# Proceedings of the 23rd International Conference on Computational Methods in Systems Biology

François Fages, Sabine Pérès

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François Fages, Sabine Pérès. Proceedings of the 23rd International Conference on Computational Methods in Systems Biology. CMSB 2025, Sep 2025, Lyon, France. Lecture Notes in Computer Science, Lecture Notes in Computer Science (LNCS-15959), Springer Nature Switzerland, 2025, Proceedings of the 23rd International Conference on Computational Methods in Systems Biology, CMSB 2025., 10.1007/978-3-032-01436-8. hal-05396349

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**Series Editors**

Sorin Istrail, *Brown University, Providence, RI, USA*

Pavel Pevzner, *University of California, San Diego, CA, USA*

Michael Waterman, *University of Southern California, Los Angeles, CA, USA*

**Editorial Board Members**

Søren Brunak, *Technical University of Denmark, Kongens Lyngby, Denmark*

Mikhail S. Gelfand, *IITP, Research and Training Center on Bioinformatics, Moscow, Russia*

Thomas Lengauer, *Max Planck Institute for Informatics, Saarbrücken, Germany*

Satoru Miyano, *University of Tokyo, Tokyo, Japan*

Eugene Myers, *Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany*

Marie-France Sagot, *Université Lyon 1, Villeurbanne, France*

David Sankoff, *University of Ottawa, Ottawa, ON, Canada*

Ron Shamir, *Tel Aviv University, Ramat Aviv, Israel*

Terry Speed, *Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia*

Martin Vingron, *Max Planck Institute for Molecular Genetics, Berlin, Germany*

W. Eric Wong, *University of Texas at Dallas, Richardson, USA*
The series Lecture Notes in Bioinformatics (LNBI) was established in 2003 as a topical subseries of LNCS devoted to bioinformatics and computational biology.

The series publishes state-of-the-art research results at a high level. As with the LNCS mother series, the mission of the series is to serve the international R &#38; D community by providing an invaluable service, mainly focused on the publication of conference and workshop proceedings and postproceedings.
François Fages · Sabine Pérès
Editors

Computational Methods
in Systems Biology

23rd International Conference, CMSB 2025
Lyon, France, September 10–12, 2025
Proceedings

<figure><img src="image_2.png" /></figure>

Springer
Editors

François Fages

Inria

Palaiseau, France

Sabine Pérès

University of Lyon 1

Lyon, France

ISSN 0302-9743

ISSN 1611-3349 (electronic)

Lecture Notes in Bioinformatics

ISBN 978-3-032-01435-1

ISBN 978-3-032-01436-8 (eBook)

https://doi.org/10.1007/978-3-032-01436-8

LNCS Sublibrary: SL8 – Bioinformatics

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026

Chapters “Optimal Input Design for Model Selection in Systems with Cell-to-Cell Variability” and “Minimal Metabolic Exchanges for Microbial Communities in a Chemostat at Steady State” are licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter.

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Preface

This volume contains the regular and tool papers presented at the 23rd International Conference on Computational Methods in Systems Biology (CMSB 2025), held at the University of Lyon, France, in the period September 10–12, 2025.

The CMSB annual conference series was initiated in 2003. The webpage for the conference series is at https://cmsb.sciencesconf.org/ and for this year's edition is at https://cmsb2025.sciencesconf.org/. The previous editions of the conference were held in Rovereto, Italy (2003), Paris, France (2004, 2011), Edinburgh, UK (2005, 2007), Trento, Italy (2006, 2010), Rostock, Germany (2008), Bologna, Italy (2009), London, UK (2012), Klosterneuburg, Austria (2013), Manchester, UK (2014), Nantes, France (2015), Cambridge, UK (2016), Darmstadt, Germany, (2017), Brno, Czech Republic (2018), Trieste, Italy (2019), Konstanz, Germany (2020, online), Bordeaux, France (2021, hybrid), Bucharest, Romania (2022), Luxembourg City, Luxembourg (2023), and Pisa, Italy (2024).

