Constraint and Contingency Pervade the Emergence of Novel Phenotypes in Complex Metabolic Systems

Abstract

An evolutionary constraint is a bias or limitation in phenotypic variation that a biological system produces. We know examples of such constraints, but we have no systematic understanding about their extent and causes for any one biological system. We here study metabolisms, genomically encoded complex networks of enzyme-catalyzed biochemical reactions, and the constraints they experience in bringing forth novel phenotypes that allow survival on novel carbon sources. Our computational approach does not limit us to analyzing constrained variation in any one organism, but allows us to quantify constraints experienced by any metabolism. Specifically, we study metabolisms that are viable on one of 50 different carbon sources, and quantify how readily alterations of their chemical reactions create the ability to survive on a novel carbon source. We find that some metabolic phenotypes are much less likely to originate than others. For example, metabolisms viable on D-glucose are 1835 times more likely to give rise to metabolisms viable on D-fructose than on acetate. Likewise, we observe that some novel metabolic phenotypes are more contingent on parental phenotypes than others. Biochemical similarities among carbon sources can help explain the causes of these constraints. In addition, we study metabolisms that can be produced by recombination among 55 metabolisms of different bacterial strains or species, and show that their novel phenotypes are also contingent on and constrained by parental genotypes. To our knowledge, our analysis is the first to systematically quantify the incidence of constrained evolution in a broad class of biological system that is central to life and its evolution.

Estimating the predictability of cancer evolution

“…However, recent technological advancements in experimental evolution, high-throughput sequencing and modeling of complex biological systems have revealed some repeatable features in diverse evolutionary processes and pervasive evolutionary constraints in various biological systems (Achaz, 2014; Blount et al , 2018; Ferretti et al , 2018; Hosseini and Wagner, 2017; Lieberman et al , 2011; Miles et al , 2011; Poelwijk et al , 2007; Salverda et al , 2011; Toprak et al , 2012; Weinreich et al , 2006). These repeatable patterns and regularities suggest a predictive theory of evolution, which has been pioneered by studies attempting to predict the future of evolution in various biological systems (Barton et al , 2016; Bull and Molineux, 2008; Cowperthwaite et al , 2008; Luksza and Lässig, 2014; Neher et al , 2014; Nyerges et al , 2018).…”