Making Personal Sense of Disease: Machine Learning and a New Calculus of Fitness

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Olivier Lichtarge, MD, PhD

 Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX

Résumé:

The relationship between genotype and phenotype shapes evolution in the long run, and human health every day. Although a complete solution may seem intractable, we will show two complementary approaches that provide insights into disease mechanisms and drug targets. First, we apply machine learning to networks of data and text. In p53 biology, malaria, and for drug discovery, these networks connect genes to chemicals and to diseases in ways that are novel, predictive, and which illustrate how data integration may yield automated discovery of new therapeutic paths. For precision medicine, however, we must also understand the role of individual genome variants in disease. For this we show a second approach: a new calculus of fitness landscapes that measures the co-evolution of DNA and fitness. A differential part quantifies the impact of mutations on proteins, patients and populations, including morbidity and mortality. An integral part solves which genes drive a phenotype, such as, antibiotic resistance in bacteria, tumors in cancers, and cognition in autism or in Alzheimer’s disease. Much work remains, but, so far, both approaches appear to be general, unbiased, scalable and complementary. Together they point to new disease genes and potentially will help guide clinical decisions tailored to each patient.

Biographie:

Olivier Lichtarge is Director of the Computational and Integrative Biomedical Research Center at Baylor College of Medicine, where he holds the Cullen Chair as a Professor in the Department of Molecular and Human Genetics. His computational Laboratory is at the interface between bioinformatics, machine learning and evolutionary theory and works on applications from bacterial to cancer biology. An early contribution was the Evolutionary Trace (ET) method to predict protein functional sites and associated studies of the allosteric mechanism of signal transduction in G protein coupled receptors. Building on this work, his laboratory developed more recently a formalism describing the impact of individual coding mutations on protein function. This Evolutionary Action (EA) theory formulates a general and computable differential equation for the co-evolution of genotype and phenotype. This equation is notable for consistently performing well against the most advanced machine learning methods at the blinded CAGI challenges, and recovering the distribution of fitness effect predicted by Fisher, in 1930. In Mendelian diseases, and some cancers, the EA equation is also predictive of morbidity and mortality. Other contributions are analyses of heterogeneous networks and of the literature by text mining leading to the automated generation of hypotheses. He was trained in Mathematics and Physics at McGill (B.Sc. first class joint honors), in Biophysics and Medicine at Stanford (M.D., Ph.D., with Oleg Jardetzky and Bruce Buchanan), and in Internal Medicine and Endocrinology at UCSF, where he also completed a postdoc in Pharmacology (with Fred Cohen and Henry Bourne). He learned most, however, from his spouse of 30 years, Karen Urbani M.D., and their three children.