The conference brings together researchers from across the biological, computational, mathematical, and physical sciences who are interested in the modelling, simulation, analysis, inference, design, and control of biological systems. It covers the broad field of computational methods and tools in systems and synthetic biology and their applications in biomedicine and the environment. The conference welcomes new theoretical results with potential applications to systems and synthetic biology, as well as novel applications and case studies of existing methods, tools, or frameworks. Topics of interest to the conference include, but are not limited to: formalisms for modelling biological processes; methods and tools for biological system analysis, modelling and simulation; frameworks for model verification, validation, analysis, and simulation of biological systems; automated parameter and model inference; machine learning and data analytics in biology and biomedicine; data-driven model learning and scientific discovery; high-performance methods for computational systems biology; model integration and biological databases; data-based approaches for systems and synthetic biology; optimality and control of biological systems; design, analysis, and verification methods for synthetic biology; methods for biomolecular computing and engineered molecular devices; multi-scale modelling, multi-agent simulation and analysis methods; and modelling, analysis, and control of microbial communities and ecosystems.

CMSB 2025 received 39 regular and tool paper submissions, of which 34 were sent for reviews. 16 were accepted at first-round review and 5 were accepted after revision and second review. Each paper was reviewed by three program committee members. The tool papers are shorter papers evaluated for the quality of the tool itself, its usability and documentation, in addition to the scientific contribution of the communication. Only one tool paper was accepted this year of three submitted. The papers that were not accepted for publication were all on topic and invited for poster presentation at the conference.
We had four invited talks at CMSB 2025 given by Hervé Isambert (Curie Institute, Paris, France), Franck Picard (ENS Lyon, France), Jörg Stelling (ETH Zürich, Switzerland), and Susana Vinga (University of Lisbon, Portugal). The abstracts of their talks are included in the front matter of this volume. The regular and tool papers presented at CMSB 2025 follow with a classification in five sections: Boolean networks, continuous and hybrid models, rule-based models, model inference and machine learning, and community models and control. Of note this year is the growing number of papers involving machine learning methods in connection with our various modelling frameworks for data-driven model inference or data analysis.

We are very grateful to the members of the program committee, and to all our reviewers for the thoughtful and detailed comments they gave on the articles submitted to the conference. We thank all our sponsors for making possible the lowering of the registration fees to a minimum: the University of Lyon, Inria, CNRS, GDR BIMM, as well as Springer Nature for their sponsoring of the best paper and best student paper awards. We also want to thank the CMSB steering committee and the previous organizers of CMSB conferences for their support in organizing this edition of the conference.

June 2025

François Fages

Sabine Pérès
# Organization

## Program Committee Chairs

François Fages

Sabine Peres

Inria Saclay, France

University of Lyon 1, France

## Steering Committee

Luca Cardelli

François Fages

Monika Heiner

Satoru Miyano

Ion Petre

Gordon Plotkin

Corrado Priami

Carolyn Talcott

Mirco Tribastone

Adelinde Uhrmacher

plus guest members:
Eugenio Cinquemani

Moreno Falaschi

Roberta Gori

Pedro Monteiro

Joachim Niehren

Jun Pang

Loïc Paulevé

Sabine Peres

University of Oxford, UK

Inria Saclay, France

Brandenburg TU Cottbus-Senftenberg, Germany

University of Tokyo, Japan

University of Turku, Finland

University of Edinburgh, UK

University of Pisa, Italy

SRI International, USA

IMT Lucca, Italy

University of Rostock, Germany

Inria Grenoble-Rhône-Alpes, France

University of Siena, Italy

University of Pisa, Italy

University of Lisbon, Portugal

Inria Lille, France

University of Luxembourg, Luxembourg

CNRS, LaBRI, France

University of Lyon 1, France

## Program Committee

Linda Brodo

Roberto Bruni

Laurence Calzone

Luca Cardelli

Milan Ceska

Università di Sassari, Italy

Università di Pisa, Italy

Institut Curie, France

University of Oxford, UK

Brno University of Technology, Czechia
Eugenio Cinquemani

David Doty

François Fages (PC Co-chair)

