Abstract
Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.
Highlights
The constraint-based reconstruction and analysis (COBRA) framework allows for the systemlevel analysis of genome-scale metabolism in microbes [1]
We find that the Biomass Tradoff Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP) formulations have a significant impact on model performance and phenotypes
We explore the ramifications of BTW and HIP using the Escherichia coli model iML1515 [20] with three artificial, yet biologically plausible, biomass compositions across varying glucose and ammonium uptake rates
Summary
The constraint-based reconstruction and analysis (COBRA) framework allows for the systemlevel analysis of genome-scale metabolism in microbes [1]. In its simplest formulations, a COBRA approach such as flux balance analysis (FBA) is based on a set of linear equations corresponding to biochemical reactions, for which reasonable biological constraints are applied. This set of mathematical relations is subsequently converted into a linear program that is optimized with regards to a biologically plausible objective [1]. This objective is chosen to be the biomass objective function (BOF); a pseudoreaction that utilizes the cellular metabolic network to consume nutrient resources. The use of BOF as a cellular objective is a reasonable assumption for many microbes, as an organism’s ability to quickly replicate is a property that often provides a fitness benefit [4, 5]
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