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Photo of Dr Brandon M. Invergo

Dr Brandon M. Invergo

Research Fellow


Research Interests

  • evolutionary systems biology
  • bioinformatics, statistics & machine learning
  • intracellular protein signaling
  • molecular evolution
  • systems modeling
  • visual phototransduction
  • molecular parasitology of apicomplexa (Plasmodium, Toxoplasma)

Research Summary

My research is generally in the area of evolutionary systems biology, wherein I am interested in the structure, dynamics and evolution of intracellular-protein signaling networks.  In particular, I focus on how natural selection acts on pathway-level signaling dynamics. For example, how evolvable are non-linear dynamic traits that are determined by the interactions of multiple proteins? How significantly do signaling dynamics vary between individuals, populations, or species? Where has natural selection produced unusually fast/slow dynamics? At what point do altered dynamics become dysregulation and disease?  Together, this requires: a) measuring signaling processes, b) reconstructing signaling pathways and analyzing them through mathematical models, c) modeling molecular evolutionary histories, and d) performing comparative analyses of protein signaling.

Initially I worked on this topic during my PhD research in Barcelona (Pompeu Fabra University) via mathematical models and bioinformatics for the visual phototransduction system.  As an ESPOD fellow at the European Bioinformatics Institute and the Wellcome Trust Sanger Institute, I further broadened my approach to include quantitative mass spectrometry and integrative 'omics analyses.  I applied these methods to investigate rapid phosphorylation-based signaling in malarial parasites and to predict novel signaling relationships between human protein kinases.

As a research fellow at the Translational Exchange @ Exeter (TREE), I will continue in this direction with the aim of gaining a clearer understanding of sequence-level determinants of variation in complex signaling dynamics in the contexts of evolution and disease.