Jérôme Feret

Maxime Folschette

Ashutosh Gupta

Ina Koch

Dung-Yup Lee

Paolo Milazzo

Andrzej Mizera

Pedro T. Monteiro

Joachim Niehren

Jun Pang

Loïc Paulevé

Sabine Peres (PC Co-chair)

Ion Petre

Tatjana Petrov

Corrado Priami

Max Tschaikowski

Andrea Vandin

Verena Wolf

David Šafránek

Inria Grenoble, France

University of California, Davis, USA

Inria Saclay, France

Inria Paris, France

Centrale Lille, France

TIFR, India

Goethe University Frankfurt, Germany

Sungkyunkwan University, South Korea

University of Pisa, Italy

University of Warsaw, Poland

INESC-ID/IST - Universidade de Lisboa, Portugal

Inria Lille, France

University of Luxembourg, Luxembourg

CNRS/LaBRI, Bordeaux, France

University of Lyon 1, France

University of Turku, Finland

University of Trieste, Italy

University of Pisa, Italy

Aalborg University, Denmark

Sant'Anna School of Advanced Studies, Italy

Saarland University, Germany

Masaryk University, Czechia

## Additional Reviewers

Virgile Andreani

Guillaume Ballif

Anna Bernasconi

Alessandro Dipalma

Silvia Giulia Galfré

Roberta Gori

Joschka Groß

Laurent Guéguen

Mathieu Hemery

Ondrej Kuzlik

Paola Lecca

Matej Lexa

Iman Mehrabinezhad

Yannic Muskalla

Bogdan Petraszczuk

Tran Huong Giang Pham

Marco Podda

Albin Salazar

Alina Sirbu

Van-Giang Trinh

Hua-Ting Yao

Hidde de Jong

## Local Organization

Emma Crisci

Sasha Darmon
Pierre Gerenton
François Gindraud
Arnaud Mary
Vincent Lacroix
Sabine Peres
Marie-France Sagot
Camille Siharath
Invited Talks
# Causal Discovery and Network Inference for Biological and Biomedical Data

Hervé Isambert

Computational Biology Lab, CNRS-UMR168, Institut Curie, Paris, France
herve.isambert@curie.fr

Discovering causal effects is at the core of scientific investigation but remains challenging when mostly observational data is available. In essence, causal discovery infers cause-effect relations from specific correlation patterns involving at least three variables, which goes beyond the popular notion that pairwise correlation does not imply causation. In principle, causal insights may also help improve the explainability and generalizability of AI methods. Yet, in practice, causal representations have been difficult to learn and interpret, in particular, for high-dimensional data such as state-of-the-art biological and biomedical data.

Single-cell transcriptomics and bioimaging techniques produce massive amounts of gene expression and cell imaging data at single-cell resolution. However, this wealth of high-dimensional biological data remains largely under-explored due to the lack of unsupervised methods and tools to interpret them without preconceived hypotheses. This highlights the need to develop novel Machine Learning strategies to better exploit the richness and complexity of the information contained in high-dimensional biological and biomedical data.

In this talk, I will outline some network reconstruction methods and applications to a broad range of biological and clinical datasets. In particular, our group has developed a reliable and scalable causal discovery method, MIIC (https://miic.curie.fr), to learn cause-effect relationships in a variety of biological or biomedical data, from single-cell transcriptomic data [1–4] and live-cell imaging data [5] to clinical data from medical records of patients [6–9]. MIIC combines multivariate information analysis with interpretable graphical models [10–12] and outperforms other methods on a broad range of benchmark datasets, in particular on complex non-linear, categorical or mixed-type datasets [9, 10]. MIIC has also been recently integrated in the CausalXtract tool [5] to analyze live-cell time-lapse images of tumor-on-chip data, as well as in the CausalCCC tool [1] to reconstruct gene interaction pathways across multiple interacting cell types from single-cell or spatial transcriptomic data.
References

1. Dupuis, L., Debeaupuis, O., Simon, F., Isambert, H.: CausalCCC: a web server to explore intracellular causal pathways enabling cell-cell communication. Nucleic Acids Res. gkaf404 (2025)

