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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Robustness, canalyzing functions and systems design.

Johannes Rauh1, Nihat Ay

  • 1Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103, Leipzig, Germany, jrauh@mis.mpg.de.

Theory in Biosciences = Theorie in Den Biowissenschaften
|September 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces knockout robustness for stochastic systems, ensuring consistent behavior when inputs are removed. Robust systems are characterized by structural constraints on Gibbs potentials and conditional independence, enabling predictable system design.

Related Experiment Videos

Last Updated: May 7, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Area of Science:

  • Probability theory
  • Statistical modeling
  • Systems engineering

Background:

  • Stochastic maps (Markov kernels) model systems with multiple inputs and one output.
  • Knockout robustness ensures system behavior remains unchanged when inputs are removed.

Purpose of the Study:

  • To define and analyze knockout robustness for stochastic maps.
  • To provide a mechanistic description of robust systems using Gibbs potentials.
  • To explore implications for systems design and probability distribution characterization.

Main Methods:

  • Utilizing Gibbs potentials for mechanistic descriptions of system behavior post-knockout.
  • Imposing structural constraints on Gibbs potentials to define robustness.
  • Characterizing robustness via conditional independence constraints on joint distributions.

Main Results:

  • Demonstrated that robust systems can be described by specific interaction families of Gibbs potentials.
  • Established a link between robustness and conditional independence properties.
  • Showed that the set of robust probability distributions decomposes into a finite union of components with found parametrizations.

Conclusions:

  • Robust systems exhibit predictable behavior under input removal, facilitated by structured Gibbs potentials.
  • The findings provide a framework for designing robust stochastic systems.
  • Characterization of robust systems aids in understanding and parametrizing their underlying probability distributions.