SS2 - Computer Algebra Software in the Life Sciences (CASinLife)

link to the session's webpage



It is well-known that to answer a question, one must first state and formulate it properly. Many questions, from the effect of a drug on cancer cells to the spread of an epidemic, require mathematical modeling. Every project in the life sciences involves chemical materials, cells, or animals, with the ultimate goal of understanding how changes in the quantity of one element affect the others. While some questions can be addressed using basic methods, most require more sophisticated techniques. For instance, when measuring all relevant quantities is impossible, one encounters the issue of identifiability: is it possible to estimate the model’s parameters using data from only the observable variables?

Computer Algebra, as the name suggests, is the field focused on developing algorithms for symbolic computations involving variables and parameters. While life scientists have a wealth of mathematically interesting questions, computer algebra researchers possess a wealth of algorithms and methods capable of addressing complex problems. Our session aims to bring these two groups together and facilitate the matching of questions with solutions. In addition to speakers from previous editions of CASinLife, who will report on collaborations sparked earlier, we are also seeking new participants with fresh challenges and ideas to join our ongoing journey of discovery.


This will be the fourth edition of CASinLife. The first three editions were held as special sessions of ACA 2022 (in Turkey), ACA 2023 (in Poland), and ICMS 2024 (in the UK). Topics of interests include, but are not limited to the following:



Session organizers

AmirHosein Sadghimanesh (Coventry University, UK)
Andrzej Mizera (University of Warsaw, Poland)


Talks

Fabrice Rouillier - On solving parametric systems
Atsushi Mochizuki - Biological functions and functional modules originated in the structure of chemical reaction network
Bryan Hernandez - Analyzing the dynamics and structure of biochemical reaction networks via network decomposition
Nicola Vassena - Symbolic bifurcation analysis of reaction networks with Python. Part I: Theory
Richard Golnik - Symbolic bifurcation analysis of reaction networks with Python. Part II: Implementation
Ovidiu Radulescu - Graph-Theoretic Algorithms for Reducing Chemical Reaction Networks
Robert Lewis - New Results about Bricard’s Flexible Octahedra
Sofia Triantafyllou - Learning treatment effects from multiple data
Marcus Aichmayr - Reaction networks with (generalized) mass-action kinetics: Sign vector conditions for the existence of a unique general equilibrium
Jack Jansma - Bayesian inference of interaction rates in a metabolite-bacteria network using time-series counts
Andrzej Mizera - Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks - the GATTACA framework