2. Fusilier, Z., et al.: Macrophages restrict tumor permissiveness to immune infiltration by controlling local collagen topography through a Tcf4-Collagen3 fibrotic axis, bioRxiv 2025.01.17.633527 (2025)

3. Sella, N., Verny, L., Uguzzoni, G., Affeldt, S., Isambert, H.: MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data. Bioinformatics 34(13), 2311–2313 (2018)

4. Verny, L., Sella, N., Affeldt, S., Singh, P.P., Isambert, H.: Learning causal networks with latent variables from multivariate information in genomic data. PLoS Comput. Biol. 13(10), e1005662 (2017)

5. Simon, F., et al.: CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data. eLife 13, RP95485 (2025).

6. Sella, N., Guinot, F., Lagrange, N., Albou, L.-P., Desponds, J., Isambert, H.: Preserving information while respecting privacy: an information theoretic framework for synthetic health data generation. npj Digit. Med. 8, 49 (2025)

7. Ribeiro-Dantas, M., et al.: Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients. iScience 27(5), 109736 (2024)

8. Sella, N., et al.: Interactive exploration of a global clinical network from a large breast cancer cohort. npj Digit. Med. 5, 113 (2022)

9. Cabeli, V., Verny, L., Sella, N., Uguzzoni, G., Verny, M., Isambert, H.: Learning clinical network from medical records based on information estimates in mixed-type data. PLoS Comput. Biol. 16(5), e1007866 (2020)

10. Lagrange, N., Isambert, H.: An efficient search-and-score algorithm for ancestral graphs using multivariate information scores for complex non-linear and categorical data. In: International Conference on Machine Learning (ICML) (2025)

11. Cabeli, V., Li, H., Ribeiro-Dantas, M., Simon, F., Isambert, H.: Reliable causal discovery based on mutual information supremum principle for finite dataset. In: Why21 at Neural Information Processing Systems (NeurIPS) (2021)

12. Li, H., Cabeli, V., Sella, N., Isambert, H.: Constraint-based causal structure learning with consistent separating sets. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 32, p. 14257 (2019)
# Kernel-Based Perturbation Testing for Single-Cell Data

Franck Picard

Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
franck.picard@ens-lyon.fr

The convergence of single-cell biology and high-throughput sequencing technologies has made it possible to generate high-dimensional molecular portraits of individual cells. As a result, the field of single-cell data science has emerged, focusing on the development of statistical and computational tools to analyze the complexity of these datasets. A central challenge in single-cell analysis is the statistical comparison of datasets across conditions or tissues. Comparative analysis is crucial for distinguishing biological variation from technical noise and for detecting meaningful expression or regulatory changes. Traditionally, differential expression analysis (DEA) has been addressed through gene-wise two-sample testing frameworks, but standard approaches often fail to capture the full distributional complexity of single-cell data. Single-cell perturbation experiments add another layer of complexity and opportunity. These experiments compare perturbed and control cells, allowing for fine-grained analysis of population responses to stimuli or drugs. However, the resulting data often exhibit complex, non-linear structures that standard linear methods cannot adequately capture. This calls for methods capable of performing non-linear comparisons and modeling intricate data geometries.

To address these challenges, we propose a kernel-based framework for the differential analysis of single-cell data [1], including perturbation studies [2]. Kernel methods, widely used in machine learning, allow for non-linear operations by embedding data into a reproducing kernel Hilbert space (RKHS)—an infinite-dimensional space in which linear operations correspond to non-linear operations in the input space. This enables powerful and flexible statistical comparisons that are robust to the high dimensionality, sparsity, and noise typical of single-cell data. Our approach captures the distance between distributions by measuring the separation of mean embeddings with respect to biological variability. For perturbation experiments, we further develop a linear model in RKHS and introduce a formal hypothesis testing of high-dimensional data structures in complex experimental designs. We implement our methodology in a software package, kaov, available in Python. The package includes tools for visualization and interpretation, and offers a user-friendly interface modeled after popular analysis libraries.

