title: Conservation analysis of large biochemical networks.
abstract: BACKGROUND
  Large biochemical networks pose a unique challenge from the point of view of evaluating conservation laws. The computational problem in most cases exceeds the capability of available software tools, often resulting in inaccurate computation of the number and form of conserved cycles. Such errors have profound effects on subsequent calculations, particularly in the evaluation of the Jacobian which is a critical quantity in many other calculations. The goal of this paper is to outline a new algorithm that is computationally efficient and robust at extracting the correct conservation laws for very large biochemical networks.
  
  
  RESULTS
  We show that our algorithm can perform the conservation analysis of large biochemical networks, and can evaluate the correct conserved cycles when compared with other similar software tools. Biochemical simulators such as Jarnac and COPASI are successful at extracting only a subset of the conservation laws that our algorithm can. This is illustrated with examples for some large networks which show the advantages of our method.
authors.affiliation: Keck Graduate Institute 535 Watson Drive, Claremont, CA 91711, USA. rrao@kgi.edu
authors.forename: Ravishankar Rao
authors.initials: RR
authors.lastname: Vallabhajosyula
authors.forename: Vijay
authors.initials: V
authors.lastname: Chickarmane
authors.forename: Herbert M
authors.initials: HM
authors.lastname: Sauro
mesh_terms: D000465:Algorithms; D001671:Biochemistry / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D020543:Proteome; D014157:Transcription Factors