UCLA

In this dissertation, we investigate whether evolutionary paths for simple biological systems exhibiting punctuated or intermittent behavior through computational modeling of asexually reproducing unicellular populations. Punctuated equilibrium is a controversial theory proposed by Gould and Eldredge in 1972 [1] to explain observations of species experiencing long periods of stasis followed by bursts of change in the fossil record. The challenges in interpreting the fossil record and in the modeling of complex ecosystems has resulted in limited progress in settling the debate around this conjecture. In our work, we aim to build computational models of a simple biological system to study the behavior of fitness evolution at the population scale where fitness refers to an organism's reproductive success. We are motivated by the results from the ongoing Long-Term Evolution Experiment (LTEE) [2], which observes punctuated patterns in the fitness of E. coli populations grown in a laboratory setting. We construct two models of organismic evolution whose results are consistent with the fitness trends observed in the LTEE. First, we develop a Monte Carlo Wright-Fisher model that we adapt to model the LTEE populations. Our adapted Wright-Fisher model exhibits punctuated patterns at the bacteria's generational time scale. Second, we develop and introduce the smooth-colony dynamics model to study a population of colonies, which is more naturally realistic than the adapted Wright-Fisher model's assumptions and the LTEE's laboratory environment. Our main result shows that while this more biologically realistic model fits the LTEE data, it does not display punctuated patterns in absence of epistasis, or the interactions of genes. This indicates that punctuated behavior at the generation time scale may be limited to specific conditions that may not be naturally occurring. Further work aims to expand the complexity of the smooth-colony dynamics model to provide additional insights into the evolutionary trends of simple biological systems.