As single-cell datasets continue to grow in size and complexity, traditional statistical methods struggle to uncover subtle but biologically important patterns. Kernel-based testing offers a robust, distribution-free, and non-linear alternative that is well suited to the unique challenges of single-cell data. By accounting for complex dependencies and offering interpretable results, it enables deeper insights into gene regulation, cell
fate, and disease progression, paving the way for biomarker discovery and precision medicine.

References

1. Ozier-Lafontaine, A.: Kernel-based testing for single-cell differential analysis. Genome Biol. 25(1), 114 (2024)

2. Ozier-Lafontaine, A.: Extending Kernel Testing to general designs. Arxiv 2405.13799 (2024)
# Predicting Metabolic Interactions Between Multiple Species from First Principles

Jörg Stelling

Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4056 Basel, Switzerland
joerg.stelling@bsse.ethz.ch

Predictive genome-scale metabolic models (GSMs) have become standard tools for systems-level analysis of metabolism in contexts ranging from biotechnology to cancer 3. They rely on incorporating mass balances and potentially other constraints on the operation of metabolic networks at steady-state. In biased methods such as flux balance analysis (FBA), assumptions on biological objectives (e.g., bacteria maximizing their growth rate) allow prediction of specific flux distributions by optimization (e.g., by linear programming in FBA). Unbiased methods for the analysis of all feasible flux distributions are subsumed under metabolic pathway analysis; they typically involve solving large combinatorial problems. However, many interactions in microbial consortia or tissues of multicellular organisms rely on networks of metabolite exchanges, where multiple species interact metabolically via a shared environment. In the multi-species setting, biased methods either require assumptions on community objectives that are biologically questionable, or model augmentations by often unknown kinetics. Unbiased methods are limited by the scalability of current metabolic pathway concepts to multi-species networks 4.

For unbiased methods, we aim to increase predictive power by relying on first principles and by systematically quantifying uncertainties. This involves integrating thermodynamic constraints via probabilistic thermodynamic analysis (PTA)², kinetics via ENKIE, a predictor of enzyme kinetic parameters ¹, and optimization over enzyme costs. The resulting method, termed global enzyme cost minimization (GECM), combines MCMC sampling with biconvex optimization to allow predictions such as alternative metabolic strategies for *E. coli* growth that are consistent with experimental observations. GECM thereby provides predictions of single-species behaviors and their uncertainty—albeit at high computational cost.

As a potentially complementary unbiased method, we define minimal pathways (MPs) that yield compact representations of metabolic network capabilities ⁵. They generalize existing pathway concepts by allowing inhomogeneous constraints as well as the targeted analysis of subnetworks. Computationally, enumeration and sampling of MPs is efficient via iterative minimization and pathway graphs. This enables applications such as assessing quantitative gene essentiality in *E. coli* central metabolism, predicting metabolite exchanges associated with homeostasis and health in a host-microbe model of the human gut, and designing butyrate-producing microbial communities.
In combination, we envisage MPs and GECM to predict metabolic interaction networks between multiple species as follows: MPs restrict the set of potential metabolite exchange patterns between individual species and the environment in the community context. Application of GECMs to the reduced problem(s) could then follow to predict actual interactions and their uncertainties. However, this requires improvements to the computational efficiency and numerical stability of GECM. In perspective, this integration could enable predictions of emergent community functions and of experimentally testable interactions for applications such as the human gut microbiome.

**Keywords:** Genome-scale metabolic models · Metabolic interactions · Thermodynamics · Kinetics · Metabolic pathways · Microbiome

## References

1. Gollub, M.G., Backes, T., Kaltenbach, H.M., Stelling, J.: ENKIE: a package for predicting enzyme kinetic parameter values and their uncertainties. Bioinformatics **40** (2024). https://doi.org/10.1093/bioinformatics/btae652

2. Gollub, M.G., Kaltenbach, H.M., Stelling, J.: Probabilistic thermodynamic analysis of metabolic networks. Bioinformatics **37**(18), 2938–2945 (2021). https://doi.org/10.1093/bioinformatics/bta194

