Differential Evolutionary Algorithms for In Vivo Dynamic Analysis of Glycolysis and Pentose Phosphate Pathway in Escherichia coli - Parallel Computing for Bioinformatics and Computational Biology - page 66

d C 2pg
d t

= r PGluMu − r ENO − r PGM − µC 2pg (3.13)

d C pep
d t

r Synth1 − = r ENO − r PK − r PTS − r PEPCxylase − r DAHPS − µC pep (3.14)

d C pyr
d t

r Synth2 +

= r PK + r PTS − r PDH − r MetSynth + r TrpSynth + µC pyp (3.15)

d C 6pg
d t

= r G6PDH − r PGDH − µC 6pg (3.16)

d C ribu5p
d t

= r PGDH − r Ru5P − r R5PI − µC ribu5p (3.17)

d C xyl5p
d t

= r Ru5P − r 5PI − µC xyl5p (3.18)

d C sed7p
d t

= r TKa − r TA − µC sed7p (3.19)

d C rib5p
d t

= r R5PI − r TKa − r RPPK − µC rib5p (3.20)

d C e4p
d t

= r TA − r TKb − r DAHPS − µC e4p (3.21)

d C g1p
d t

= r PGM − r G1PAT − µC g1p (3.22)

3.2.5 Estimation of Non-Measured Steady-State Concentrations

Statistical optimization process utilizes linear estimation techniques (least-square
estimation) to produce models that describe the research space. Today, owing to
the development of high computing, we are able to implement new algorithms which
use nonlinear optimization techniques. Computational optimization methods such
as genetic algorithm, neural networks, and particle swarm optimization have shown
some promise in developing optimization strategies.