Systemic stability, cell differentiation, and evolution - A dynamical systems perspective

Species or population that proliferate faster than others become dominant in numbers. Catalysis allows catalytic sets within a molecular reaction network to dominate the non catalytic parts of the network by processing most of the available substrate. As a consequence one may consider a ’catalytic fitness’ of sets of molecular species. The fittest sets emerge as the expressed chemical backbone or sub-network of larger chemical reaction networks employed by organisms. However, catalytic fitness depends on the systemic context and the stability of systemic dynamics. Unstable reaction networks would easily be reshaped or destroyed by fluctuations of the chemical environment. In this paper we therefore focus on recognizing systemic stability as an evolutionary selection criterion. In fact, instabilities of regulatory systems dynamics become predictive for associated evolutionary forces driving the emergence large reaction networks that avoid or control inherent instabilities. Systemic instabilities can be identified and analyzed using relatively simple mathematical random networks models of complex regulatory systems. Using a statistical ensemble approach one can identify fundamental causes of instable dynamics, infer evolutionary preferred network properties, and predict evolutionary emergent control mechanisms and their entanglement with cell differentiation processes. Surprisingly, what systemic stability tells us here is that cells (or other non-linear regulatory systems) never had to learn how to differentiate, but rather how to avoid and control differentiation. For example, in this framework we can predict that regulatory systems will evolutionary favor networks where the number of catalytic enhancers is not larger than the number of suppressors.