3. Gu, C., Kim, G.B., Kim, W.J., Kim, H.U., Lee, S.Y.: Current status and applications of genome-scale metabolic models. Genome Biol. **20**(1) (2019). https://doi.org/10.1186/s13059-019-1730-3

4. Theorell, A., Stelling, J.: Metabolic networks, microbial consortia, and analogies to smart grids. Proceedings of the IEEE **110**(5), 541–556 (2022). https://doi.org/10.1109/jproc.2022.3158396

5. Øyås, O., Theorell, A., Stelling, J.: Scalable enumeration and sampling of minimal metabolic pathways for organisms and communities. bioRxiv (2024). https://doi.org/10.1101/2024.10.09.617357
# Reverse Engineering and Optimization of Metabolic Networks

Susana Vinga

INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Portugal
susanavinga@tecnico.ulisboa.pt

Understanding the dynamic behavior of cells and their metabolism is a key goal in systems biology. Reverse engineering metabolic processes from data is crucial for uncovering underlying mechanisms, building predictive models, and optimizing networks for strain engineering and biotechnological applications. Here, we present three representative case studies that highlight different modeling and optimization strategies.

Among the various modeling approaches, S-systems provide a remarkably flexible and interpretable framework. They consist of systems of ordinary differential equations (ODEs) based on a power-law formalism, derived from a first-order Taylor series approximation in logarithmic space. This structure makes them well suited for approximating reaction dynamics without relying on detailed mechanistic information. We present a case study of glycolysis in *Lactococcus lactis*, modeled using in vivo NMR multivariate time-series of metabolic concentrations [6]. The structural analysis of the proposed model was initially used to assess parameter sensitivity and identifiability, thereby enhancing model interpretability and significantly improving the subsequent reverse engineering step [5].

While classical ODEs offer a powerful framework for modeling continuous nonlinear dynamics, they typically assume that model parameters remain constant over time. However, many biological processes, such as cell growth under nutrient shifts or environmental changes, may exhibit distinct phases and are therefore better modeled using time-varying parameters. Indeed, these transitions can be more effectively captured using hybrid modeling approaches, which combine continuous dynamics with discrete state changes. In this context, we developed SON-EM, a methodology for parameter identification in switched systems [2], which integrates convex optimization, clustering, and a refinement step to detect switching points and estimate local parameters. Applied to multiphasic cell growth, SON-EM successfully captured diauxic transitions and metabolic shifts using a parsimonious structure based on logistic models, with minimal manual intervention [3].

Optimization also plays a crucial role in designing metabolic interventions. We developed OptPipe, a consensus-based pipeline that integrates several algorithms (including OptKnock, RobustKnock, and RobOKoD) to identify promising gene knockouts and rank them based on growth, target production, and adaptability metrics [4]. Complementarily, we explored multi-objective optimization to identify reaction deletions that simultaneously maximize and/or minimize multiple objectives, using multi-objective
mixed-integer optimization (MOMO), and applied this approach as a proof of concept to enhance ethanol production in *Saccharomyces cerevisiae* [1].

The presented modeling and optimization methods illustrate how mathematical frameworks, algorithms, and biological insights can be combined to understand, predict, and manipulate metabolic networks.

**Keywords:** Systems Biology · Strain Design · Multi-Objective Optimization · Kinetic Modeling · Hybrid models

**Acknowledgments.** SV thanks all the co-authors of the presented studies.

## References

1. Andrade, R., Doostmohammadi, M., Santos, J.L., Sagot, M.F., Mira, N.P., Vinga, S.: MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering. BMC Bioinformatics **21**(1), 69 (2020). https://doi.org/10.1186/s12859-020-3377-1

2. Hartmann, A., Lemos, J.M., Costa, R.S., Xavier, J., Vinga, S.: Identification of switched ARX models via convex optimization and expectation maximization. J. Process Control **28**, 9–16 (2015). https://doi.org/10.1016/j.jprocont.2015.02.003

3. Hartmann, A., Neves, A.R., Lemos, J.M., Vinga, S.: Identification and automatic segmentation of multiphasic cell growth using a linear hybrid model. Math. Biosci. **279**, 83–89 (2016). https://doi.org/10.1016/j.mbs.2016.06.013

