Defining Network Topologies that Can Achieve Biochemical Adaptation

Center for Theoretical Biology, Peking University, Beijing 100871, China..
Cell (Impact Factor: 32.24). 09/2009; 138(4):760-73. DOI: 10.1016/j.cell.2009.06.013
Source: PubMed


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.


Available from: Chao Tang
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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. However, as some theoretical studies have suggested [14] [23], adaptation may not be solely related to integral control . "
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    • "So C is independent from the concentration of ligand L: technically C is a biochemically adaptive variable at τ = τ c [10] [11]. "
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    • "For example, boolean network models were used to study the gene evolution [44], attractor number variation with asynchronous stochastic updating [45], gene expression in the state space [39], and organism system growth rate improvement [46]. Another approach is to abstract key regulation genetic networks [47] [48] (or motifs) from all associated interactions, and to employ synthetic biology to modify, control and finally understand the biological mechanisms within these complicated systems [38] [42]. An earlier application of this approach led to a good understanding of the ubiquitous phenomenon of bistability in biological systems [49], where there are typically limit cycle attractors and, during cell cycle control, noise can trigger a differentiation process by driving the system from a limit circle to another steady state attractor [38]. "
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