Differential Methods for Assessing Sensitivity in Biological Models
Abstract
Over the past few decades, mathematical modeling has become an indispensable tool in the biologists toolbox. From deterministic to stochastic to statistical models, computational modeling is ubiquitous in almost every field of biology. Because model parameter estimates are often noisy or depend on poorly understood interactions, it is crucial to examine how both quantitative and qualitative predictions change as parameter estimates change, especially as the number of parameters increases. Sensitivity analysis is the process of understanding how a models behavior depends on parameter values. Sensitivity analysis simultaneously quantifies prediction certainty and clarifies the underlying biological mechanisms that drive computational models. While sensitivity analysis is universally recognized to be an important step in modeling, it is often unclear how to best leverage the available differential sensitivity methods. In this manuscript we explain and compare various differential sensitivity methods in the hope that best practices will be widely adopted. We stress the relative advantages of existing software and their limitations. We also present a new numerical technique for computing differential sensitivity.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 13, 2022
- Accession Number
- AD1203683
Entities
People
- Alfonso Landeros
- Chris Rackauckas
- Kenneth Lange
- Rachel Mester
Organizations
- Massachusetts Institute of Technology