4. Hartmann, A., et al.: OptPipe - a pipeline for optimizing metabolic engineering targets. BMC Systems Biology **11**, 143 (2017). https://doi.org/10.1186/s12918-017-0515-0

5. Vinga, S., Neves, A.R., Santos, H., Brandt, B.W., Kooijman, S.A.L.M.: Subcellular metabolic organization in the context of dynamic energy budget and biochemical systems theories. Philos. Trans. R. Soc. B Biol. Sci. **365**(1557), 3429–3442 (2010). https://doi.org/10.1098/rstb.2010.0156

6. Vinga, S., Thomaseth, K., Lemos, J., Neves, A.R., Santos, H., Freitas, A.T.: Structural analysis of metabolic networks: a case study on Lactococcus lactis. In: Proceeding of the 8th Portuguese Conference on Automatic Control (CONTROLO 2008), pp. 4379–4384, January 2008
# Contents

## Boolean Networks

Computation of Immediate Neighbours of Monotone Boolean Functions
José E. R. Cury, Patrícia Tenera Roxo, Vasco Manquinho,
Claudine Chaouiya, and Pedro T. Monteiro
3

Three-State Gene Expression Model Parameterized for Single-Cell
Multi-omics Data
Thibaut Peyric, Thomas Lepoutre, Anton Crombach, and Thomas Guyet
23

On Model Reductions of Boolean Networks
Claudine Chaouiya, Jérôme Feret, and Patrícia Tenera Roxo
42

AEON 2025: Robust Control of Partially-Specified Boolean Networks
Vojtěch Veselý, Eva Šmijáková, Samuel Pastva, Nikola Beneš,
and David Šafránek
61

Looking for Signs: Reasoning About FOBNNs Using SAT
Hans-Jörg Schurr and Athénaïs Vaginay
69

Continuous Petri Nets Faithfully Fluidify Most Permissive Boolean
Networks
Stefan Haar and Juri Kolčák
89

## Continuous and Hybrid Models

Efficient Stochastic Simulation of Gene Regulatory Networks Using
Hybrid Models of Transcriptional Bursting
Mathilde Gaillard and Ulysse Herbach
109

Exploring Metastable Dynamics of Gene Regulatory Networks
with ISOKANN
Maryam Yousefian, Luca Donati, Alexander Sikorski, Marcus Weber,
and Susanna Röblitz
126

## Rule-Based Models

Reachability Analysis for Parametric Rule-Based Models
Jérôme Feret and Rebecca Ghidini
153
<table><tr><td>A Rule-Based Modeling Approach for Studying Animal Collectives:<br/>A Case Study of Juvenile Honeybee Thermotaxis</td><td>174<br/>Matthieu Bouguéon, Tatjana Petrov, and Albin Salazar</td></tr></table>

<table><tr><td>A Formal Approach to Identify Structural Patterns in RNA</td><td>195<br/>Michele Loreti, Michela Quadrini, Matteo Scoccia, and Luca Tesei</td></tr></table>

## Model Inference and Machine Learning

<table><tr><td>Kolmogorov-Arnold Network for Gene Regulatory Network Inference</td><td>221<br/>Tsz Pan Tong, Aoran Wang, George Panagopoulos, and Jun Pang</td></tr></table>

<table><tr><td>Optimal Input Design for Model Selection in Systems with Cell-to-Cell<br/>Variability</td><td>240<br/>Andrea Stallvik, Hans-Michael Kaltenbach, and Jörg Stelling</td></tr></table>

<table><tr><td>Decoding Gene Regulation in Alzheimer's Disease with Transfer Learning<br/>and Explainable Machine Learning</td><td>258<br/>Sergio Peignier, Amanda Lo Van, Yann Meunier, Elea Pauliat,<br/>Matis Zouari, and Federica Calevro</td></tr></table>

