Your browser doesn't support javascript.
loading
Quantitative computational models of molecular self-assembly in systems biology.
Thomas, Marcus; Schwartz, Russell.
Afiliação
  • Thomas M; Computational Biology Department, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, United States of America. Joint Carnegie Mellon University/University of Pittsburgh Ph.D. Program in Computational Biology, 4400 Fifth Avenue, Pittsburgh, PA 15213, United States of America.
Phys Biol ; 14(3): 035003, 2017 05 23.
Article em En | MEDLINE | ID: mdl-28535149
ABSTRACT
Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Montagem de Vírus / Biologia de Sistemas / Modelos Biológicos Idioma: En Revista: Phys Biol Assunto da revista: BIOLOGIA Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Montagem de Vírus / Biologia de Sistemas / Modelos Biológicos Idioma: En Revista: Phys Biol Assunto da revista: BIOLOGIA Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos