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Principles of Computation by Competitive Protein Dimerization Networks.
Parres-Gold, Jacob; Levine, Matthew; Emert, Benjamin; Stuart, Andrew; Elowitz, Michael B.
Afiliación
  • Parres-Gold J; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
  • Levine M; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
  • Emert B; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Stuart A; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
  • Elowitz MB; Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125, USA.
bioRxiv ; 2023 Nov 02.
Article en En | MEDLINE | ID: mdl-37961250
Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.
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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos