Molecule-Inspired Methods for Coarse-Grain Multi-System Optimization

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 advantages 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 simulation. 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 understand systems such as living cells and large neuronal assemblies. With large-scale applications of this prototype in sight, we propose three basic optimization principles 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.