Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

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.

Experimental determination of Escherichia coli biomass composition for constraint-based metabolic modeling

“…The BOF is a central pseudo-reaction in constraint-based metabolic models containing the metabolic precursors required for cellular replication and growth [ 9 , 15 , 20 , 21 , 51 , 52 ]. Currently, most E. coli BOFs can trace their lineage back to data compiled by Neidhardt in 1990 on the B/r strain [ 38 ].…”

“…High-quality, condition-dependent biomass measurements are therefore necessary in order to enable accurate phenotypic predictions using a constraint-based modeling framework. This realization has sparked multiple initiatives for the measurement of biomass compositions [ 15 , 16 , 19 , 20 ], and ways to address the challenge of integrating variable biomass compositions into GEMs [ 21 ].…”

Quantification of macromolecular biomass composition for constraint-based metabolic modeling

“…The BOF is a central pseudo-reaction in constraint-based metabolic models containing the metabolic precursors required for cellular replication and growth [9, 15, 20, 21, 50, 51]. Currently, most E. coli BOFs can trace their lineage back to data compiled by Neidhardt in 1990 [38]; it should be noted that these data are not for a single E. coli strain, and are partly based on measurements from a range of literature sources.…”

A study of a diauxic growth experiment using an expanded dynamic flux balance framework

“…An additional central component in GEMs is the representation of a biomass objective function (BOF), a pseudo-reaction which represents the metabolites needed for the cell to reproduce. Since it is a key component of these models, the BOF has recently been the target of increased interest in the field [2][3][4][5][6].…”

“…While the traditional approach has been to approximate it based on data compiled from literature, often from different strains or conditions, the subject has been the target of increasing interest in recent years [2][3][4][5][6]. Yet, much work remains to be done, both in terms of establishing protocols and analysis techniques [6], and in terms of a theoretical framework for integrating the resulting data [5] before we can reasonably expect BOFs to allow accurate predictions in varying and novel environments. Due to its central role in metabolism, the BOF is of special importance when trying to quantitatively predict metabolic behavior.…”