(DEPSCOR-RC FY21) INFERRING NETWORK MODELS FROM SPATIAL SNAPSHOTS FOR SYSTEMS AND SYNTHETIC BIOLOGY APPLICATIONS
Abstract
Recent advances in measurement technology are now enabling simultaneous assessment of thousands of gene expression levels across thousands of spatial positions in biological tissues and microbial cultures. Such rich datasets offer massive potential for revealing the underlying mechanisms that control gene regulation, but current mathematical frameworks for inferring mechanistic regulatory networks are lagging the measurement capabilities. We aim to advance the inference of computational models from spatial ‘snapshots’ of biological data that can ultimately advance several DoD-relevant applications in systems and synthetic biology. This work will pursue the following three objectives: (1) Characterize spatial properties in baseline Hill function differential equation models, (2) Develop inference methods for spatial snapshot data, and (3) Determine inference performance against experimental validation data. If successful, our proposed work has the potential for several significant outcomes including better informed design of spatial expression experiments, better control over synthetic biology approaches related to spatial patterning, and better mechanistic discovery of the key regulatory drivers of DoD applications like wound healing, neuroscience, and microbiome engineering.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 20, 2023
- Source ID
- FA95502210379
Entities
People
- W. E. Richardson
Organizations
- Air Force Office of Scientific Research
- Office of the Secretary of Defense
- University of Arkansas System