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Defining Network Topologies that Can Achieve Biochemical Adaptation

Center for Theoretical Biology, Peking University, Beijing 100871, China..
Cell (Impact Factor: 33.12). 09/2009; 138(4):760-73. DOI: 10.1016/j.cell.2009.06.013
Source: PubMed

ABSTRACT Many signaling systems show adaptation-the ability to reset themselves after responding to a stimulus. We computationally searched all possible three-node enzyme network topologies to identify those that could perform adaptation. Only two major core topologies emerge as robust solutions: a negative feedback loop with a buffering node and an incoherent feedforward loop with a proportioner node. Minimal circuits containing these topologies are, within proper regions of parameter space, sufficient to achieve adaptation. More complex circuits that robustly perform adaptation all contain at least one of these topologies at their core. This analysis yields a design table highlighting a finite set of adaptive circuits. Despite the diversity of possible biochemical networks, it may be common to find that only a finite set of core topologies can execute a particular function. These design rules provide a framework for functionally classifying complex natural networks and a manual for engineering networks. For a video summary of this article, see the PaperFlick file with the Supplemental Data available online.

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Available from: Chao Tang, Aug 15, 2015
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    • "Following an osmotic shock, nuclear enrichment of the MAP kinase Hog1 adapts perfectly to changes in external osmolarity, a result of an integral feedback action that requires Hog1 kinase activity. Adaptation, however, may not be necessarily related to integral control as some theoretical studies have suggested [12] [20]. "
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    DESCRIPTION: Homeostasis is a running theme in biology. Often achieved through feedback regulation strategies, homeostasis allows living cells to control their internal environment as a means for surviving changing and unfavourable environments. While many endogenous homeostatic motifs have been studied in living cells, synthetic homeostatic circuits have received far less attention. The tight regulation of the abundance of cellular products and intermediates in the noisy environment of the cell is now recognised as a critical requirement for several biotechnology and therapeutic applications. Here we lay the foundation for a regulation theory at the molecular level that explicitly takes into account the noisy nature of biochemical reactions and provides novel tools for the analysis and design of robust synthetic homeostatic circuits. Using these ideas, we propose a new regulation motif that implements an integral feedback strategy which can generically and effectively regulate a wide class of reaction networks. By combining tools from probability and control theory, we show that the proposed control motif preserves the stability of the overall network, steers the population of any regulated species to a desired set point, and achieves robust perfect adaptation -- all without any prior knowledge of reaction rates. Moreover, our proposed control motif can be implemented using a very small number of molecules and hence has a negligible metabolic load. Strikingly, the regulatory motif exploits stochastic noise, leading to enhanced regulation in scenarios where noise-free implementations result in dysregulation. Several examples demonstrate the potential of the approach.
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    • "Following an osmotic shock, nuclear enrichment of the MAP kinase Hog1 adapts perfectly to changes in external osmolarity, a result of an integral feedback action that requires Hog1 kinase activity. Adaptation, however, may not be necessarily related to integral control as some theoretical studies have suggested [5], [6]. "
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    ABSTRACT: Homeostasis is a running theme in biology. Often achieved through feedback regulation strategies, homeostasis allows living cells to control their internal environment as a means for surviving changing and unfavourable environments. While many endogenous homeostatic motifs have been studied in living cells, synthetic homeostatic circuits have received far less attention. The tight regulation of the abundance of cellular products and intermediates in the noisy environment of the cell is now recognised as a critical requirement for several biotechnology and therapeutic applications. Here we lay the foundation for a regulation theory at the molecular level that explicitly takes into account the noisy nature of biochemical reactions and provides novel tools for the analysis and design of robust synthetic homeostatic circuits. Using these ideas, we propose a new regulation motif that implements an integral feedback strategy which can generically and effectively regulate a wide class of reaction networks. By combining tools from probability and control theory, we show that the proposed control motif preserves the stability of the overall network, steers the population of any regulated species to a desired set point, and achieves robust perfect adaptation -- all without any prior knowledge of reaction rates. Moreover, our proposed control motif can be implemented using a very small number of molecules and hence has a negligible metabolic load. Strikingly, the regulatory motif exploits stochastic noise, leading to enhanced regulation in scenarios where noise-free implementations result in dysregulation. Several examples demonstrate the potential of the approach.
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    • "rtional and integral feedback controls or among feedforward controls with different signaling strengths in the feedforward arm . Many cellular signaling pathways utilize combinations of motifs . For example , in stress response pathways , rapid , robust adaptation often arises by coupling negative feedback and incoherent feedforward loops ( El - Samad et al . 2005 ; Zhang et al . 2009 ) . In these situations low - dose extrapolations need to consider the concerted action of the interconnected network motifs ."
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    ABSTRACT: Background: Increasingly, there is a move toward using in vitro toxicity testing to assess human health risk due to chemical exposure. As with in vivo toxicity testing, an important question for in vitro results is whether there are thresholds for adverse cellular responses. Empirical evaluations may show consistency with thresholds, but the main evidence has to come from mechanistic considerations. Objectives: Cellular response behaviors depend on the molecular pathway and circuitry in the cell and the manner in which chemicals perturb these circuits. Understanding circuit structures that are inherently capable of resisting small perturbations and producing threshold responses is an important step towards mechanistically interpreting in vitro testing data. Methods: Here we have examined dose–response characteristics for several biochemical network motifs. These network motifs are basic building blocks of molecular circuits underpinning a variety of cellular functions, including adaptation, homeostasis, proliferation, differentiation, and apoptosis. For each motif, we present biological examples and models to illustrate how thresholds arise from specific network structures. Discussion and Conclusion: Integral feedback, feedforward, and transcritical bifurcation motifs can generate thresholds. Other motifs (e.g., proportional feedback and ultrasensitivity)produce responses where the slope in the low-dose region is small and stays close to the baseline. Feedforward control may lead to nonmonotonic or hormetic responses. We conclude that network motifs provide a basis for understanding thresholds for cellular responses. Computational pathway modeling of these motifs and their combinations occurring in molecular signaling networks will be a key element in new risk assessment approaches based on in vitro cellular assays. Citation: Zhang Q, Bhattacharya S, Conolly RB, Clewell HJ III, Kaminski NE, Andersen ME. 2014. Molecular signaling network motifs provide a mechanistic basis for cellular threshold responses. Environ Health Perspect 122:1261–1270; http://dx.doi.org/10.1289/ehp.1408244
    Environmental Health Perspectives 08/2014; 122(12). DOI:10.1289/ehp.1408244 · 7.03 Impact Factor
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