Stochastic yield analysis of self-assembling, single-enzyme reaction networks

Yuliy Baryshnikov; Ed Coffman; Teddy Yimwadsana

Stochastic yield analysis of self-assembling, single-enzyme reaction networks

Yuliy Baryshnikov, Ed Coffman, Teddy Yimwadsana

Research output: Contribution to conference › Paper › peer-review

Abstract

We investigate yield patterns in a class of reaction networks appearing naturally in many biochemical systems, and playing a crucial role in various designs of controllable self-assembling structures, nano-machines, and biocomputing. We approximate yields by the solutions of a linear system that arises in a formulation based on Markov chain theory. This approach avoids the intractability normally found in the systems of nonlinear ODE's of reaction networks. An example is worked out to illustrate the method.

Original language	English
Pages	43-47
Number of pages	5
Publication status	Published - Dec 1 2005
Externally published	Yes
Event	2nd Conference on Foundations of Nanoscience: Self-Assembled Architectures and Devices, FNANO 2005 - Snowbird, UT, United States Duration: Apr 24 2005 → Apr 28 2005

Conference

Conference	2nd Conference on Foundations of Nanoscience: Self-Assembled Architectures and Devices, FNANO 2005
Country	United States
City	Snowbird, UT
Period	4/24/05 → 4/28/05

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Electrical and Electronic Engineering

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abstract = "We investigate yield patterns in a class of reaction networks appearing naturally in many biochemical systems, and playing a crucial role in various designs of controllable self-assembling structures, nano-machines, and biocomputing. We approximate yields by the solutions of a linear system that arises in a formulation based on Markov chain theory. This approach avoids the intractability normally found in the systems of nonlinear ODE's of reaction networks. An example is worked out to illustrate the method.",

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AU - Yimwadsana, Teddy

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AB - We investigate yield patterns in a class of reaction networks appearing naturally in many biochemical systems, and playing a crucial role in various designs of controllable self-assembling structures, nano-machines, and biocomputing. We approximate yields by the solutions of a linear system that arises in a formulation based on Markov chain theory. This approach avoids the intractability normally found in the systems of nonlinear ODE's of reaction networks. An example is worked out to illustrate the method.

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