Predicting metabolic adaptation from networks of mutational paths.

摘要: Competition for substrates is a ubiquitous selection pressure faced by microbes, yet intracellular trade-offs can prevent cells from metabolizing every type of available substrate. Adaptive evolution constrained these trade-offs, but their consequences the repeatability and predictability are unclear. Here we develop an eco-evolutionary model with metabolic trade-off to generate networks mutational paths in microbial communities show that have descriptive predictive information about communities. We find long-term outcomes, including community collapse, diversity, cycling, characteristic evolutionary dynamics determine entropy, or repeatability, paths. Although reliable prediction outcomes environmental conditions difficult, graph-theoretic properties enable accurate even incomplete observations. In conclusion, present novel methodology analyzing adaptive report adaptation key variable success. The structure reflect ability use different resources. Here, Josephides Swain incorporate into predict