DigiPhen: a new paradigm for building predictive models of biological systems

Abstract

Reengineered biological systems have the potential to revolutionize chemical and material production, enhance critical mineral recovery, serve as threat sensors and improve human health. Unfortunately, the extreme complexity of organisms has made it difficult to achieve this potential in all but the simplest cases. Recent technological advances, however, have provided a foundation for solving this problem. Here, we describe a capability for accelerating the reengineering of cells by providing accurate predictions of the impact of genetic or environmental changes on cell phenotype. This digital phenome platform (DigiPhen) consists of integrated experimental, analytical and modeling workflows for building a digital representation of microbial or plant systems. It is designed around an expanding set of interchangeable, interconnecting software and experimental modules that can accurately represent the mechanistic determinants of phenotype. The DigiPhen platform will systematically collect data on cell composition, spatial organization, metabolic pathways and regulatory networks in a semi-autonomous fashion and use this information to build modular, multi-scale models of biological systems. These models will be used to predict molecular and environmental changes needed for producing desired biological outcomes. DigiPhen is intended to be the heart of community research campaigns that will meet the immediate needs of individual researchers while fulfilling long-term goals of the scientific community. Altogether, the DigiPhen platform represents a new paradigm for building predictive models of biological systems.