Stochastic sensitivity analysis and kernel inference via distributional data.

Published

Journal Article

Cellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing.

Full Text

Duke Authors

Cited Authors

  • Li, B; You, L

Published Date

  • September 2014

Published In

Volume / Issue

  • 107 / 5

Start / End Page

  • 1247 - 1255

PubMed ID

  • 25185560

Pubmed Central ID

  • 25185560

Electronic International Standard Serial Number (EISSN)

  • 1542-0086

International Standard Serial Number (ISSN)

  • 0006-3495

Digital Object Identifier (DOI)

  • 10.1016/j.bpj.2014.07.025

Language

  • eng