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Feng, Song; Ollivier, Julien F.; Soyer, Orkun S. (2016)
Publisher: Public Library of Science
Journal: PLoS Computational Biology
Languages: English
Types: Article
Subjects: Computational Biology, Signaling Cascades, Research Article, Mathematics, Signal Processing, Enzymes, Engineering and Technology, Biophysical Simulations, Physical Sciences, Evolutionary Biology, Proteins, Physics, Biophysics, Systems Science, Biology and Life Sciences, Computer and Information Sciences, Signal Transduction, Biochemical Simulations, QH426, Signaling Networks, Molecular Evolution, Enzymology, QH301-705.5, Cell Biology, Cell Signaling, Biochemistry, Protein Kinase Signaling Cascade, Network Analysis, Dynamic Response, Biology (General), Phosphatases
Author Summary Biological systems utilise signalling networks that are composed of multiple interacting proteins to process environmental information. The function of these networks is critical for cells to respond and adapt to their environment by converting environmental signals to appropriate cellular response dynamics. As results of evolution, these signalling networks display certain evolutionary design principles (i.e. common structural and dynamical features) that allow them to implement specific functions. Here, we use an in silico evolution approach to simulate the emergence of signalling networks that are capable of two specific types of response dynamics: switch-like and/or adaptive response dynamics. These two response dynamics underpin cellular decision-making and homeostasis. By analysing the evolved networks, we discover that enzyme sequestration is a key feature involved in achieving both types of response dynamics. Based on this finding, we design a minimalistic signalling motif featuring enzyme sequestration through a scaffold protein. We demonstrate that this motif can achieve both response dynamics and furthermore the type of response can be controlled through the concentration level of the scaffold protein. These results highlight enzyme sequestration as a potential evolutionary design principle to achieve key response dynamics in natural signalling networks and as an engineering route in synthetic biology.

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