Computational Methods in Systems Biology: 22nd International Conference, CMSB 2024, Pisa, Italy, September 16–18, 2024, Proceedings

Preface
Organization
Abstracts of Invited Talks and Tutorials
	CMSB Invited Talks
	Circadian Clocks: Modeling and Characterization of Cycle Dynamics
	Tools for Microbial Network Analysis and Community Modelling
	Digital Health Opportunities
	Graph Databases for Integration, Exploration and Analysis of Complex Data
Workshop on Reaction Systems Invited Talks
	Mining Reactions in Reaction Systems with Discrete Concentrations
	Tutorial on Reaction Systems. A Set-Theoretical Modeling Framework Based on Facilitation, Inhibition, and Interaction with the Environment
	Contents
Process Calculi and Rewriting Techniques for Analyzing Reaction Systems
	1 Introduction
	2 Reaction Systems
	3 ccReact: A Language for Executing and Analyzing RSs
	4 Rewriting Logic Semantics for ccReact
	5 Case Studies and Analyses
	6 Related Work
	7 Conclusions and Future Work
	References
BNClassifier: Classifying Boolean Models by Dynamic Properties
	1 Introduction
	2 Model Classification Problem
	3 Tool Components and Implementation
	4 Evaluation
	References
Enhancing Reaction Systems with Guards for Analysing Comorbidity Treatment Strategies
	1 Introduction
	2 Concise Guidelines Description
	3 Background on Reaction Systems
	4 Reaction Systems with Guards
	5 RS Models for Clinical Guidelines
	6 A Clinical Case Study: AFib and Hypertension
	7 Modelling and Analysis of the Clinical Case Study
	8 Related Work and Concluding Remarks
	References
Graphical Conditions Ensuring Equality Between Differential and Mean Stochastic Dynamics
	1 Introduction
	2 Motivating Example
	3 Preliminaries on Reaction Systems
	4 Stoichiometric Influence and Modification Graph
	5 Main Theorem
	6 SIMG Ancestor Condition Checking Algorithm
	7 Examples
	8 Equality Property for Cosine Oscillatory RS
	9 Evaluation in Biomodels and Partial Approximations
	10 Conclusion and Perspectives
	References
Bio-Stark: A Tool for the Time-Point Robustness Analysis of Biological Systems
	1 Bio-Stark: Motivation and Originality
	2 Bio-Stark: Implementation
	3 Case Studies
	4 Concluding Remarks
	References
BoNesis: a Python-Based Declarative Environment for the Verification, Reprogramming, and Synthesis of Most Permissive Boolean Networks
	1 Introduction
	2 Features
	3 Implementation and Use Cases
	4 Conclusion
	References
Computing Thermodynamically Consistent Elementary Flux Modes with Answer Set Programming
	1 Introduction
	2 Computation of EFMs Compatible with Thermodynamic Constraints
		2.1 Thermodynamics of Chemical Reactions and Formulation in LP
		2.2 DeltaGChecker Extension to aspefm
	3 Application to Central Carbon Metabolism of E.coli
		3.1 Selection of EFMs of Thermodynamic Interest in E.coli
	4 Conclusion
	A Appendix
		A.1 Computation Times on E.coli
	References
Batch Effect Correction in a Confounded Scenario: a Case Study on Gene Expression of Chornobyl Tree Frogs
	1 Introduction
	2 Tree Frog Sampling and Dataset Description
		2.1 Transcriptomic Data
		2.2 Genetic Distance
	3 Methods
		3.1 Batch Effect-Correction Algorithms
		3.2 Application to the Tree Frog RNA-Seq Study
	4 Results
		4.1 Confounding Effect of the Collection Site
		4.2 Genetic Diversity Treated as Batch Effects
		4.3 Search of Other Possible Confounders with Surrogate Variable Analysis
		4.4 Biological Interpretation
	5 Discussion
	6 Conclusion
	A  Appendix: Close-Up on the Impact of Sparsity in Gene Selection
	References
Approximate Reductions of Rational Dynamical Systems in CLUE
	1 Introduction
	2 Preliminaries
	3 Implementation
	4 Illustration of Model Workflow Using CLUE
	5 Conclusion
	References
Discovering Biochemical Reaction Models by Evolving Libraries
	1 Introduction
	2 Background
		2.1 Biochemical Reaction Models
