Adapting modeling and simulation credibility standards to computational systems biology.

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 forms 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 narrower 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.

Cesium: A public database of evolved oscillatory reaction networks.

New tools and software in systems biology require testing and validation on reaction networks with desired characteristics such as number of reactions or oscillating behaviors. Often, there is only a modest number of published models that are suitable, so researchers must generate reaction networks with the desired characteristics, a process that can be computationally expensive. To reduce these computational costs, we developed a data base of synthetic reaction networks to facilitate reuse. The current database contains thousands of networks generated using directed evolution. The network are of two types: (1) those with oscillations in species concentrations and (2) those for which no oscillation was found using directed evolution. To facilitate access to networks of interest, the database is queryable by the number of species and reactants, the presence or absence of autocatalytic and degradation reactions, and the network behavior. Our analysis of the data revealed some interesting insights, such as the population of oscillating networks possess more autocatalytic reactions compared to random control networks. In the future, this database will be expanded to include other network behaviors.