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(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

He F, Fromion V, Westerhoff HV - BMC Syst Biol (2013)

Bottom Line: This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise.In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the 'perfect' regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness.To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK. hans.westerhoff@manchester.ac.uk.

ABSTRACT

Background: Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect.

Results: This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily.

Conclusions: A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the 'perfect' regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.

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Control system structure of a pseudo-integral or an ideal integral control problem. hTrsc(·) denotes the transcription process. gTrnl(·) denotes the rate of protein synthesis. The pseudo-integral control system becomes an ideal integral control only when the dashed line connecting the degradation rate kED is removed.
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Figure 11: Control system structure of a pseudo-integral or an ideal integral control problem. hTrsc(·) denotes the transcription process. gTrnl(·) denotes the rate of protein synthesis. The pseudo-integral control system becomes an ideal integral control only when the dashed line connecting the degradation rate kED is removed.

Mentions: In the exponential growth phase or if proteolysis is first order (i.e. kED ≠ 0), the above pathway example corresponds to a pseudo- or non-integral control scenario. The control structure of the regulatory system is then given by Figure 11. The dynamics of the ‘sensor’ is decomposed here by addressing both transcription and the translation through mRNA. At steady state, often gTrnl(R)ss = kED ⋅ Ess ≠ 0. Therefore, after perturbation of a system parameter (i.e. kinetic constants), the new steady state values of x3, R, and gTrnl(R) will no longer be the same as the old steady state values, which indicates that then the regulatory system does not achieve perfect adaptation. However, when kTnD is very small, near-perfect adaptation behaviour should be observed.


(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

He F, Fromion V, Westerhoff HV - BMC Syst Biol (2013)

Control system structure of a pseudo-integral or an ideal integral control problem. hTrsc(·) denotes the transcription process. gTrnl(·) denotes the rate of protein synthesis. The pseudo-integral control system becomes an ideal integral control only when the dashed line connecting the degradation rate kED is removed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4222491&req=5

Figure 11: Control system structure of a pseudo-integral or an ideal integral control problem. hTrsc(·) denotes the transcription process. gTrnl(·) denotes the rate of protein synthesis. The pseudo-integral control system becomes an ideal integral control only when the dashed line connecting the degradation rate kED is removed.
Mentions: In the exponential growth phase or if proteolysis is first order (i.e. kED ≠ 0), the above pathway example corresponds to a pseudo- or non-integral control scenario. The control structure of the regulatory system is then given by Figure 11. The dynamics of the ‘sensor’ is decomposed here by addressing both transcription and the translation through mRNA. At steady state, often gTrnl(R)ss = kED ⋅ Ess ≠ 0. Therefore, after perturbation of a system parameter (i.e. kinetic constants), the new steady state values of x3, R, and gTrnl(R) will no longer be the same as the old steady state values, which indicates that then the regulatory system does not achieve perfect adaptation. However, when kTnD is very small, near-perfect adaptation behaviour should be observed.

Bottom Line: This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise.In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the 'perfect' regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness.To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK. hans.westerhoff@manchester.ac.uk.

ABSTRACT

Background: Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect.

Results: This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily.

Conclusions: A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the 'perfect' regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.

Show MeSH