Evolutionary Innovations and Dynamics in Wagner's Model of Genetic Regulatory Networks

  • Yifei Wang

Student thesis: Doctoral ThesisPhD


The gene regulatory network (GRN) controls the expression of genes providing phenotypic traits in living organisms. In particular, transcriptional regulation is essential to life, as it governs all levels of gene products that enable cell survival and numerous cellular functions. However, there is still poor understanding of how shifts in gene regulation alter the underlying evolutionary dynamics and consequently generate evolutionary innovations. By employing Wagner's GRN model, this dissertation investigates how the interplay of simple evolutionary forces (mutation and recombination) with natural selection acting on gene regulatory dynamics can generate major evolutionary innovations.In this dissertation, firstly, I review all currently available research papers using Wagner's GRN model, which is also employed as the computational model used extensively in the remaining chapters. I then describe how Wagner's GRN model and its variants are implemented. Finally, network properties such as stability, robustness and path length in initial populations are investigated.In the first study, I explore the characteristics of compensatory mutation in the context of genetic networks. Specifically, I find that 1) compensatory mutations are relatively insensitive to the size and connectivity of the network, 2) compensatory mutations are more likely to occur in genes at or adjacent to the site of a previous deleterious mutation and 3) compensatory mutations are more likely to be driven by mutations with a relatively large regulatory impact.In the second study, I further investigate the evolutionary consequences of the properties of compensatory mutation discovered previously. Specifically, I find that 1) compensatory mutations can occur regardless of patterns of selection, 2) networks with compensatory mutations exhibit proportionately higher robustness when compensatory mutations interact closely with deleterious mutations or have large effects on gene regulation, and 3) regulatory complexity can arise as a consequence of the propensity for co-localised and large-effect compensatory mutations.In the third study, I provide a mechanistic understanding of how recombination benefits sexual lineages. Specifically, I find that 1) recombination together with selection for developmental stability can drive populations towards the optimum, 2) recombination does not frequently disrupt well-adapted lineages as conventionally expected, and 3) recombination facilitates finding good genetic combinations which are robust to disruption, although it also rapidly purges weaker configurations.In the final study, I show that the selection pressure acting on rewiring gene regulation is critical to increasing benefits for sexual lineages whilst mitigating costs of sex and recombination. Specifically, I find that 1) strong selection strength can greatly benefit low-fitness sexual lineages, especially at the early stage, 2) recombination is initially costly, but it can rapidly evolve to compensate for costs of sex and recombination, and 3) sexual lineages with low levels of sex and recombination can outcompete strictly asexual populations under higher selection pressure and lower mutation rates.The results presented for all of the studies are important for mechanistically understanding evolutionary innovations through altering transcriptional regulatory dynamics. These innovations include 1) facilitating alternative pathway evolution, 2) driving regulatory complexity, 3) benefiting sexual reproduction, and 4) resisting invasion against asexual lineages.
Date of Award18 Apr 2016
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorJoanna Bryson (Supervisor), Nicholas Priest (Supervisor) & James Davenport (Supervisor)


  • Gene Regulatory Networks
  • Compensatory Mutations
  • Recombination
  • Non-adaptive Evolution
  • Network Dynamics
  • Evolutionary Innovations
  • Regulatory Complexity

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