<table><tr><td>Interaction Graphs of Phytoplankton Species Interactions Using Logical<br/>Learning</td><td>276<br/>Madeleine Eyraud, Maxime Folschette, Katsumi Inoue,<br/>Sébastien Lefebvre, and Cédric Lhoussaine</td></tr></table>

<table><tr><td>Evaluating PDE Discovery Methods for Multiscale Modeling of Biological<br/>Signals</td><td>297<br/>Andréa Ducos, Audrey Denizot, Thomas Guyet, and Hugues Berry</td></tr></table>

<table><tr><td>Optimizing Feature Extraction Methods Using CLASS SIMILARITY RATIO<br/>for EMG-Based Hand Gesture Classification</td><td>313<br/>Felicity Escarzaga, Brian Donnelly, and Kyle Winfree</td></tr></table>

## Population Models and Control

<table><tr><td>Eukaryotic Ancestry in a Finite World</td><td>337<br/>Juliette Luiselli and Manuel Lafond</td></tr></table>

<table><tr><td>Minimal Metabolic Exchanges for Microbial Communities in a Chemostat<br/>at Steady State</td><td>356<br/>Alix Moawad and Jörg Stelling</td></tr></table>
Seed Inference in Interacting Microbial Communities Using Combinatorial Optimization ................................................................. 370
Chabname Ghassemi Nedjad, Sebastián Nelson Mendoza, Clémence Frioux, and Loïc Paulevé

A Model-Free Control Strategy for Selective Disruption of Parkinsonian Brain Oscillations .................................................... 388
Cédric Join, Jakub Orlowski, Antoine Chaillet, Madeleine Lowery, Hugues Mounier, and Michel Fliess

Author Index .................................................................................. 403
Author Index

B

Beneš, Nikola 61
Berry, Hugues 297
Bouguéon, Matthieu 174

C

Calevro, Federica 258
Chaillet, Antoine 388
Chaouiya, Claudine 3, 42
Crombach, Anton 23
Cury, José E. R. 3

D

Denizot, Audrey 297
Donati, Luca 126
Donnelly, Brian 313
Ducos, Andréa 297

E

Escarzaga, Felicity 313
Eyraud, Madeleine 276

F

Feret, Jérôme 42, 153
Fliess, Michel 388
Folschette, Maxime 276
Frioux, Clémence 370

G

Gaillard, Mathilde 109
Ghassemi Nedjad, Chabname 370
Ghidini, Rebecca 153
Guyet, Thomas 23, 297

H

Haar, Stefan 89
Herbach, Ulysse 109

I

Inoue, Katsumi 276

J

Join, Cédric 388

K

Kaltenbach, Hans-Michael 240
Kolčák, Juri 89

L

Lafond, Manuel 337
Lefebvre, Sébastien 276
Lepoutre, Thomas 23
Lhoussaine, Cédric 276
Lo Van, Amanda 258
Loreti, Michele 195
Lowery, Madeleine 388
Luiselli, Juliette 337

M

Manquinho, Vasco 3
Mendoza, Sebastián Nelson 370
Meunier, Yann 258
Moawad, Alix 356
Monteiro, Pedro T. 3
Mounier, Hugues 388

O

Orłowski, Jakub 388

P

Panagopoulos, George 221
Pang, Jun 221
Pastva, Samuel 61
Paulevé, Loïc 370
Pauliat, Elea 258
Peignier, Sergio 258
Petrov, Tatjana 174
Peyric, Thibaut 23

Q

Quadrini, Michela 195
R

Röblitz, Susanna 126

S

Salazar, Albin 174
Šafránek, David 61
Schurr, Hans-Jörg 69
Scoccia, Matteo 195
Sikorski, Alexander 126
Šmijáková, Eva 61
Stallvik, Andrea 240
Stelling, Jörg 240, 356

T

Tenera Roxo, Patrícia 3, 42
Tesei, Luca 195

Tong, Tsz Pan 221

V

Vaginay, Athénaïs 69
Veselý, Vojtěch 61

W

Wang, Aoran 221
Weber, Marcus 126
Winfree, Kyle 313

Y

Yousefian, Maryam 126

Z

Zouari, Matis 258