Predictive Phenomics
Predictive Phenomics
Predictive Phenomics will address the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems.
Illustration by Stephanie King
The genome revolution has provided a current technical capability to sequence organisms at a pace that delivers 50–100 billion kilobytes of data per day. However, for scientists to transform this data to innovative biological solutions, there must be science that overcomes the current standard of inferring biological function from gene data. Realizing the full potential of biology on the economy and society will require moving beyond genome to function.
The initiative’s research is organized into three science and technology thrust areas:
- enhancing multi-scale phenomics measurements;
- identifying molecular patterns of biological function; and
- developing computational methods to identify phenotypic signatures for predicting and controlling the phenome.
To overcome the inference gap, the Predictive Phenomics Science and Technology Initiative team will use a reverse genomics approach to target the molecular basis of function. The initiative’s strategy will not solely rely on genomic information. The reverse genomic strategy (metabolome>proteome>genome) will derive functional information by examining the relationships between proteins and metabolites as molecular surrogates of function without having to ascribe function to associated genes.
The initiative seeks to advance experimental and computational sciences to illuminate how the functions of biological systems and their interactions with the environment produce a phenotypic response. These advances require development of multi-scale phenomics measurements, identification of molecular patterns of biological function, and development of computational methods to identify phenotypic signatures for predicting and controlling the phenome.
Coordinated biological systems relevant to key PNNL sponsors are being used across experimental and technological platforms to set data standards and collect large-scale data, build models of the dynamics and molecular interactions of biosystems that will accurately predict phenotypic response to environmental and genetic perturbations, and enable rational strategies for their design and control.