Literature DB >> 17989686

Executable cell biology.

Jasmin Fisher1, Thomas A Henzinger.   

Abstract

Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.

Mesh:

Year:  2007        PMID: 17989686     DOI: 10.1038/nbt1356

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  136 in total

Review 1.  Systems immunology: a survey of modeling formalisms, applications and simulation tools.

Authors:  Vipin Narang; James Decraene; Shek-Yoon Wong; Bindu S Aiswarya; Andrew R Wasem; Shiang Rong Leong; Alexandre Gouaillard
Journal:  Immunol Res       Date:  2012-09       Impact factor: 2.829

2.  Simulation of lung alveolar epithelial wound healing in vitro.

Authors:  Sean H J Kim; Michael A Matthay; Keith Mostov; C Anthony Hunt
Journal:  J R Soc Interface       Date:  2010-03-17       Impact factor: 4.118

Review 3.  The executable pathway to biological networks.

Authors:  Jasmin Fisher; Nir Piterman
Journal:  Brief Funct Genomics       Date:  2010-01       Impact factor: 4.241

4.  Efficient modeling, simulation and coarse-graining of biological complexity with NFsim.

Authors:  Michael W Sneddon; James R Faeder; Thierry Emonet
Journal:  Nat Methods       Date:  2010-12-26       Impact factor: 28.547

5.  Rule-based modelling and simulation of biochemical systems with molecular finite automata.

Authors:  J Yang; X Meng; W S Hlavacek
Journal:  IET Syst Biol       Date:  2010-11       Impact factor: 1.615

6.  Hierarchical graphs for rule-based modeling of biochemical systems.

Authors:  Nathan W Lemons; Bin Hu; William S Hlavacek
Journal:  BMC Bioinformatics       Date:  2011-02-02       Impact factor: 3.169

Review 7.  Intelligently deciphering unintelligible designs: algorithmic algebraic model checking in systems biology.

Authors:  Bud Mishra
Journal:  J R Soc Interface       Date:  2009-04-08       Impact factor: 4.118

8.  Towards programming languages for genetic engineering of living cells.

Authors:  Michael Pedersen; Andrew Phillips
Journal:  J R Soc Interface       Date:  2009-04-15       Impact factor: 4.118

9.  Programming with models: modularity and abstraction provide powerful capabilities for systems biology.

Authors:  Aneil Mallavarapu; Matthew Thomson; Benjamin Ullian; Jeremy Gunawardena
Journal:  J R Soc Interface       Date:  2009-03-06       Impact factor: 4.118

10.  Visible Machine Learning for Biomedicine.

Authors:  Michael K Yu; Jianzhu Ma; Jasmin Fisher; Jason F Kreisberg; Benjamin J Raphael; Trey Ideker
Journal:  Cell       Date:  2018-06-14       Impact factor: 41.582

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