Nate Goehring is a Senior Group Leader who leads the Polarity and Patterning Networks lab at the Crick.
Nate obtained his BA in biology at Amherst College, USA, and was a Fulbright Scholar in the laboratory of Peter Overath at the Max Planck Institute for Biology (Germany), where he worked on cell surface proteins of Leishmania parasites. As a PhD student, he trained with Jon Beckwith at Harvard Medical School as a Howard Hughes Fellow, working on the mechanisms of bacterial cell division.
He then pursued postdoctoral training at the Max Planck Institute of Molecular Cell Biology and Genetics (Germany) where he began his work on cell polarity and patterning in the C. elegans embryo with Tony Hyman and Stephan Grill, supported by fellowships from the Max Planck Society, the Alexander von Humboldt Society and the EU/Marie Curie Training Programme.
In 2013, Nate started his research group at the Cancer Research UK London Research Institute at Lincoln's Inn Fields, which in 2016 became part of the Francis Crick Institute.
Nate's research aims to unravel the design principles that underlie the emergence of cell polarity and how it is integrated into developmental programmes to define cell fate, form and function. Work in his lab combines genetics, quantitative cell biology, biophysical analysis, and computational approaches to bridge scales between molecular, cellular, and embryo-level behaviours. His specific research program focuses on the highly conserved PAR cell polarity network that underlies cell polarity in epithelia, migrating cells, and at the earliest stages of development where polarity is intimately linked to the organisation and fate specification of embryonic blastomeres. Nate is particularly fascinated by how information flows through this network: how does this system sense and integrate spatiotemporal information, how is it encoded and stored by the network, and how are outputs of the network integrated with and read out by developmental pathways? Key to his research efforts is a multidisciplinary team and successful collaborations across the biology/physics interface, from structural biology and biochemistry to theoretical physics and machine learning.
