Systems Biology: A Brief Overview

By Hiroaki Kitano. Science, 2002

4,404 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bb2d0d378f82472e4286b451f9b9fb584922321d

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In Molecular Biology

Citations in the corpus

Controlled vocabularies and semantics in systems biology (2011, background): “Activities in systems biology are often depicted as a modeling– hypothesis–experiment cycle (Kitano, 2002).”
A review of imaging techniques for systems biology (2008, background): “Systems biology Systems biology [1-8] attempts to model the dynamics and structure of complete biological systems.”
Computational modeling of the EGF-receptor system: a paradigm for systems biology. (2003, methodology): “The recent flood of genomic and proteomic data has spurred efforts to integrate this information into a higher-order, systems-level understanding of how organisms function [ 4 ].”
Evolutionary conservation of motif constituents in the yeast protein interaction network (2003, background): “gif" NDATA ITEM> ]> Understanding why some cellular components are conserved across species but others evolve rapidly is a key question of modern biolog”
Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database (2011, background): “Global datasets such as the ones we present are necessary for a systems-level understanding of biology; we must know what mechanisms exist that can control the state of a protein and therefore the cell to minimize malfunctions and provide therapeutics for the treatment of diseas”
Functional and evolutionary inference in gene networks: does topology matter? (2006, background): “…terms, and therefore marks a dramatic departure from the long successful and still dominant reductionist program that seeks to understand complex systems through detailed investigations of their individual parts (Ideker et al. 2001; Kitano 2002; Levesque and Benfey 2004; Barabási and Oltvai 2004).”
Systems medicine: the future of medical genomics and healthcare (2009, neutral): “interacting components (reviewed in [1-9]).”
A computational algebra approach to the reverse engineering of gene regulatory networks. (2003, background): “The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values.”
Systems biology informed deep learning for inferring parameters and hidden dynamics (2019, background): “To understand the biological systems, we must understand the structures of the systems (both their components and the structural relationships), and their dynamics [2].”
Metabolomics by numbers: acquiring and understanding global metabolite data. (2004, background): “‘Systems biology’ describes a range of techniques, including the ‘omics and mathematical modelling, for understanding systems ‘as a whole’ [63, 64 ], and it is widely recognized that metabolomics will have a major part to play in its development [65].”