Bioinspired Concepts: Unified Theory for Complex Biological and Engineering Systems

Abstract

The overall long-term objective of our research is to develop mathematical and software infrastructure in support of post-genomics research in systems biology. One near-term objective articulated in this abstract centers on a deeper understanding of the organizational principles of biological networks. A distinguishing theme of this work is its focus on scalable methods of robustness and model (in)validation with data, as opposed to relying purely on simulation. In computability terms, if simulation is viewed as a way to attack the NP hard side of biological problems, our approach attacks the coNP side. Much of the success of reductionist biology has depended on creative individuals who draw biologically meaningful inferences from data and computation using small scale and informal reasoning. This type of inference was critical because the reductionist research program itself offered no systematic tools to deal with complexity, only with the component parts. Far from being dispensed with, this reasoning process and its biological content must be both formalized and made rigorous, systematic, and scalable as well, and ultimately teachable. This requires the development of new mathematics as well as algorithms and software.A central goal of modeling and simulation is to connect molecular mechanisms to network function to questions of biomedical relevance. Unfortunately, many of the most critical questions involve events which are extremely rare at the individual cell level where the mechanisms act yet catastrophic to the organism. Thus simulation methods that may be adequate for studying generic or typical behavior are entirely inadequate to explore such worst-case scenarios, which with conventional methods are computational intractable. We are extending the best-practice tools and algorithms for robustness analysis that have become standards in engineering to models of biological relevance, which are typically nonlinear, hybrid, uncertain, and stochastic. This includes integrating formal inference methods from the previously fragmented theories in Computer Science with those of Control and Dynamical Systems. This involves deep mathematical challenges that parallel those for technological networks, for which we have made dramatic progress, and on which we are building new tools for systems biology.DppWTJIToM ST ýIFbr A Distriibution Un'lhrrited 20U0U50608 054

A methodological framework for Eco-Industrial Park design and optimization

“…Formalizing IS concept for EIP Modern principles of industrial system design and operation are emerging by inspiration from biological systems, adapting natural principles to industrial applications (Doyle 2006) and the biological term of "symbiosis" has been assimilated for the engineering systems.…”