To improve protein folding simulations, we investigated a new search strategy in combination with the simple genetic algorithm on a two-dimensional lattice model. This search strategy, we called systematic crossover, couples the best individuals, tests every possible crossover point, and takes the two best individuals for the next generation. We compared the standard genetic algorithm with and without this new implementation for various chain lengths and showed that this strategy finds local minima with better energy values and is significantly faster in identifying the global minimum than the standard genetic algorithm.

This article gives an overview of recent developments in the modelling of the structure, control and optimality of metabolic networks. In particular, methods of algebraically analysing the topology of such networks are presented. By these methods, conservation relations and elementary modes of functioning (biochemical routes) can be detected. The principles of metabolic control analysis are outlined. Various recent extensions of this theory are presented, such as an analysis in terms of time dependent variables and modular analysis. Evolutionary optimisation principles are applied to explain the catalytic efficiency of single enzymes as well as the structural design of metabolic pathways. Special results concern the optimal distribution of ATP consuming and ATP producing reactions in glycolysis.