Difference between revisions of "Mass Conservation Analysis"
(Class name added) |
(Automatic synchronization with BioUML) |
||
| (One intermediate revision by one user not shown) | |||
| Line 1: | Line 1: | ||
;Analysis title | ;Analysis title | ||
| − | :[[File: | + | :[[File:Default-analysis-icon.png]] Mass Conservation Analysis |
;Provider | ;Provider | ||
:[[Institute of Systems Biology]] | :[[Institute of Systems Biology]] | ||
| Line 26: | Line 26: | ||
[[Category:Analyses]] | [[Category:Analyses]] | ||
| − | [[Category: | + | [[Category:Differential algebraic equations (analyses group)]] |
[[Category:ISB analyses]] | [[Category:ISB analyses]] | ||
[[Category:Autogenerated pages]] | [[Category:Autogenerated pages]] | ||
Latest revision as of 18:14, 9 December 2020
- Analysis title
Mass Conservation Analysis
- Provider
- Institute of Systems Biology
- Class
MassConservationAnalysis- Plugin
- biouml.plugins.modelreduction (Model reduction plug-in)
[edit] Mass conservation analysis
The mass conservation analysis involves the decomposition of the stoichiometric matrix N into the product of two matrixes:
- N = L × NR,
where NR is the reduced stoichiometry consisting of the linearly independent rows of the matrix N and L is the link matrix.
To find such decomposition, we use the Gauss-Jordan method detecting the left null space Γ of the matrix N1, so that
- Γ × N = 0.
The matrix Γ specifies the conservation laws of the system, ie. linear combinations of species concentrations which remain constant over time. Generation of the matrix Γ is based on the premultiplication of N by a series of elementary matrixes. In particular, rows of this matrix and hence the species order are permutated. Thus, eliminating from the matrix N all rows corresponding to the species which result in the null space, we find the matrix NR. In addition, the matrix L consists of the identity matrix rows and rows defined by Γ arranged according to the species order in the matrix N.
[edit] References
- HM Sauro, B Ingalls, "Conservation analysis in biochemical networks: computational issues for software writers". Biophysical Chemistry, 109(1): 1-15, 2004.
