An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results

Church, George M

Dudley, Aimée M

Janse, Daniel M

Segrè, Daniel

Snitkin, Evan S

Wong, Kaisheen

Genome Biology

English

PubMed

An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results. BACKGROUND. Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored. RESULTS. In this study, we measured growth phenotypes of 465 Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes. CONCLUSIONS. Our study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources.National Human Genome Institute, National Institute of Health (K22 HG002908); United States Department of Energy; National Institutes of Health, NASA Astrobiology Institut

Snitkin, Evan S.

Dudley, Aimée M.

Janse, Daniel M.

Church, George M.

Name not available

Model-Driven Analysis of Experimentally Determined Growth Phenotypes for 465 Yeast Gene Deletion Mutants Under 16 Different Conditions

Evan S Snitkin

Aimée M Dudley

Daniel M Janse

Kaisheen Wong

George M Church

Daniel Segrè

Springer - Publisher Connector

Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions

Crossref

Background: Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such  integration have only begun to be explored. Results: In this study, we measured growth phenotypes of 465 Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between  experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes.  Conclusions: Our study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources

Church, George McDonald

Harvard University - DASH 

Boston University Institutional Repository (OpenBU)

BACKGROUND: Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored.
RESULTS: In this study, we measured growth phenotypes of 465 Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes.
CONCLUSIONS: Our study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources.AMD is supported by a Genome Scholar and Faculty Transition award (K22 HG002908) from the NIH/NHGRI. DS acknowledges support by the US Department of Energy, the National Institute of Health and the NASA Astrobiology Institute. The authors would also like to thank Zhenjun Hu for help with flux visualization in VisANT and Dan Fraenkel for comments on the manuscript. It is with sadness that we report the premature passing of our co-author Kaisheen Wong, who participated enthusiastically and productively in this project as a high school student. (AMD; K22 HG002908 - Genome Scholar and Faculty Transition award; NIH/NHGRI; US Department of Energy; National Institute of Health; NASA Astrobiology Institute)Published versio

Dudley, Aimee M.

Segre, Daniel

A review of phenotypes in Saccharomyces cerevisiae. Yeast

Bayesian-based selection of metabolic objective functions. Bioinformatics

Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nat Genet

Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol

DA: Metabolic modeling of a mutualistic microbial community. Mol Syst Biol

Dandekar T: Integrated network reconstruction, visualization and analysis using YANAsquare.

Delneri D: Plasticity of genetic interactions in metabolic networks of yeast.

Divergent evolution of pyrimidine biosynthesis between anaerobic and aerobic yeasts.

Ensemble modeling for analysis of cell signaling dynamics. Nat Biotechnol

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature

Extracellular melibiose and fructose are intermediates in raffinose catabolism during fermentation to ethanol by engineered enteric bacteria.

F: Metabolic network visualization eliminating node redundance and preserving metabolic pathways.

FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps. Bioinformatics

From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res

Frontali L: Large-scale phenotypic analysis reveals identical contributions to cell functions of known and unknown yeast genes. Yeast

Functional profiling of the Saccharomyces cerevisiae genome. Nature

Genealogy of principal strains of the yeast genetic stock center. Genetics

Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol

Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res

Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell

GM: A global view of pleiotropy and phenotypically derived gene function in yeast. Mol Syst Biol

GM: Analysis of optimality in natural and perturbed metabolic networks.

Herrgard MJ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc

Horizontal gene transfer promoted evolution of the ability to propagate under anaerobic conditions in yeasts. Mol Genet Genomics

Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comput Biol

Integrating high-throughput and computational data elucidates bacterial networks.

Interactive Flux Visualization Tool

JS: Advances in flux balance analysis. Curr Opin Biotechnol

Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast. Genome Biol

Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. Omics

Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes.

LD: Chance and necessity in the evolution of minimal metabolic networks.

LD: Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast. Nature

LM: Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Res

Maranas CD: Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol Bioeng

Maranas CD: Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng

Mating type determination and mating-type interconversion in Saccharomyces cerevisiae.

Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat Genet

Metabolic network structure determines key aspects of functionality and regulation. Nature

Mirny LA: Using the topology of metabolic networks to predict viability of mutant strains.

MJ: Widespread aneuploidy revealed by DNA microarray expression profiling. Nat Genet

Modular epistasis in yeast metabolism. Nat Genet

Nevalainen KM: Comparison of melibiose utilizing baker's yeast strains produced by genetic engineering and classical breeding. Lett Appl Microbiol

Palsson BO: Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA

Palsson BO: In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol

Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol

Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res

Robyt JF: Specificity of yeast (Saccharomyces cerevisiae) in removing carbohydrates by fermentation. Carbohydr Res

Ruppin E: Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nat Genet

RW: Systematic screen for human disease genes in yeast. Nat Genet

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc Natl Acad Sci USA

Saccharomyces Genome Database [http://www.yeastge nome.org/]

Schilling CH: The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng

Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science

The CDS1 gene encoding CDP-diacylglycerol synthase in Saccharomyces cerevisiae is essential for cell growth.

The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities.

The generalized flux-minimization method and its application to metabolic networks affected by enzyme deficiencies. Biosystems

The Pathway Tools software. Bioinformatics

The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks.

The role of population size, pleiotropy and fitness effects of mutations in the evolution of overlapping gene functions. Genetics

The transcriptional regulator Hap1p (Cyp1p) is essential for anaerobic or hemedeficient growth of Saccharomyces cerevisiae: Genetic and molecular characterization of an extragenic suppressor that encodes a WD repeat protein. Genetics

Thinking about genetic redundancy. Trends Genet

U: Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol

Unravelling the regulatory structure of biochemical networks using stimulus response experiments and large-scale model selection. Syst Biol (Stevenage)

VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res

Westhead DR: metaSHARK: a WWW platform for interactive exploration of metabolic networks.

http://creativecommons.org/licenses/by/2.0

Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions

Abstract

Similar works

Full text

Available Versions

Name not available

Springer - Publisher Connector

Crossref

Harvard University - DASH

Springer - Publisher Connector

Boston University Institutional Repository (OpenBU)

Boston University Institutional Repository (OpenBU)