Rewriting Theory for the Life Sciences: A Unifying Theory of CTMC Semantics (Long version)
Nicolas Behr (Universit\'e de Paris, CNRS, IRIF), Jean Krivine, (Universit\'e de Paris, CNRS, IRIF), Jakob L. Andersen (SDU Odense), Daniel, Merkle (SDU Odense)

TL;DR
This paper develops a universal theoretical framework for stochastic rewriting systems in life sciences, integrating CTMC semantics with structural constraints, and unifies existing biochemical and chemical rewriting models.
Contribution
It introduces a novel rule algebra and a framework for constrained rewriting, enabling a unified CTMC semantics for bio- and organo-chemical systems.
Findings
Unified CTMC framework for rewriting systems
Encoding of bio-chemical and organo-chemical rewriting
First formal CTMC semantics for M{\
Abstract
The Kappa biochemistry and the M{\O}D organic chemistry frameworks are amongst the most intensely developed applications of rewriting-based methods in the life sciences to date. A typical feature of these types of rewriting theories is the necessity to implement certain structural constraints on the objects to be rewritten (a protein is empirically found to have a certain signature of sites, a carbon atom can form at most four bonds, ...). In this paper, we contribute a number of original developments that permit to implement a universal theory of continuous-time Markov chains (CTMCs) for stochastic rewriting systems. Our core mathematical concepts are a novel rule algebra construction for the relevant setting of rewriting rules with conditions, both in Double- and in Sesqui-Pushout semantics, augmented by a suitable stochastic mechanics formalism extension that permits to derive…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSemantic Web and Ontologies · Scientific Computing and Data Management · DNA and Biological Computing
