Information Technology Reference
In-Depth Information
Chapter 14
Molecule-Inspired Methods for Coarse-Grain
Multi-System Optimization
Max H. Garzon and Andrew J. Neel
Abstract
A major goal in multi-objective optimization is to strike a compromise
among various objective functions subject to diverse sets of conflicting constraints.
It is a reality, however, that we must face optimization of entire systems in which
multiple objective sets make it practically impossible to even formulate objective
functions and constraints in the standard closed form. We present a new approach
techniques inspired by biomolecular interactions such as embodied in DNA. The ad-
vantages are more comprehensive and integrated understanding of complex chains
of local interactions that affect an entire system, such as the chemical interaction of
biomolecules in vitro, a living cell, or a mammalian brain, even if done in simula-
tion. We briefly describe a system of this type, EdnaCo (a high-fidelity simulation
in silico of chemical reactions in a test tube in vitro), that can be used to under-
stand systems such as living cells and large neuronal assemblies. With large-scale
applications of this prototype in sight, we propose three basic optimization prin-
ciples critical to the successful development of robust synthetic models of these
complex systems: physical-chemical, computational, and biological optimization.
We conclude with evidence for and discussion of the emerging hypothesis that
multi-system optimization problems can indeed be solved, at least approximately,
by so-called coarsely optimal models of the type discussed above, in the context of
a biomolecule-based asynchronous model of the human brain.
