An Intuitive Automated Modelling Interface for Systems Biology

TL;DR

This paper presents a natural language interface for constructing stochastic pi calculus models of biological systems, enabling modular, narrative-style modeling and easy modifications, demonstrated on Fc-gamma receptor phosphorylation.

Contribution

It introduces a novel intuitive language for systems biology modeling that simplifies building and refining complex biochemical models.

Findings

Successfully modeled Fc-gamma receptor phosphorylation.

Provided a tool for translating natural language models into SPiM.

Enhanced modularity and ease of model refinement.

Abstract

We introduce a natural language interface for building stochastic pi calculus models of biological systems. In this language, complex constructs describing biochemical events are built from basic primitives of association, dissociation and transformation. This language thus allows us to model biochemical systems modularly by describing their dynamics in a narrative-style language, while making amendments, refinements and extensions on the models easy. We demonstrate the language on a model of Fc-gamma receptor phosphorylation during phagocytosis. We provide a tool implementation of the translation into a stochastic pi calculus language, Microsoft Research's SPiM.