Adaptive and Non-Adaptive Evolution of the Control of Gene Expression
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PublisherThe University of Arizona.
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AbstractNon-adaptive evolution refers to evolutionary processes that are primarily driven not by natural selection, but by factors such as a bias towards generating certain mutations over others. Although non-adaptive evolution is supported by abundant data, it is obscure outside the field of evolutionary biology, potentially for historical reasons. Considering non-adaptive evolution helps us to understand the origins and roles of traits at molecular and cellular levels, where research is often dominated by adaptationist assumptions. To demonstrate that a balanced view on evolution is necessary, my thesis research asks how adaptive and non-adaptive evolution shape the control of gene expression. I start by simulating the evolution of mechanisms for quality control of gene expression. I show that the error rate associated with gene expression is determined by both the mutational bias that tends to increase the error rate and by the effective population size of the species, which determines the strength of natural selection on the error rate. This offers an explanation for the observed non-monotonic relationship between transcriptional error rate and effective population size. I next study the evolution of transcriptional regulatory networks (TRNs). The adaptationist view hypothesizes that the enrichment of a subnetwork called coherent type 1 feed-forward loops (C1-FFLs) in TRNs is an adaptation for filtering out short spurious signals, but this and similar hypotheses about other enriched subnetworks are widely questioned by evolutionary biologists, because the adaptive hypothesis fails to consider network topologies that evolve non-adaptively. To help resolve this debate, I developed a highly mechanistic computational model that captures non-adaptive factors that can shape the topology of TRNs. I show that functional C1-FFLs evolve readily under selection for filtering out a spurious signal, but not under control selection conditions. While this result supports the adaptive origin of C1-FFLs, I show that non-adaptive subnetworks can also be enriched in TRNs evolved for filtering out a spurious signal, suggesting that inferring functions of TRNs from topology alone can be problematic. A further complication comes from the fact that a subnetwork that is topologically different from C1-FFLs also evolves to filter out spurious signals. In conclusion, I argue that non-adaptive evolution can explain the origins and roles of traits that are difficult to understand under adaptationism, and that considering non-adaptive evolution is necessary to carry out scientific research in all fields of biology. Molecular and cellular biologists should actively consider non-adaptive evolution in their research.
Degree ProgramGraduate College
Molecular & Cellular Biology