Embracing Complexity: Integrating Data for Predictive Disease Models

Discover how supercomputing and human biology intersect to revolutionize scientific thinking in this 18-minute TEDMED 2011 talk. Explore the Single Molecule Real Time Observation System and its impact on genetic studies. Understand the limitations of linear thinking and storytelling in scientific research, and learn how embracing complexity through integrated data models can lead to breakthroughs in understanding complex diseases and drug responses. Delve into the process of integrating population data and literature to build predictive models, and examine a case study of a novel phosphatase under development at Merck for Type 2 Diabetes. Gain insights into how these advanced approaches can validate network predictions and potentially transform the future of medical research and treatment development.

Intro
Single Molecule Real Time Observation System (Zero Mode Waveguides)
DNA is but one dimension, and in genetic studies reflects averages in populations: Understanding?
The problem: Our minds are wired for linear thinking, storytelling, not statistical uncertainty
Embracing Complexity: Integrating data to build predictive models of complex disease and drug response phenotypes
Integrating data collected on populations, literature
Models used to interpret targets under development Novel phosphatase under development at Merck for T2D
Pharmacologic validation of the network predictions