Modular model building

TL;DR

This paper advocates for modular, programmable models in systems biology to improve flexibility, reusability, and abstraction, enabling more efficient and scalable construction of biological models.

Contribution

The authors introduce a computational infrastructure that supports modularity and abstraction in biological modeling, facilitating reusable and adaptable model components.

Findings

Modular models enable independent specification and combination of sub-systems.

Programmable models allow creation of reusable libraries of biological process modules.

The infrastructure supports new modeling capabilities not possible with monolithic equations.

Abstract

Mathematical models are increasingly used in both academia and the pharmaceutical industry to understand how phenotypes emerge from systems of molecular interactions. However, their current construction as monolithic sets of equations presents a fundamental barrier to progress. Overcoming this requires modularity, enabling sub-systems to be specified independently and combined incrementally, and abstraction, enabling general properties to be specified independently of specific instances. These in turn require models to be represented as programs rather than as datatypes. Programmable modularity and abstraction enables libraries of modules to be created for generic biological processes, which can be instantiated and re-used repeatedly in different contexts with different components. We have developed a computational infrastructure to support this. We show here why these capabilities are needed, what is required to implement them and what can be accomplished with them that could not be done previously.