Distribution shapes govern the discovery of predictive models for gene regulation
Abstract
Systems biology seeks to combine experiments with computation to predict biological behaviors. However, despite tremendous data and knowledge, biological models make less-accurate predictions compared with other fields. By analyzing single-cell, single-molecule measurements of mRNA during yeast stress response, we explore why and how the shapes of experimental distributions control prediction accuracy. We show how asymmetric data distributions with long tails cause standard modeling approaches to yield excellent fits but make meaningless predictions. We show how these biases arise from the violation of fundamental assumptions in standard modeling approaches. We demonstrate how advanced computational tools solve this dilemma and achieve predictive understanding of spatiotemporal mechanisms of transcription control including RNA polymerase initiation and elongation and mRNA accumulation, transport, and decay.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 29, 2018
- Source ID
- 10.1073/pnas.1804060115
Entities
People
- Brian Munsky
- Douglas P. Shepherd
- Gregor Neuert
- Guoliang Li
- Zachary R. Fox
Organizations
- Colorado State University
- Defense Threat Reduction Agency
- National Institute of General Medical Sciences
- Office of the Director
- University of Colorado
- Vanderbilt University