TY - JOUR

T1 - Using Metabolic Flux Data to Further Constrain the Metabolic Solution Space and Predict Internal Flux Patterns

T2 - The Escherichia coli Spectrum

AU - Wiback, Sharon J.

AU - Mahadevan, Radhakrishnan

AU - Palsson, Bernhard

PY - 2004/5/5

Y1 - 2004/5/5

N2 - Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the α-spectrum. The α-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The α-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the α-spectrum extreme pathway matrix that incorporates measured uptake and by-product secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques.

AB - Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the α-spectrum. The α-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The α-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the α-spectrum extreme pathway matrix that incorporates measured uptake and by-product secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques.

KW - Constraint-based modeling

KW - Extreme pathways

KW - Metabolic flux analysis

UR - http://www.scopus.com/inward/record.url?scp=2342427580&partnerID=8YFLogxK

U2 - 10.1002/bit.20011

DO - 10.1002/bit.20011

M3 - Article

C2 - 15083512

AN - SCOPUS:2342427580

SN - 0006-3592

VL - 86

SP - 317

EP - 331

JO - Biotechnology and Bioengineering

JF - Biotechnology and Bioengineering

IS - 3

ER -