Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach.
BACKGROUND
This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics.


RESULTS
The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems.


CONCLUSIONS
Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.
von Stosch
Moritz
M
LEPAE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.
Peres
Joana
J
de Azevedo
Sebastião Feyo
SF
Oliveira
Rui
R
D021581:Active Transport, Cell Nucleus; D053263:Gene Regulatory Networks; D007700:Kinetics; D008660:Metabolism / Q000502:physiology*; D008954:Models, Biological; D010843:Pichia / Q000235:genetics; D013997:Time Factors; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
D014157:Transcription Factors