Using Genome-scale Models to Predict Biological Capabilities

Abstract

Constraint-based reconstruction and analysis (COBRA) methods at the genome-scale have been under development since the first whole genome sequences appeared in the mid-1990s. A few years ago this approach began to demonstrate the ability to predict a range of cellular functions including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome-scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This primer will get you started.

Modelling sponge-symbiont metabolism

“…I have chosen to apply the constrained-based modelling approach (Lewis et al, 2012) to the dual-species model developed in this chapter. Constraintbased modelling allows the prediction of metabolic phenotypes, such as growth rates and the flow of material through the network, under defined conditions (O'Brien et al, 2015). The advantages of being able to simulate the effects of different nutrient conditions, or of gene deletions and additions, have made constraint-based modelling an indispensable tool for commercial metabolic engineering (e.g.…”

“…Constraints are applied that define the conditions to which the network must conform for a particular simulation (Lewis et al, 2012;O'Brien et al, 2015). This allows the user to reduce the solution space and, importantly, to place the model into a relevant biological context.…”

“…How metabolic models are constructed and analysed is explained in detail in Chapter 5 but briefly, a GEM is a mathematical representation of the metabolic network of an organism based on the enzymatic, transport and other biochemical capabilities encoded within the genome of that organism (O'Brien et al, 2015). The metabolic network is represented by the metabolite stoichiometry of the biochemical reactions identified in the genome (for detailed protocols see Feist & Palsson, 2010;Santos et al, 2011;Schellenberger et al, 2011;.…”

“…The inherent complexity of a metabolic network means that there are many possible solutions to the same problem; this is termed the solution space (Orth et al, 2010;O'Brien et al, 2015). Constraints are applied that define the conditions to which the network must conform for a particular simulation (Lewis et al, 2012;O'Brien et al, 2015).…”

“…The model predictions can then be tested experimentally, with the results used to refine and improve the model (e.g. Heinken et al, 2014;Orth et al, 2011;O'Brien et al, 2015).…”