Random biochemical networks: the probability of self-sustaining autocatalysis.
We determine conditions under which a random biochemical system is likely to contain a subsystem that is both autocatalytic and able to survive on some ambient 'food' source. Such systems have previously been investigated for their relevance to origin-of-life models. In this paper we extend earlier work, by finding precisely the order of catalysation required for the emergence of such self-sustaining autocatalytic networks. This answers questions raised in earlier papers, yet also allows for a more general class of models. We also show that a recently described polynomial-time algorithm for determining whether a catalytic reaction system contains an autocatalytic, self-sustaining subsystem is unlikely to adapt to allow inhibitory catalysation--in this case we show that the associated decision problem is NP-complete.
Mossel
Elchanan
E
Statistics Department, UC Berkeley, CA, USA. mossel@stat.berkeley.edu
Steel
Mike
M
D001669:Biochemical Phenomena; D002384:Catalysis; D006706:Homeostasis; D008956:Models, Chemical; D011336:Probability