Discovering Fundamental Laws Governing Prokaryotic Adaptation in Surface-to-Surface Transitions Using Data-Driven Inverse Modeling
Abstract
We aim to develop a new mathematical theory for modeling how synthetic and naturally occurring operons in E. coli act as layered computational units to implement models of adaptive learning during phase transitions. In so doing, we aim to lay the foundation for a new generation of biologically inspired and biologically implemented computing systems, capable of low-power learning applications. More precisely, we seek to discover and characterize the time-scales and layered network models that govern surface-to-surface transitions of prokaryotic organisms, e.g. Acinetobacter baylyi. Many prokaryotes exhibit the ability to persist even on nutrient starved and non-organic surfaces, such as metal or plastic nosocomial surfaces. The precise metabolic and adaptation dynamics of these prokaryotes is poorly understood. We propose a data-driven operator theoretic approach to reverse engineer and establish the fundamental rules of learning that enable bacteria to persist in surface-to-surface transitions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 09, 2020
- Source ID
- W911NF2010165
Entities
People
- Enoch Yeung
Organizations
- Army Contracting Command
- United States Army
- University of California, Santa Barbara