Interactions between Causal Structures in Graph Rewriting Systems

Abstract

Graph rewrite formalisms are a powerful approach to modeling complex molecular systems. They capture the intrinsic concurrency of molecular interactions, thereby enabling a formal notion of mechanism (a partially ordered set of events) that explains how a system achieves a particular outcome given a set of rewrite rules. It is then useful to verify whether the mechanisms that emerge from a given model comply with empirical observations about their mutual interference. In this work, our objective is to determine whether a specific event in the mechanism for achieving X prevents or promotes the occurrence of a specific event in the mechanism for achieving Y. Such checks might also be used to hypothesize rules that would bring model mechanisms in compliance with observations. We define a rigorous framework for defining the concept of interference (positive or negative) between mechanisms induced by a system of graph-rewrite rules and for establishing whether an asserted influence can be realized given two mechanisms as an input.

Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

“…For rule‐based models, the causal analysis graph is equivalent to the influence map. Agent based simulations of rule‐based models can be represented as random walks on the influence map (Cristescu et al , 2019 ). Accordingly, causal relationships can be extracted by analyzing the traces of individual agents on the knowledge graph (Boutillier et al , 2018 ).…”

Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

“…For rule-based models, the causal analysis graph is equivalent to the influence map. Agent based simulations of rules-based models can be represented as random walks on the influence map (Cristescu et al , 2019). Accordingly, causal relationships can be extracted by analyzing the traces of individual agents on the knowledge graph (Boutillier et al , 2018).…”

“…The copyright holder for this preprint this version posted February 18, 2022. ; https://doi.org/10.1101/2022.02.17.480899 doi: bioRxiv preprint based models, the causal analysis graph is equivalent to the influence map. Agent based simulations of rules-based models can be represented as random walks on the influence map (Cristescu et al, 2019).…”

“…For rule-based models, the causal analysis graph is equivalent to the influence map. Agent based simulations of rules-based models can be represented as random walks on the influence map (Cristescu et al, 2019).…”