title: Towards predictive stochastic dynamical modeling of cancer genesis and progression.
abstract: Based on an innovative endogenous network hypothesis on cancer genesis and progression we have been working towards a quantitative cancer theory along the systems biology perspective. Here we give a brief report on our progress and illustrate that combing ideas from evolutionary and molecular biology, mathematics, engineering, and physics, such quantitative approach is feasible.
authors.affiliation: Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine of Ministry of Education, Shanghai Jiao Tong University, Shanghai, China. aoping@u.washington.edu
authors.forename: P
authors.initials: P
authors.lastname: Ao
authors.forename: D
authors.initials: D
authors.lastname: Galas
authors.forename: L
authors.initials: L
authors.lastname: Hood
authors.forename: L
authors.initials: L
authors.lastname: Yin
authors.forename: X M
authors.initials: XM
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D018450:Disease Progression; D019143:Evolution, Molecular; D006801:Humans; D007257:Information Theory; D008297:Male; D008962:Models, Theoretical; D009369:Neoplasms / Q000378:metabolism* / Q000473:pathology*; D017711:Nonlinear Dynamics; D011471:Prostatic Neoplasms / Q000473:pathology; D013269:Stochastic Processes; D049490:Systems Biology