Understanding FBA Solutions under Multiple Nutrient Limitations

Abstract

Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes.

Global sensitivity analysis for investigation of bacterial physiology in complex media with FBA-PRCC

“…What is common between all such reconstructions, as was noted by Van Pelt-KleinJan with coauthors (Van Pelt-KleinJan, De Groot, and Teusink 2021), is that all of them are based on experimental flux data from limited media growth. This is reasonable from experimental point of view, as single carbon source experiment allow to compare experimental growth rate with essentiality data from the model.…”

“…The interaction between pathways utilizing different substrates make solution space more complex, and increase number of non-unique solutions to the optimization FBA problem. In (Van Pelt-KleinJan, De Groot, and Teusink 2021) authors propose the enumeration of Elementary Conversion Modes technique to overcome that limitation. In this paper we propose the use of alternative approach (Sorokin and Goryanin 2023), which is based on sampling flux space and global sensitivity analysis (GSA), for analysis of the GEM FBA solution space in the nutrient-rich environment.…”

Multi-Objective Optimization of Microalgae Metabolism: An Evolutive Algorithm Based on FBA

“…This situation becomes more complex in simultaneous bioproducts optimization. A recent trend that works in metabolic analysis involves optimizing several objectives to engage in the study of more than one bioproduct of interest [ 21 , 22 , 23 ]. In the past decade, this method can be traced back to the work of Zomorrodi and Maranas [ 24 ].…”

“…Budinich et al [ 21 ] extend FBA for microbial communities by defining a Multi-Objective FBA (MOFBA) in order to study multiple trade-offs between nutrients and growth rates. More recently, Andrade et al [ 22 ] and Pelt-KleinJan [ 23 ] proposes a multi-objective formulation of FBA that considers nutrient limitations for metabolic analysis.…”