms ◽  
Modular Organization ◽  
Gene Interactions ◽  
Strong Inference ◽  
Highly Correlated

Genetic interactions mediate the emergence of phenotype from genotype. The systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Such assays of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains and similarly imply shared biological function. We constructed a network of genes with correlated fitness profiles across 276 high-quality CRISPR knockout screens in cancer cell lines into a “coessentiality network,” with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of gene function and highlighting the modular organization of the cell.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

2020 ◽  
Vol 27 (4) ◽  
pp. 265-278 ◽  
Author(s):  
Ying Han ◽  
Liang Cheng ◽  
Weiju Sun
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Comprehensive Information ◽  
Protein Interaction Prediction ◽  
Or Gene ◽  
Experimental Findings

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

2020 ◽  
Vol 9 (3) ◽  
pp. 177-191
Author(s):  
Sridharan Priya ◽  
Radha K. Manavalan
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Computational Models ◽  
Genetic Interaction ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

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