A COMBINATORIAL OPTIMISATION APPROACH TO MULTI-FACTORIAL GAP-FILLING IN GENOME-SCALE METABOLIC MODELS (GEMS)
This paper discusses a method for improving models that simulate how organisms metabolize nutrients. It focuses on filling in gaps in these models where data is missing, using a new computational approach that works across multiple conditions at once.
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- 1 This is done by extending FBA to explore which additional reactions better contribute to such objective.
- 2 Each iteration applies a small modification to (\u0177, c), resolves pFBA independently for all media, evaluates the multi-component objective, and updates (i) the best solution seen, (ii) the incumbent, and (iii) the Pareto archive.
- 3 Genome-Scale Metabolic Models (GEMs) aim to capture these capabilities as a network of metabolic reactions (metabolic network), enabling computational prediction of phenotypes under genetic and nutritional changes, such as growth under defined media.
- 4 We treat the problem as a multi-objective discrete optimisation problem and solve it using a metaheuristic search strategy that explores sets of reactions to incorporate into a given GEM.
Introduction
An organism’s metabolic capabilities are largely determined by its genome, which encodes enzymes that catalyse, sometimes unique, biochemical reactions required to sustain life. This information can then be applied to strain and bioprocess engineering approaches -for instance whether a particular change will be effective in promoting more efficient breakdown of plastic .
A key challenge in constructing a GEM is gap-filling: identifying reactions that are used by the organism but are not linked to a known gene (non-gene associated reactions).
In the case of microbes, gap-filling is commonly guided by empirical growth measurements across many media conditions.
If a selection algorithm cannot be applied (e.g., Add random when no reactions are excluded), we resample.
Research Question
Genome-Scale Metabolic Models (GEMs) aim to capture these capabilities as a network of metabolic reactions (metabolic network), enabling computational prediction of phenotypes under genetic and nutritional changes, such as growth under defined media. We treat the problem as a multi-objective discrete optimisation problem and solve it using a metaheuristic search strategy that explores sets of reactions to incorporate into a given GEM.
\u2022 We propose objective functions that capture both plausibility of added reactions and agreement with empirical growth outcomes.
This is done by extending FBA to explore which additional reactions better contribute to such objective.
Methodology
Growth in a defined medium can be simulated using GEMs through Flux Balance Analysis (FBA), which optimises an objective (often biomass production), subject to network capacity constraints. \u2022 We present a multi-objective metaheuristic method to solve the resulting discrete optimisation problem.
Study Design
\u2022 We evaluate this method on three organisms, and compare the results with baseline GEMs constructed by a standard method.
Flux Balance Analysis (FBA) predicts feasible reaction fluxes x i through the metabolic network under a specified medium by enforcing (approximate) steady-state mass balance and reaction capacity constraints while optimising an objective, conventionally biomass production.
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Results & Findings
When predictions do not correlate well with observed growth across one or more media, the reaction set in the current GEM is likely incomplete, motivating gap-filling. This can be informed by evolutionary relatedness: if a close relative is known to use a reaction, it is more likely to be present in the organism under study.
- When predictions do not correlate well with observed growth across one or more media, the reaction set in the current GEM is likely incomplete, motivating gap-filling.
- This can be informed by evolutionary relatedness: if a close relative is known to use a reaction, it is more likely to be present in the.
- We quantify this evidence using taxonomic distance, computed from a custom taxonomic tree of a defined set of bacteria (Appendix A.1), and use it to assign.
- Solution quality is evaluated using multiple criteria capturing reaction plausibility and agreement with the measured outcomes, including growth\/no-growth consistency and quantitative prediction accuracy.
- In practice, these outputs are intended to support iterative refinement, allowing domain expertise to be reflected through reaction costs and run parameters.
Future work includes evaluating alternative plausibility models (e.g., phylogenetic similarity) and integrating complementary constraints (e.g., enzyme and proteome allocation dynamics) to further reduce unrealistic flux distributions and improve predictive accuracy.
This can be informed by evolutionary relatedness: if a close relative is known to use a reaction, it is more likely to be present in the organism under study.
Practical Applications
In this study, the AGORA2 database constitutes the candidate pool of reactions from which gap-filling may select additions.
GEMs and FBA
This section explains GEMs as representations of an organism’s metabolism through biochemical reactions. It describes how Flux Balance Analysis (FBA) is used to simulate growth and predict feasible reaction fluxes under specified media, emphasizing the importance of steady-state mass balance.
Data sources and growth measurements
The study utilizes the AGORA2 database for candidate reactions and empirical growth measurements from bacterial strains across multiple media. It discusses how growth is quantified and the challenges of using absolute growth scores due to noise and platform dependency.
The gap-filling problem
This section outlines the gap-filling problem, which aims to augment a GEM’s reaction set to better align simulated growth with experimental observations. It details the selection of candidate reactions and the cost associated with each reaction based on taxonomic distance.
Frequently Asked Questions
This is done by extending FBA to explore which additional reactions better contribute to such objective. Each iteration applies a small modification to (\u0177, c), resolves pFBA independently for all media, evaluates the multi-component objective, and updates (i) the best solution seen.
\u2022 We evaluate this method on three organisms, and compare the results with baseline GEMs constructed by a standard method. The method is multi-objective and returns a Pareto set of candidate GEMs, allowing users to choose models that best trade off plausibility.
This can be informed by evolutionary relatedness: if a close relative is known to use a reaction, it is more likely to be present in the organism under study. In practice, these outputs are intended to support iterative refinement, allowing domain expertise.
For our simulations, we use parsimonious FBA (pFBA), which favours solutions achieving growth with minimal overall flux, often used as a proxy for metabolic efficiency . In this study, the AGORA2 database constitutes the candidate pool of reactions from which gap-filling may.
If a selection algorithm cannot be applied (e.g., Add random when no reactions are excluded), we resample. Future work includes evaluating alternative plausibility models (e.g., phylogenetic similarity) and integrating complementary constraints (e.g., enzyme and proteome allocation dynamics) to further reduce unrealistic flux.
This paper discusses a method for improving models that simulate how organisms metabolize nutrients. It focuses on filling in gaps in these models where data is missing, using a new computational approach that works across multiple conditions at once.