Evolutionary & Ecological Dynamics
As a grad student in the Hallatschek lab, I’ve been interested in understanding what evolutionary, ecological, and eco-evolutionary forces drive the dynamics of experimental microbial communities.
Local Adaptive Landscape of a Nascent Bacterial Community The effects of all possible mutations largely shape the adaptive routes available to an organism. But most species coevolve in a community, not alone. So how does adaptive potential depend on (changing) ecological conditions? To find out, we measured mutational fitness effects of a small community of closely related microbes—S & L from the E. coli LTEE—and the ancestor, while altering the (a)biotic environment. We did this by making genome-wide barcoded transposon libraries on all three backgrounds. We saw a sensitive dependence of fitness effects on genetic background & environment and investigated how that happened. We also saw correlations between evolutionary outcomes and measured fitness effects, among other findings. We’re currently following up on some of our observations to test how eco-evolutionary feedback may emerge from an ecologically-dependent fitness landscape.
Ascensao JA, Wetmore KM, Good BH, Arkin AP, Hallatschek O. Quantifying the Local Adaptive Landscape of a Nascent Bacterial Community. Nature Communications 14, 248 (2023). https://doi.org/10.1038/s41467-022-35677-5 [bioRxiv]
Evolution of the Strength of Genetic Drift Together with QinQin Yu, we’ve been using transposon-barcoded libraries to measure the strength of genetic drift (offspring number variance), the stochastic force affecting allele frequency trajectories, in populations from the LTEE. We developed, tested, and verified a computational pipeline and inferential method to quantify the strength of genetic drift from barcode frequency trajectory fluctuations. We found the fluctuations from genetic drift are larger than would be expected purely from sampling, indicating other sources of stochasticity in the growth process. The strength of drift also differs between populations separated by evolution. We’re using single-cell microscopy along with theory to better understand how the strength of drift emerges from single-cell growth traits and the evolutionary processes that drive changes in the strength of drift.
Stochasticity & Determinism in Ecological Dynamics Empirical observations such as Taylor’s law suggest that the process setting community composition is generally stochastic. However, we often have limited ability to precisely observe and perturb the system to uncover the underlying ecological process that cause the stochasticity. We found that a simple LTEE-derived community displays large ecotype frequency fluctuations that are sensitive to small environmental perturbations. Using precise ecotype frequency and abundance measurements along with systematic system perturbations, we have been characterizing the deterministic and stochastic components of the dynamics across a range of timescales. This project is currently ongoing.
Ecological Rediversification While working with the S and L ecotypes from the LTEE, we noticed that when the S ecotype (from multiple evolutionary time points) was propagated in monoculture, sometimes larger colonies would appear on certain agar plates—as this stable variant arose from the original S type, we called it SB. We found that S and SB can coexist via negative frequency dependence. SB shares some metabolic traits with L, including some not directly relevant for its environment, while diverging in other ways. We are continuing to probe this system as a model for rapid ecological diversification.
When I was an undergraduate, I was mentored by Oleg Igoshin, where we were interested in out-of-equilibrium dynamics of microbial transcriptional networks. Understanding how dynamical responses of biological networks are constrained by underlying network topology is one of the fundamental goals of systems biology. To this end, we asked which properties of the network allow systems to have a non-monotonic time-response (first increasing and then decreasing) to a monotonically increasing signal. We showed that the networks displaying such responses must include indirect negative feedback or incoherent feedforward loop. Applying this result to the measured non-monotonic expression for glyoxylate shunt genes in Mycobacterium tuberculosis, a network known to be important to mycobacterial virulence, we showed that the currently postulated network structure does not match the predictions of the theorem. Using a combination of mathematical modeling and follow-up experimental tests we predicted a novel incoherent loop in the network.
Ascensao JA*, Datta P*, Hancioglu B*, Sontag E*, Gennaro ML, Igoshin OA. Non-monotonic Response to Monotonic Stimulus: Regulation of Glyoxylate Shunt Gene-Expression Dynamics in Mycobacterium tuberculosis. PLoS Comput Biol (2016) 12: e1004741. DOI: 10.1371/journal.pcbi.1004741
During an undergrad summer, I worked in Judith Blake’s lab at the Jackson Laboratory in Maine, which works on the development and curation of biomedical ontologies. Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. We developed a novel set of statistical methods to suggest Gene Ontology (GO) functional annotations from patterns of Mammalian Phenotype (MP) Ontology annotations. We showed that our method is capable of inferring high-quality functional annotations from curated phenotype data, assisting in the efforts of expert curators. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by semantics-based approaches.
Ascensao JA*, Dolan ME*, Hill DP, Blake JA. Methodology for the inference of gene function from phenotype data. BMC Bioinformatics (2014) 15: 405. DOI: 10.1186/s12859-014-0405-z
Additionally, I worked in Adam Arkin’s lab as an Amgen scholar during summer 2015, where I worked on applying principals of dynamical systems and transcriptional kinetics to design novel genetic circuits. From 2016-17, I was a Fulbright Scholar in Jordi Garcia-Ojalvo’s lab, working on microbial systems biology and decision making; and crucially, where I learned the bulk of my wet lab skills.