Adapting Modeling and Simulation Credibility Standards to Computational Systems Biology

TL;DR

This paper reviews existing credibility standards for computational models in various fields and proposes developing a specific credibility standard tailored for systems biology models, which are becoming increasingly complex and influential.

Contribution

It analyzes current standards across disciplines and advocates for a dedicated credibility assessment framework for systems biology models.

Findings

Existing standards are mostly qualitative and lack specific recommendations.

Systems biology models are increasing in complexity and importance.

A tailored credibility standard for systems biology is necessary.

Abstract

Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as form of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrow domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models.