@ARTICLE{10.3389/fgene.2012.00131,
  
 AUTHOR={Radulescu, Ovidiu  and  Gorban, Alexander N.  and  Zinovyev, Andrei  and  Noel, Vincent},   
     
TITLE={Reduction of dynamical biochemical reactions networks in
computational biology},      
    
JOURNAL={Frontiers in Genetics},      
    
VOLUME={3},      
    
YEAR={2012},      
    
NUMBER={131},     
      
URL={http://www.frontiersin.org/bioinformatics_and_computational_biology/10.3389/fgene.2012.00131/abstract},       
    
DOI={10.3389/fgene.2012.00131},      
    
ISSN={1664-8021},      
    
ABSTRACT={Biochemical networks are used in computational biology, to
model mechanistic details of systems involved in cell signaling, metabolism,
and regulation of gene expression. Parametric and structural uncertainty, as well as
combinatorial explosion are strong obstacles against analyzing the dynamics of large
models of this type. Multiscaleness, an important property of these networks, can be
used to get past some of these obstacles. Networks with many well separated time scales,
can be reduced to simpler models, in a way that depends only on the orders of magnitude
and not on the exact values of the kinetic parameters. The main idea used for such robust
simplifications of networks is the concept of dominance among model elements, allowing
hierarchical organization of these elements according to their effects on the network
dynamics.  This concept finds a natural formulation in tropical geometry. We revisit, in
the light of these new ideas, the main approaches to model reduction of reaction
networks, such as quasi-steady state and quasi-equilibrium approximations, and provide
practical recipes for model reduction of linear and nonlinear networks. We also discuss
the application of model reduction to the problem of parameter identification, via
backward pruning machine learning techniques.
}}