Your browser doesn't support javascript.
loading
The Axes of Life: A Roadmap for Understanding Dynamic Multiscale Systems.
Chandrasekaran, Sriram; Danos, Nicole; George, Uduak Z; Han, Jin-Ping; Quon, Gerald; Müller, Rolf; Tsang, Yinphan; Wolgemuth, Charles.
Afiliação
  • Chandrasekaran S; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Danos N; Department of Biology, University of San Diego, San Diego, CA, USA.
  • George UZ; Department of Mathematics & Statistics, San Diego State University, San Diego, CA, USA.
  • Han JP; IBM TJ Watson Research Center, Ossining, NY, USA.
  • Quon G; Department of Molecular and Cellular Biology, University of California-Davis, Davis, CA,USA.
  • Müller R; Department of Mechanical Engineering, Virginia Tech, Blacksburg, VI, USA.
  • Tsang Y; Department of Natural Resources and Environmental Management, University of Hawai'i at Manoa, Honolulu, HI, USA.
  • Wolgemuth C; Departments of Physics and Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.
Integr Comp Biol ; 61(6): 2011-2019, 2022 02 05.
Article em En | MEDLINE | ID: mdl-34048574
ABSTRACT
The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado de Máquina Tipo de estudo: Guideline / Prognostic_studies Limite: Animals Idioma: En Revista: Integr Comp Biol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado de Máquina Tipo de estudo: Guideline / Prognostic_studies Limite: Animals Idioma: En Revista: Integr Comp Biol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos