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FBApro: A Fast, Simple Linear Transformation for Diverse Metabolic Modeling Tasks

Authors Ariel Bruner , Mona Singh

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LIPIcs.WABI.2026.26.pdf
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ACM Subject Classification
  • Applied computing → Systems biology
Keywords
  • metabolic modeling
  • flux balance analysis
  • constraint-based metabolic modeling

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Abstract

Constraint-based metabolic modeling is the predominant framework for simulating cellular metabolism. The central assumption of these models is that metabolism operates at a steady state, meaning that the production and consumption rates of each metabolite are balanced. This assumption imposes linear constraints on the fluxes of biochemical reactions. Flux Balance Analysis (FBA), a fundamental method in the field, is formulated as an optimization problem maximizing a cellular objective (e.g., growth) over the resulting linear subspace of steady state fluxes. Many other methods in the field are expressed either as a modification to FBA, or use FBA as a black box within an algorithm. Here, we propose a general alternative to optimization called FBApro. For any given vector of reference fluxes, FBApro finds the closest flux vector within the steady-state subspace, and accounts for both partially given reference fluxes and exact constraints on reactions. While FBApro is the solution to a quadratic program, we show that it can be implemented as a single linear operation using orthogonal projections to corresponding affine spaces and sets of linear equations. The overall approach is computationally efficient, does not require a cellular objective, and is easy to implement. We formally derive the closed-form expressions for FBApro and simpler variants, and validate it on both synthetic and real cancer cell line data.
Code availability. The code implementing FBApro is available at https://github.com/Singh-Lab/FBApro. All code required to reproduce the figures in the paper is available, although the data used must be sourced separately. The repository also contains toy models and examples.

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Ariel Bruner and Mona Singh. FBApro: A Fast, Simple Linear Transformation for Diverse Metabolic Modeling Tasks. In 26th International Conference on Algorithms for Bioinformatics (WABI 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 390, pp. 26:1-26:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.WABI.2026.26

Author Details

Ariel Bruner
  • Princeton University, NJ, USA
Mona Singh
  • Princeton University, NJ, USA

Funding

This work was supported in part by the Ludwig Institute for Cancer Research, Princeton Branch and NIH grant R01-CA208148.

Supplementary Materials

References

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