		2.2 Symbolic Regression for Reaction Systems
	3 Related Work
	4 Evolving Libraries
		4.1 Searching the Sub-library Space Using a Genetic Algorithm
		4.2 Including Background Knowledge
	5 Case Studies
		5.1 Susceptible-Infected-Recovered Model
		5.2 Lotka-Volterra Model
		5.3 Wnt Pathway
	6 Discussion
	7 Conclusion
	A Complete List of Experiment (Hyper-)parameters
	B Learned Model\'s Trajectories for the Wnt Pathway
	C Learned Models for the Wnt Pathway
	References
Flexible Nets to Improve GEM Cell Factories by Combining Kinetic and Proteomics Data
	1 Introduction
	2 Flexible Nets to Model Biological Systems
		2.1 Flexible Nets
		2.2 Modeling GEMs with FNs
	3 GECKO Method
		3.1 Modeling the GECKO Method with FNs
	4 sMOMENT Method
		4.1 Modeling the sMOMENT Method with FNs
	5 Combining the sMOMENT and GECKO Methods
	6 Bacillus Subtilis models
	7 Enriching iYO844 with Kinetic and Proteomic Constraints Using FNs
		7.1 Collection of Enzymatic Parameters
		7.2 Integrating Kinetic and Proteomic Constraints
		7.3 Assessing the Effect of the Integrated Constraints
	8 Conclusions
	9 Annex
	References
Reverse Engineering of Renal Tubule Networks in the High-Dimensional Regime
	1 Introduction
	2 Methods
		2.1 Concentration Graph Models
		2.2 Network Inference
	3 Evaluating the Approach Performance
		3.1 Dataset
		3.2 Evaluation Procedure
		3.3 Results
	4 Application to Kidney Tubules Expression Atlas
		4.1 Dataset
		4.2 Segment-Specific Networks Comparison
		4.3 Hierarchical Organization of Modularity in Segment-Specific Networks
	5 Conclusions
	References
Causal Model Discovery in Cancer Guided by Cellular Pathways
	1 Introduction
	2 Background
		2.1 Cancer Data
		2.2 Pathways
		2.3 Causal Discovery
	3 Causal Discovery Pipeline
		3.1 Variable Reduction and Decomposition
		3.2 Aggregation
		3.3 Datasets for Causal Model Discovery
	4 Results
		4.1 Sub-groups and Consensus Causal Models
		4.2 All Patient\'s Causal Model
		4.3 Discussion
	5 Conclusion
	References
Deciphering Structural Selective Constraints: A Comparative Evolutionary Analysis of RNA Hairpin Structures
	1 Introduction
	2 Comparing Both RNA Sequences and Structures: Proposed Approach
		2.1 Computational Methods: Aligning and Folding
		2.2 Data for Application
		2.3 Spontaneous Substitutions
		2.4 Control Models
		2.5 Substitutions that Preserve Secondary Structure
	3 Results: Application to Pre-miRNA Hairpins
		3.1 Spontaneous vs Observed Substitutions: Selective Pressure in RNA Hairpins
		3.2 Control Versus Real MiRNAs
		3.3 Structural Selective Constraint
		3.4 Classifying Substitutions by Their Structural Effects
	4 Discussion
	5 Disclosure of Interests.
	References
How Much Do DNA and Protein Deep Embeddings Preserve Biological Information?
	1 Introduction
	2 Methods
		2.1 Datasets
		2.2 From Genetic Sequences to Embeddings
		2.3 Evaluation of Embeddings
	3 Results
		3.1 Evolutionary Information
		3.2 Biological Annotations
	4 Related Works
	5 Conclusions
	References
Uncovering Dynamic Structures Within Cyclic Attractors of Asynchronous Boolean Networks with Spectral Clustering
	1 Introduction
	2 Methods
		2.1 Boolean Interaction Networks: Definitions and Background
		2.2 Continuous-Time Boolean Networks
		2.3 Spectral Clustering via PCCA+
		2.4 Spectral Clustering Applied to the Toy Model
	3 Analysis of Mammalian Cell Cycle Models
		3.1 Cell Cycle Models
		3.2 Spectral Clustering of the Mammalian Cell Cycle Model
		3.3 Comparison Between Model A and Model B
	4 Conclusion
	A Appendix
		A.1 Clustering Result for the Toy Model
		A.2 Clustering for Model A and B with Equiprobable Density
		A.3 Markov State Model for Model B
		A.4 Other Measures of Modularity
		A.5 Clustering of States in Model A
		A.6 Differences in the Clustering Between Model A and B
	References
Author Index