English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Maximizing the information content of experiments in systems biology.

Liepe, J., Filippi, S., Komorowski, M., & Stumpf, M. P. H. (2013). Maximizing the information content of experiments in systems biology. PLoS Computational Biology, 9(1): e1002888. doi:10.1371/journal.pcbi.1002888.

Item is

Files

show Files
hide Files
:
2491524.pdf (Publisher version), 2MB
Name:
2491524.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-
:
2491524_Suppl.htm (Supplementary material), 292KB
Name:
2491524_Suppl.htm
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/xhtml+xml / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Liepe, J.1, Author           
Filippi, S., Author
Komorowski, M., Author
Stumpf, M. P. H., Author
Affiliations:
1Research Group of Quantitative and System Biology, MPI for Biophysical Chemistry, Max Planck Society, ou_2466694              

Content

show
hide
Free keywords: -
 Abstract: Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.

Details

show
hide
Language(s): eng - English
 Dates: 2013-01-31
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1002888
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: PLoS Computational Biology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: 13 Volume / Issue: 9 (1) Sequence Number: e1002888 Start / End Page: - Identifier: -