title: Random biochemical networks: the probability of self-sustaining autocatalysis.
abstract: 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.
authors.affiliation: Statistics Department, UC Berkeley, CA, USA. mossel@stat.berkeley.edu
authors.forename: Elchanan
authors.initials: E
authors.lastname: Mossel
authors.forename: Mike
authors.initials: M
authors.lastname: Steel
mesh_terms: D001669:Biochemical Phenomena; D002384:Catalysis; D006706:Homeostasis; D008956:Models, Chemical; D011336:Probability