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Ph.D. Defense: Abhijit Mondal
November 22, 2022 @ 11:30 am - 12:30 pm EST
Title: Algorithms for Understanding and Dating Microbial Evolution Through Horizontal Gene Transfer
Ph.D. Candidate: Abhijit Mondal
Major Advisor: Dr. Mukul S. Bansal
Associate Advisors: Dr. Ion Mandoiu, Dr. Derek Aguiar
Date/Time: Tuesday, Nov 22, 2022, 11:30 am
Location: Library 1102
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m1c0f9deaeb6ebb1b6a8c9281fe33cef2
Meeting number: 2622 280 0647
Join by phone: + 1-415-655-0002 US Toll
Access code: 2622 280 0647
Abstract:
Horizontal gene transfer (transfer for short) is an important driver of microbial evolution. Recent computational advances in the study of microbial gene families have now made it possible to infer transfers efficiently and with relatively high accuracy. Despite the resulting ability to efficiently infer transfers, several fundamental aspects of transfers remain poorly understood. Furthermore, there are several important problems in the study of microbial evolution that could benefit from using the evolutionary information present in inferred transfers. In this dissertation, we focus on the development of new computational methods that can leverage these recent computational advances for transfer inference to (i) distinguish between two types of transfers, additive and replacing, (ii) better understand the scale of transfer events by systematically detecting horizontal transfer of protein domains, and (iii) use inferred transfers to improve microbial phylogenetic dating.
Our first contribution is the development of a supervised machine learning approach, called ARTra, for distinguishing between additive and replacing transfers. The complexity of microbial evolution makes it difficult to computationally distinguish between these two types of transfer. ARTra uses as features the classifications provided by several simple classification rules, along with phylogenetic information, and ensembles them to produce a more accurate classification. Our second contribution is to generalize an existing computational framework, called the Domain-Gene-Species (DGS) reconciliation framework, that allows for the co-inference of gene-level and protein domain-level evolutionary events in multi-cellular eukaryotes (which generally have negligible transfer), by allowing for the spread of genes and protein domains across species boundaries through horizontal transfer. We show that the problem of computing optimal generalized DGS reconciliations is NP-hard but approximable to within a constant factor, provide efficient heuristics for the problem, and demonstrate the impact of the new framework using simulated and real biological data. Finally, our third contribution is the development of a new, constrained optimization-based computational method, called DaTeR, to improve microbial phylogenetic tree dating by using transfers to impose relative constraints on dates assigned to different regions of the phylogenetic tree. We demonstrate the effectiveness and utility of DaTeR by applying it to real biological data.
