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Related Experiment Video

Updated: Jul 17, 2026

Reliably Engineering and Controlling Stable Optogenetic Gene Circuits in Mammalian Cells

Reliably Engineering and Controlling Stable Optogenetic Gene Circuits in Mammalian Cells

Published on: July 6, 2021

Probabilistic control of gene networks.

Peter Y Chen¹, Jeremy Chen

¹Bachelor of Tech. Programme, Nat. Univ. of Singapore.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 7, 2007

Summary

This study introduces a Markov chain method for controlling gene networks by modeling states as probability distributions. The developed algorithm efficiently guides gene networks to desired states using probabilistic control actions.

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Area of Science:

Computational Biology
Systems Biology
Control Theory

Background:

Gene networks are complex biological systems with dynamic states.
Controlling these networks is crucial for understanding and manipulating cellular functions.
Existing methods may lack efficiency or probabilistic guarantees.

Purpose of the Study:

To propose a novel approach for controlling gene networks using Markov chains.
To develop an algorithm for guiding gene networks to desired states probabilistically.
To enhance the efficiency of the control algorithm with a heuristic.

Main Methods:

Representing gene network states as probability distributions.
Utilizing Markov chains for probabilistic state transitions.
Designing a control algorithm to find action sequences for state transitions.
Implementing a heuristic to improve algorithm efficiency.

Main Results:

Demonstrated a probabilistic framework for gene network control.
Developed an algorithm to achieve desired network states with specified probabilities.
Showcased the effectiveness of a heuristic in improving computational efficiency for certain network types.

Conclusions:

The proposed Markov chain approach offers a robust method for gene network control.
The algorithm provides a probabilistic guarantee for reaching target states.
The heuristic significantly enhances the practical applicability of the control strategy.