Modeling for (physical) biologists: an introduction to the rule-based approach

被引:34
作者
Chylek, Lily A. [1 ,2 ,3 ]
Harris, Leonard A. [4 ]
Faeder, James R. [5 ]
Hlavacek, William S. [2 ,3 ,6 ]
机构
[1] Cornell Univ, Dept Chem & Chem Biol, Ithaca, NY 14853 USA
[2] Los Alamos Natl Lab, Div Theoret, Theoret Biol & Biophys Grp, Los Alamos, NM 87545 USA
[3] Los Alamos Natl Lab, Ctr Nonlinear Studies, Los Alamos, NM 87545 USA
[4] Vanderbilt Univ, Sch Med, Dept Canc Biol, Nashville, TN 37212 USA
[5] Univ Pittsburgh, Sch Med, Dept Computat & Syst Biol, Pittsburgh, PA 15260 USA
[6] New Mexico Consortium, Los Alamos, NM 87544 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
rule-based modeling; systems biology; cell signaling; BIOMOLECULAR SITE DYNAMICS; GROWTH-FACTOR RECEPTOR; FC-EPSILON-RI; CELL-SURFACE; STOCHASTIC SIMULATION; SIGNALING PATHWAYS; REACTION NETWORKS; DETAILED BALANCE; VISUAL INTERFACE; EARLY EVENTS;
D O I
10.1088/1478-3975/12/4/045007
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.
引用
收藏
页数:24
相关论文
共 143 条
[51]  
Faeder JR., 2005, P 2005 ACM S APPL CO, P133, DOI [DOI 10.1145/1066677.1066712, 10.1145/1066677.1066712]
[52]   Stochastic generator of chemical structure. 3. Reaction network generation [J].
Faulon, JL ;
Sault, AG .
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2001, 41 (04) :894-908
[53]   Internal coarse-graining of molecular systems [J].
Feret, Jerome ;
Danos, Vincent ;
Krivine, Jean ;
Harmer, Russ ;
Fontana, Walter .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2009, 106 (16) :6453-6458
[54]   Simple, realistic models of complex biological processes: Positive feedback and bistability in a cell fate switch and a cell cycle oscillator [J].
Ferrell, James E., Jr. ;
Pomerening, Joseph R. ;
Kim, Sun Young ;
Trunnell, Nikki B. ;
Xiong, Wen ;
Huang, Chi-Ying Frederick ;
Machleder, Eric M. .
FEBS LETTERS, 2009, 583 (24) :3999-4005
[55]  
Fontana W., 1996, Boundaries and barriers, P56
[56]   Construction of a genetic toggle switch in Escherichia coli [J].
Gardner, TS ;
Cantor, CR ;
Collins, JJ .
NATURE, 2000, 403 (6767) :339-342
[57]   Perspective: Stochastic algorithms for chemical kinetics [J].
Gillespie, Daniel T. ;
Hellander, Andreas ;
Petzold, Linda R. .
JOURNAL OF CHEMICAL PHYSICS, 2013, 138 (17)
[58]   Approximate accelerated stochastic simulation of chemically reacting systems [J].
Gillespie, DT .
JOURNAL OF CHEMICAL PHYSICS, 2001, 115 (04) :1716-1733
[59]   The chemical Langevin equation [J].
Gillespie, DT .
JOURNAL OF CHEMICAL PHYSICS, 2000, 113 (01) :297-306
[60]   Mathematical and computational models of immune-receptor signalling [J].
Goldstein, B ;
Faeder, JR ;
Hlavacek, WS .
NATURE REVIEWS IMMUNOLOGY, 2004, 4 (06) :445-456