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Documents authored by Besançon, Mathieu

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DOI: 10.4230/artifacts.23824
hannahtro/Loopless_Fluxes_with_Mixed_Integer_Optimization

Authors: Hannah Troppens, Mathieu Besançon, St. Elmo Wilken, and Sebastian Pokutta

Abstract
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Hannah Troppens, Mathieu Besançon, St. Elmo Wilken, Sebastian Pokutta. hannahtro/Loopless_Fluxes_with_Mixed_Integer_Optimization (Software). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-23824,
   title = {{hannahtro/Loopless\underlineFluxes\underlinewith\underlineMixed\underlineInteger\underlineOptimization}}, 
   author = {Troppens, Hannah and Besan\c{c}on, Mathieu and Wilken, St. Elmo and Pokutta, Sebastian},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:9c41495c7f2ecd9e7459fa0c51d4be32fa0c033e}{\texttt{swh:1:dir:9c41495c7f2ecd9e7459fa0c51d4be32fa0c033e}} (visited on 2025-07-15)},
   url = {https://github.com/hannahtro/Loopless_Fluxes_with_Mixed_Integer_Optimization},
   doi = {10.4230/artifacts.23824},
}
Document
DOI: 10.4230/LIPIcs.SEA.2025.26
Mixed-Integer Optimization for Loopless Flux Distributions in Metabolic Networks

Authors: Hannah Troppens, Mathieu Besançon, St. Elmo Wilken, and Sebastian Pokutta

Published in: LIPIcs, Volume 338, 23rd International Symposium on Experimental Algorithms (SEA 2025)

Abstract
Constraint-based metabolic models can be used to investigate the intracellular physiology of microorganisms. These models couple genes to reactions, and typically seek to predict metabolite fluxes that optimize some biologically important metric. Classical techniques, like Flux Balance Analysis (FBA), formulate the metabolism of a microbe as an optimization problem where growth rate is maximized. While FBA has found widespread use, it often leads to thermodynamically infeasible solutions that contain internal cycles (loops). To address this shortcoming, Loopless-Flux Balance Analysis (ll-FBA) seeks to predict flux distributions that do not contain these loops. ll-FBA is a disjunctive program, usually reformulated as a mixed-integer program, and is challenging to solve for biological models that often contain thousands of reactions and metabolites. In this paper, we compare various reformulations of ll-FBA and different solution approaches. Overall, the combinatorial Benders' decomposition is the most promising of the tested approaches with which we could solve most instances. However, the model size and numerical instability pose a challenge to the combinatorial Benders' method.
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Hannah Troppens, Mathieu Besançon, St. Elmo Wilken, and Sebastian Pokutta. Mixed-Integer Optimization for Loopless Flux Distributions in Metabolic Networks. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 26:1-26:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{troppens_et_al:LIPIcs.SEA.2025.26,
  author =	{Troppens, Hannah and Besan\c{c}on, Mathieu and Wilken, St. Elmo and Pokutta, Sebastian},
  title =	{{Mixed-Integer Optimization for Loopless Flux Distributions in Metabolic Networks}},
  booktitle =	{23rd International Symposium on Experimental Algorithms (SEA 2025)},
  pages =	{26:1--26:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-375-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{338},
  editor =	{Mutzel, Petra and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2025.26},
  URN =		{urn:nbn:de:0030-drops-232646},
  doi =		{10.4230/LIPIcs.SEA.2025.26},
  annote =	{Keywords: Systems biology, mixed-integer optimization, disjunctive optimization, flux balance analysis}
}
Document
DOI: 10.4230/LIPIcs.SEA.2024.16
Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods

Authors: Deborah Hendrych, Mathieu Besançon, and Sebastian Pokutta

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)

Abstract
We tackle the Optimal Experiment Design Problem, which consists of choosing experiments to run or observations to select from a finite set to estimate the parameters of a system. The objective is to maximize some measure of information gained about the system from the observations, leading to a convex integer optimization problem. We leverage Boscia.jl, a recent algorithmic framework, which is based on a nonlinear branch-and-bound algorithm with node relaxations solved to approximate optimality using Frank-Wolfe algorithms. One particular advantage of the method is its efficient utilization of the polytope formed by the original constraints which is preserved by the method, unlike alternative methods relying on epigraph-based formulations. We assess our method against both generic and specialized convex mixed-integer approaches. Computational results highlight the performance of our proposed method, especially on large and challenging instances.
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Deborah Hendrych, Mathieu Besançon, and Sebastian Pokutta. Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 16:1-16:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{hendrych_et_al:LIPIcs.SEA.2024.16,
  author =	{Hendrych, Deborah and Besan\c{c}on, Mathieu and Pokutta, Sebastian},
  title =	{{Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{16:1--16:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.16},
  URN =		{urn:nbn:de:0030-drops-203810},
  doi =		{10.4230/LIPIcs.SEA.2024.16},
  annote =	{Keywords: Mixed-Integer Non-Linear Optimization, Optimal Experiment Design, Frank-Wolfe, Boscia}
}
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