Person:
McGrath, Patrick T.

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https://hdl.handle.net/1853/71512
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    Using Mechanistic Modeling to Understand Non-Linear and Age-Dependent Effects of Genetic Factors
    (Georgia Institute of Technology, 2015-08-31) McGrath, Patrick T. ; Georgia Institute of Technology. School of Physics ; Georgia Institute of Technology. School of Biology
    Differences in DNA sequence between two individuals or cells can cause diseases like autism and cancer. The McGrath lab is interested in understanding how changes in DNA lead to observable differences among individuals of a species. This basic goal has been difficult due to the complexity of the relationship between genotype and phenotype. In order to improve our ability to predict how genetic variants influence reproductive rates in the nematode C. elegans, we are using a mechanistic model of the egg-laying process based upon years of accumulated experiments. This model is able to explain seemingly non-linear interactions between two genetic variants as well as predict the effect age has on the effect of the genetic variant. Application of this model improves our ability to understand how the genetic factors control this trait.
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    50 Years of Solitude: Lessons in Evolution from a Neglected Strain of C. elegans
    (Georgia Institute of Technology, 2017-01-10) McGrath, Patrick T. ; Georgia Institute of Technology. Institute for Bioengineering and Bioscience
    Most biological traits have a strong genetic, or heritable, component. Understanding how genetic variation influences these phenotypes will be important for understanding common, heritable diseases like autism. However, the genetic architecture controlling most biological traits is incredibly complex – hundreds of interacting genes and variants combine in unknown ways to create phenotype. The McGrath lab is interested in using fundamental mechanistic studies in C. elegans to identify, predict, and understand how genetic variation impacts the function of the nervous system. We are studying laboratory adapted strains and harnessing directed evolution experiments to understand how genetic changes affect development, reproduction, and lifespan. We combine quantitative genetics, CRISPR/Cas9, genomics, and computational approaches to address these questions. We believe this work will lead to insights into evolution, multigenic disease, and systems biology.
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    Analysis of social behavior in Lake Malawi cichlids using automated behavior phenotying
    ( 2021-09-20) McGrath, Patrick T. ; Georgia Institute of Technology. Neural Engineering Center ; Georgia Institute of Technology. School of Biological Sciences
    In the wild, behaviors are often expressed over long time periods in complex and dynamic environments, and many behaviors include direct interaction with the environment itself. However, measuring behavior in naturalistic settings is difficult, and this has limited progress in understanding the mechanisms underlying many naturally evolved behaviors that are critical for survival and reproduction. Here we describe an automated system for measuring long-term bower construction behaviors in Lake Malawi cichlid fishes, in which males use their mouths to sculpt sand into large species-specific structures for courtship and mating. We integrate two orthogonal methods, depth sensing and action recognition, to simultaneously track the developing bower structure and the thousands of individual sand manipulation behaviors performed throughout construction. As an example of the utility of this system, we will demonstrate how it can be used with single nuclei RNAseq to identify cellular populations activated during bower building behavior.