The Inference of Nature: Cause and Effect in Molecular Biology

Theoretical approaches have always played an important role in biology, dating back to Mendel’s peas. In today’s era of genomics and big data in biology, statistical and computational tools are even more vital for biologists seeking to infer causation in living systems. To illustrate the range of methods, from modelling to machine learning, and how they contribute to understanding biological mechanisms, Dr. Teichmann will pick examples from some of the core problems her lab has been investigating as case studies. Starting with the assembly of proteins into complexes, she will move on to the single cell revolution in genomics and her quest to develop cell atlases of the human body. She will finish with her work leveraging these atlases to understand how COVID-19 affects the body. The examples will show how intertwined and complementary ‘wet’ (i.e. experimental) and ‘dry’ (i.e. computational/theoretical) approaches are in today’s biology.

The conversant for the August 31, 2021 discussion will be Neil Lawrence, the inaugural DeepMind Professor of Machine Learning at the University of Cambridge.  He has been working on machine learning models for over 20 years.  He recently returned to academia after three years as Director of Machine Learning at Amazon.  Professor Lawrence’s main interest is the interaction of machine learning with the physical world.  This interest was triggered by deploying machine learning in the African context, where ‘end-to-end’ solutions are normally required.  This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute.  Professor Lawrence is also visiting Professor at the University of Sheffield and the co-host of Talking Machines.