Computational algorithms as biological switches

Biological systems have developed a wide range of mechanisms to respond to changes in their environment. Biological switches are mechanisms which drive a change in the functional state of a system in an all-or-none fashion. Biological switches produce a reliable and robust transition between states, sometimes generating an irreversible transition.

Current eukaryotic switches usually contain several components with multiple positive feedback loops. This level of complexity could have been reached by an evolutionary process from a simple system. The simplest possible system could have been a single molecule that regulates itself and it can go through different functional states. Most of the quickly responding switches work on the post- translational level. Phosphorylation has been proposed as an ancient post-translational mechanism, which could have defined phosphorylated and dephosphorylated states.

It was previously shown that simple systems based on single autocatalytic element could behave like a switch. Therefore, maybe there exist an evolutionary way of obtaining complex networks from simpler ones. We investigate from a computational perspective, the increasing complexity from simple systems to complex ones and how their key properties could have been kept through evolution.