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dc.contributor.author
Dickenstein, Alicia Marcela  
dc.contributor.other
Cox, David  
dc.date.available
2022-05-06T20:09:34Z  
dc.date.issued
2020  
dc.identifier.citation
Dickenstein, Alicia Marcela; Algebraic methods for the study of biochemical reaction networks; American Mathematical Society; 134; 2020; 222-233  
dc.identifier.isbn
978-1-4704-5137-0  
dc.identifier.uri
http://hdl.handle.net/11336/156851  
dc.description.abstract
We will concentrate on biochemical reaction networks, of interest in systems biology, in particular enzymatic networks, consisting of different types of multisite phosphorylation networks. One source of inspiration for our study with algebro-geometric tools is the following quote from the abstract of the paper [44]:"Multisite phosphorylation cycles are ubiquitous in cell regulation systems andare studied at multiple levels of complexity, from molecules to organisms, withthe ultimate goal of establishing predictive understanding of the effects of geneticand pharmacological perturbations of protein phosphorylation in vivo. Achievingthis goal is essentially impossible without mathematical models, which providea systematic framework for exploring dynamic interactions of multiple networkcomponents."We will mainly concentrate on recent advances on the determination of multistationarity for these networks, whose dynamics are usually modeled with mass-action kinetics. For many classes of chemical networks, as the complex balanced networks, monostationarity is an important property. Instead, for biochemical reaction networks, that is, chemical reaction networks modeling pathways in systems biology, multistationarity is a general feature and it is important because it is intepreted as a way for the cell to take different decisions. Indeed, differential systems with mass-action kinetics are deterministic. But the occurrence of multiple stable steady states in the same stoichiometric compatibility class implies that trajectories starting from different initial conditions with the same conserved quantities can converge to steady states with different properties. We will end the chapter with some open questions.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
American Mathematical Society  
dc.rights
info:eu-repo/semantics/closedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
REACTION NETWORKS  
dc.subject
BIOCHEMISTRY  
dc.subject
ALGEBRA  
dc.subject
MULTISTATIONARITY  
dc.subject.classification
Matemática Aplicada  
dc.subject.classification
Matemáticas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Algebraic methods for the study of biochemical reaction networks  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.type
info:eu-repo/semantics/bookPart  
dc.type
info:ar-repo/semantics/parte de libro  
dc.date.updated
2021-07-30T18:58:50Z  
dc.journal.volume
134  
dc.journal.pagination
222-233  
dc.journal.pais
Estados Unidos  
dc.description.fil
Fil: Dickenstein, Alicia Marcela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://bookstore.ams.org/cbms-134#:~:text=Examples%20in%20the%20book%20include,constraint%20systems%2C%20and%20enzymatic%20cascades.  
dc.conicet.paginas
250  
dc.source.titulo
Applications of polynomial systems