Advances in Bioinformatics and Computational Biology: by Joao Carlos Setubal, Sergio Verjovski-Almeida

By Joao Carlos Setubal, Sergio Verjovski-Almeida

This e-book constitutes the refereed court cases of the Brazilian Symposium on Bioinformatics, BSB 2005, held in Sao Leopoldo, Brazil in July 2005.

The 15 revised complete papers and 10 revised prolonged abstracts awarded including three invited papers have been conscientiously reviewed and chosen from fifty five submissions. The papers tackle a vast variety of present subject matters in computational biology and bioinformatics.

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Extra info for Advances in Bioinformatics and Computational Biology: Brazilian Symposium on Bioinformatics, BSB 2005, Sao Leopoldo, Brazil, July 27-29, 2005, Proceedings

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Usually written as a linear combination of the fluxes, the objective function could, for example, be growth rate, ATP production, or glucose intake. (2): Z = dT f , (1) Sf = 0, (2) l≤f ≤u (3) and, Perturbing Thermodynamically Unfeasible Metabolic Networks 33 where, f ∈ Rn is the vector of n fluxes, S ∈ Rm×n is a stoichiometric matrix and m is the number of reactants or metabolites in the network. All vectors by default are column vectors. Also, d, l and u are vectors ∈ Rn of objective function coefficients, lower and upper bound constraints on the fluxes respectively, and 0 is a zero vector of size m.

In the FBA optimization the internal fluxes are unrestricted and only satisfy the flux balance constraint. The C02 and acetate fluxes come from the literature (references found in [4]). Since only the relative rates matter, the glucose flux is set to 1, and all other fluxes are normalized with respect to it. The G matrix is formed by considering the columns of the following internal fluxes from Table 1 of Delgado and Liao (1997): x = [Jpgi , J3 , Jpep , Jpyk , Jpdh , Jace , J8 , Jict , J11 , J12 , Jppc , J14 , J15 , J16 , Jtkt , Jtal , Jresp , Jatp , Jbiomass , Jglyox ] and the H matrix is formed from the columns of the external fluxes: y = [Jgluc , qCO2 , qace ].

From S we remove the columns corresponding to boundary fluxes and keep only the columns of non-redundant internal fluxes, which we define as those between metabolites. The resulting matrix G ∈ Rm×ni , where ni is the number of internal fluxes in the network. Using the reduced row echelon form [11] we can find the null space matrix N of G. The matrix N ∈ Rni ×nl consists of nl basis vectors of N (G), the null space of G. The dimension of N (G), denoted by D(N (G)) gives the number of independent loops nl in the network (Strang, 2003).

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