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Prediction of dynamic behavior of mutant strains from limited wild-type data

Paper ID Volume ID Publish Year Pages File Format Full-Text
31576 44821 2012 12 PDF Available
Title
Prediction of dynamic behavior of mutant strains from limited wild-type data
Abstract

Metabolic engineering is the field of introducing genetic changes in organisms so as to modify their function towards synthesizing new products of high impact to society. However, engineered cells frequently have impaired growth rates thus seriously limiting the rate at which such products are made. The problem is attributable to inadequate understanding of how a metabolic network functions in a dynamic sense. Predictions of mutant strain behavior in the past have been based on steady state theories such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), and regulatory on/off minimization (ROOM). Such predictions are restricted to product yields and cannot address productivity, which is of focal interest to applications. We demonstrate that our framework ( Song and Ramkrishna, 2010 and Song and Ramkrishna, 2011), based on a “cybernetic” view of metabolic systems, makes predictions of the dynamic behavior of mutant strains of Escherichia coli from a limited amount of data obtained from the wild-type. Dynamic frameworks must necessarily address the issue of metabolic regulation, which the cybernetic approach does by postulating that metabolism is an optimal dynamic response of the organism to the environment in driving reactions towards ensuring survival. The predictions made in this paper are without parallel in the literature and lay the foundation for rational metabolic engineering.

► Predictions are made of dynamic behavior of KO mutant strains from wild-type data for the first time. ► These predictions are made by Lumped Hybrid Cybernetic Models using very limited but critical data. ► Predictions include growth rate, substrate uptake rate and productivity of metabolic products of KO strains.

Keywords
Dynamic prediction; Gene knockout; Lumped hybrid cybernetic model (L-HCM); Limited data; Productivity; Escherichia coli
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Prediction of dynamic behavior of mutant strains from limited wild-type data
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Publisher
Database: Elsevier - ScienceDirect
Journal: Metabolic Engineering - Volume 14, Issue 2, March 2012, Pages 69–80
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
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Full-text PDF Download
Online Support
Any Questions? feel free to contact us