Affiliations: Biotechnology Counseling Services. Av. San Martín
4927 dpto A (1417), Buenos Aires. Argentina. Tel.: +54 11 4503 9355; E-mail:
[email protected]
Abstract: The dynamic range of metabolic models can be extended to deal with
large perturbations by introducing the related concepts of "generalized"
kinetic order and "canonical" sensitivities. Generalized kinetic orders are
built as a well-defined non linear combination of the canonical sensitivities
coefficients, which in turn are obtained by a least-squares regression on
central composite factorial design data. In a such way, the whole domain of the
operating variables is mapped without need to determine locally neither the
first nor the second order model derivatives. The method was validated through
numerical simulations, its predictions being compared with those coming from a
Michaelis-Menten formalism taken as reference. In parallel, two variants of the
Power-law formalism (S-system, least-squares GMA) also were tested. The
canonical sensitivities method produced the widest range to predict metabolite
concentrations and metabolic fluxes at the steady states. In addition, the
variation pattern for the logarithmic gains and for the characteristic
eigenvalues have been accurately determined from a unique overall model, being
both required to make realistic analysis in metabolic engineering. The achieved
information also can be expressed in terms of those typical coefficients
derived from the Metabolic Control Analysis (MCA). Even if current first order
Power-law or MCA formalisms were used, the canonical sensitivities approach
provides a significant advantage, since complete sets of homologous, accurate,
locally valid metabolic coefficients can be simultaneously recovered from the
array proposed, being representative of the whole range of the operating
variables instead of a unique nominal condition as is usual.
