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.
Read Online or Download Advances in Bioinformatics and Computational Biology: Brazilian Symposium on Bioinformatics, BSB 2005, Sao Leopoldo, Brazil, July 27-29, 2005, Proceedings PDF
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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
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 ). 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  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).