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

He, Fei; Fromion, Vincent; Westerhoff, Hans V.

doi:10.1186/1752-0509-7-131

Cited by 28 publications

(43 citation statements)

References 36 publications

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“…It achieves this by partitioning a pathway into supply and demand modules. More recently, supplydemand theory has been generalized from including exclusively metabolic regulation to both metabolic and gene-expression regulation, which is named the hierarchical supply -demand theory [77]. For example, an unbranched metabolic pathway under both allosteric inhibition and transcriptional regulation of the first enzyme by the end product (figure 1a) can be simplified into a hierarchical supply -demand system (figure 1b).…”

Section: Modular Control Analysis: Hierarchical Supply -Demand Theorymentioning

confidence: 99%

“…The intersection of these two monotonic functions determines the steady-state properties of the pathway, i.e. the steady-state concentration of metabolite, the steady-state flux, the elasticities with respect to the supply and demand modules as shown in figure 1c, and thereby the distribution of control between supply and demand [75][76][77].…”

Section: Modular Control Analysis: Hierarchical Supply -Demand Theorymentioning

confidence: 99%

“…Mathematically, such a so-called MCA-based robustness coefficient equals the inverse of the corresponding control coefficient of that step. He et al [77] studied the robustness of a metabolic network subjected to both metabolic and gene-expression regulation, and generalized the MCA-based analysis to HCA-based robustness analysis. The MCA (or HCA)-based robustness and fragility coefficient can be defined as follows: Here f denotes the system function of interest, such as a particular flux J j or a metabolite concentration x j .…”

Section: Robustness and Fragilitymentioning

confidence: 99%

“…This may ensure the robustness versus perturbations at various frequencies as widely considered in engineering system design. It has been shown [35,77,123] that metabolic regulation through allosteric or more direct substrate-product effects is related to a 'proportional or nonlinear control' action, because the catalytic activity of an enzyme can depend on a metabolite concentration in a proportional or nonlinear kinetic relationship. The metabolic regulation often acts as a fast actuator/controller that rapidly buffers against highfrequency perturbations but possibly with small amplitude or capability, such as adaptation to small fluctuations in the flux demand [86].…”

Section: Metabolic and Gene-expression Regulationmentioning

confidence: 99%

“…Recent analyses indicate that gene-expression regulation improves the robustness of a metabolic pathway significantly but that in practice such robustness may not be infinite, because perfect adaptation requires biochemically unrealistic features such as zero-order kinetics of protein degradation [77,136]. Such control engineering interpretations can also be linked with classical MCA and HCA.…”

Section: Metabolic and Gene-expression Regulationmentioning

confidence: 99%

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Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

Murabito

Westerhoff

2016

J. R. Soc. Interface.

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Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.

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Section: Modular Control Analysis: Hierarchical Supply -Demand Theorymentioning

confidence: 99%

Section: Modular Control Analysis: Hierarchical Supply -Demand Theorymentioning

confidence: 99%

Section: Robustness and Fragilitymentioning

confidence: 99%

Section: Metabolic and Gene-expression Regulationmentioning

confidence: 99%

Section: Metabolic and Gene-expression Regulationmentioning

confidence: 99%

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Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

Murabito

Westerhoff

2016

J. R. Soc. Interface.

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Crosstalk between transcription and metabolism: how much enzyme is enough for a cell?

Donati

Sander

Link

2017

WIREs Mechanisms of Disease

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Cells employ various mechanisms for dynamic control of enzyme expression. An important mechanism is mutual feedback-or crosstalk-between transcription and metabolism. As recently suggested, enzyme levels are often much higher than absolutely needed to maintain metabolic flux. However, given the potential burden of high enzyme levels it seems likely that cells control enzyme expression to meet other cellular objectives. In this review, we discuss whether crosstalk between metabolism and transcription could inform cells about how much enzyme is optimal for various fitness aspects. Two major problems should be addressed in order to understand optimization of enzyme levels by crosstalk. First, mapping of metabolite-protein interactions will be crucial to obtain a better mechanistic understanding of crosstalk. Second, investigating cellular objectives that define optimal enzyme levels can reveal the functional relevance of crosstalk. We present recent studies that approach these problems, drawing from experimental transcript and metabolite data, and from theoretical network analyses. © 2017 Wiley Periodicals, Inc. How to cite this article:WIREs Syst Biol Med 2018, 10:e1396. doi: 10.1002/wsbm.1396 INTRODUCTIONT he function and structure of metabolic and transcriptional networks are well characterized. Transcription is the first step in the control of gene expression. Metabolism governs the supply of energy and cellular building blocks. Besides regulatory interactions within each of the two networks, mutual feedback is abundant between them. Already in the 1950s the discovery of the lac operon showed that transcription impacts metabolic operation (metabolic gene expression).1 A few years later, the discovery of allosteric metabolite-protein interactions provided a mechanism for metabolitedriven transcription (metabolic feedback on transcription).2 In our view, the crosstalk between metabolism and transcription results from two interdependent processes: information from transcriptional networks to metabolism is transmitted by metabolic gene expression, while metabolic information is conveyed via metabolic feedback on transcription (Figure 1). In the past decade, systems biology has mostly been focused on genomes, transcriptomes, and proteomes owing to the availability of advanced and sensitive technologies. Recent improvements in metabolomics methods 3 have now enabled metabolites to become the focus of many studies. [4][5][6][7] The fundamental challenge for understanding how metabolites regulate transcriptional programs lies in identifying metabolites that are key signals for transcriptional regulators. This is illustrated by the fact that the master regulatory metabolite of catabolic genes in Escherichia coli-α-ketoglutarate-was identified only recently, 8 although the regulatory mechanism has been known since the early 1950s (carbon catabolite repression). Recent findings suggest that such metabolic feedback on transcription could of 11govern global gene regulation and metabolism. For example, a recent study in yea...

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Modeling energy intake by adding homeostatic feedback and drug intervention

Gennemark

Stephan

Gabrielsson

2014

J Pharmacokinet Pharmacodyn

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Energy intake (EI) is a pivotal biomarker used in quantification approaches to metabolic disease processes such as obesity, diabetes, and growth disorders. Eating behavior is however under both short-term and long-term control. This control system manifests itself as tolerance and rebound phenomena in EI, when challenged by drug treatment or diet restriction. The paper describes a model with the capability to capture physiological counter-regulatory feedback actions triggered by energy imbalances. This feedback is general as it handles tolerance to both increases and decreases in EI, and works in both acute and chronic settings. A drug mechanism function inhibits (or stimulates) EI. The deviation of EI relative to a reference level (set-point) serves as input to a non-linear appetite control signal which in turn impacts EI in parallel to the drug intervention. Three examples demonstrate the potential usefulness of the model in both acute and chronic dosing situations. The model shifts the predicted concentration-response relationship rightwardly at lower concentrations, in contrast to models that do not handle functional adaptation. A fourth example further shows that the model may qualitatively explain differences in rate and extent of adaptation in observed EI and its concomitants in both rodents and humans.

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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

Cited by 28 publications

References 36 publications

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

Crosstalk between transcription and metabolism: how much enzyme is enough for a cell?

Modeling energy intake by adding homeostatic feedback and drug intervention

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