Predicting Gene Essentiality Using Genome-Scale in Silico Models

Abstract

Genome-scale metabolic models of organisms can be reconstructed using annotated genome sequence information, well-curated databases, and primary research literature. The metabolic reaction stoichiometry and other physicochemical factors are incorporated into the model, thus imposing constraints that represent restrictions on phenotypic behavior. Based on this premise, the theoretical capabilities of the metabolic network can be assessed by using a mathematical technique known as flux balance analysis (FBA). This modeling framework, also known as the constraint-based reconstruction and analysis approach, differs from other modeling strategies because it does not attempt to predict exact network behavior. Instead, this approach uses known constraints to separate the states that a system can achieve from those that it cannot. In recent years, this strategy has been employed to probe the metabolic capabilities of a number of organisms, to generate and test experimental hypotheses, and to predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the constraint-based modeling approach and focuses on its application to computationally predicting gene essentiality.

Metabolic Network Analysis-Based Identification of Antimicrobial Drug Targets in Category A Bioterrorism Agents

“…As there is no measured or predicted biomass composition published for F. tularensis , for this organism we used the biomass components of Gram-negative E. coli MG1655 strain [15], [16]. Similarly, for B. anthracis , we used a close relative Gram-positive Bacillus subtilis strain 168 [33] and for Y. pestis , we again used E. coli MG1655 biomass components [15], [16], [17], [18].…”

“…Identifying gene essentiality by experimental approaches either using transposon mutagenesis or RNA silencing is time consuming and expensive, and the results are strain-specific. In contrast, computational methods provide an alternate approach for the identification of single essential and synthetic lethal metabolic enzymes [10], [15], [16], [17], [18] that can be simultaneously tested for multiple strains [16], [19]. These methods can be also tested simultaneously under several growth conditions and identify organism/strain specific essential metabolic enzymes as common drug targets.…”

Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks

“…Many studies suggested that highly connected nodes or “hubs” are more likely to be essential (Hahn and Kern, 2005; Joyce and Palsson, 2008). For instance, in 2005, Hann and Kern compared centrality and essentiality in yeast, worm and fly PPI networks and concluded that a protein connectivity has an effect on the probability of being essential (Hahn and Kern, 2005).…”

Genome-scale modeling for metabolic engineering

“…Targets for possible genetic manipulation to improve strain performance can be identified through comparative studies under both genetic and environmental perturbations. The model can then be used to calculate knockout lethality or growth rates, and results can be compared to experimental observations, which allows for the model to be iteratively tested and improved [40]. Several computational approaches for network manipulation and phenotypic simulation have been developed, such as the COBRA Toolbox for MATLAB [10], a popular FBA simulator.…”