Algorithms and models for complex natural systems

Analysing genomic data and complex natural phenomena in computational terms enhances our comprehension of both nature and computation. Thus, a cross fertilization of algorithms and models for natural complex systems at molecular, cellular, or higher levels, became an active research area, and a more in depth investigation of mutual relationships, synergies, similarities, and differences should be encouraged. This special issue is meant to foster novel hybrid approaches, including general methods of (bio) informatics and synthetic biology, as well as to present the new emerging research concerned with the study and analysis of genome organization, and with the design, modelling, and implementation of bio-inspired evolvable systems. Of particular interest are (unconventional) computational techniques designed to increase our understanding of the evolution of biological life, such as algorithms to infer gene structure and functioning, and parallel distributed computational models involving mechanisms of recognition, affinity based discrimination, reactivity and adaptation to the environment, and spatial search and moving. After a peer review process, 6 manuscripts were accepted for inclusion in this special issue. In ‘‘Combining flux balance analysis and model checking for metabolic network validation and analysis’’, by Roberto Pagliarini, Mara Sangiovanni, Adriano Peron, and Diego Di Bernardo, the authors present a novel useful approach for extracting relevant qualitative information from a metabolic network model, integrating constraintbased techniques with model checking methods. This new computational approach may be helpful in understanding the mechanisms governing the onset and progression of human metabolic-related disorders. It was applied to a simulation and analysis of a well-known inherited disease (primary hyperoxaluria type I) where the lack of a particular liver enzyme causes the body to accumulate excessive amounts of oxalate, leading to renal failure. InA hybrid method for inversion of 3DDC resistivity login measurements, by Ewa Gajda-Zagorska, Robert Schaefer, Maciej Smolka, Maciej Paszynski, and David Pardo, the authors present a new hybrid method for solving the challenging inversion of 3D direct current (DC) resistivity logging measurements. This methodology consists of a hp hierarchic genetic strategy (hp-HGS), and a gradient based optimization method for a local search. The problem has been formulated as a global optimization problem, and simulations have been performed using a self-adaptive hpfinite element method. The experimental results demonstrate the suitability of the proposed method for the tackled inversion problem. In An evolutionary procedure for inferring MP systems regulation functions of biological networks, by Alberto & Mario Pavone mpavone@dmi.unict.it