Uploaded August 2026 | Updated September 2026, 2 weeks ago

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Title: Categorical Abstractions for Living Systems (CatALiSys): A Framework for Collaborative, Comprehensive and Customizable Modeling of Biochemical Reaction Systems
Abstract: Modeling is fundamentally the process of choosing useful abstractions. Different researchers studying overlapping biochemical systems often choose different abstractions because they seek answers to different scientific questions. While these choices are individually well motivated, they make it difficult to integrate existing models into larger representations of cellular processes, posing a fundamental obstacle to the long-term vision of scientifically interpretable whole-cell models.

In biochemical reaction systems, such incompatibilities typically arise from mismatches in the granularity of chemical species and reactions, as well as differing assumptions about the surrounding environment (the “host cell”). Our preliminary work on CatALiSys aims to overcome these barriers to scalable modeling by enabling collaborative knowledge sharing, while preserving individual research interests through generation of customized models.

Catering to an interdisciplinary audience, this talk will provide a brief introduction to biochemical reaction systems, existing modeling formalisms and the categorical ideas underlying our approach.

Based on this, we demonstrate how symmetric monoidal indexed categories can formalize biochemical systems. In this framework, an indexing category explicitly represents the relationships between chemical species types and environmental contexts. This indexing category can be pruned to the desired scope and granularity. Finally, we showcase RuleTT, our preliminary type-theoretic Rust implementation. Using minimal working examples, we demonstrate how RuleTT handles knowledge updates, enables customization of scope and granularity, and helps to make implicit environmental assumptions explicit. We hope these ideas represent a next step toward the collaborative construction of scientifically interpretable and predictive virtual cells.
Date: July 21, 2026