The CMSB 2026 proceedings deal with all aspects biological, computational, mathematical, and physical sciences who are interested in the modeling.
This book constitutes the refereed proceedings of the 24th International Conference, CMSB 2026, Lisbon, Portugal, July 24–25, 2026, Proceedings.
The 15 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 43 submissions sent to reviews.
They are grouped into the following topics: Boolean and Qualitative Networks; Rule-Based and Stochastic Models; Constraint-Based Modeling and Molecular Computation and Causal Inference and Multi-Omics Integration.
Table of Contents:
.- Boolean and Qualitative Networks
.- Source-Target Control of Boolean Networks with Minimal Edge Perturbations.
.- Modulation-Reaction Networks.
.- Inference of Qualitative Models from Steady-State Data via Weighted MaxSMT.
.- pyModRev: a Python Tool for Model Revision of Boolean Networks.
.- Rule-Based and Stochastic Models
.- Statistical model checking for rule-based models in the Kappa language.
.- Efficient Stochastic Trace Generation for Transcriptio.
.- Mamdani-Driven Fuzzy Reaction Systems.
.- Incremental KaSa: Static Analysis of Kappa Models at Edit Time.
.- Constraint-Based Modeling and Molecular Computation
.- Inferring Minimal Culture Media using Biologically Constrained Combinatorial Optimization.
.- On the Design of an Analog-Dyadic Converter CRN.
.- Analog computation with transcriptional networks.
.- Metabolic Transformation Algorithm: A Systems Biology Approach to Aging and Alzheimer’s Disease.
.- Causal Inference and Multi-Omics Integration
.- Metabolic Flux Inference in a Cheese Microbial Community via comFI: a Biology-informed Approach for Time-resolved Multi-omics Integration.
.- GRNgen: a Generator of Gene Regulatory Networks Fitting Graph and Motif Properties.
.- Consensus Enhances Individual Causal Models: a Use Case on Lung Cancer Driven by Cellular Pathways.
.- Graph Learning Models for Temporal Gene Expression Prediction and the Role of Interactions Topology.
.- Multi-view Clustering of Transcriptomics and Methylomics Data Elucidates Glioma Molecular Stratification.
