Changes in the parameters under study were found to result in the development of nitroxide and oxidant stresses and endotoxicosis. Thirty placentas (10 placentas from parturients after discoordinated labor, 10 from those after powerless labor, 10 placentas as a control group) were examined. Sitnikova, O G Peretiatko, L P Sharygin, S A Kuz'menko, G N Popova, I GĪ number of biochemical parameters (total nitrites and nitrates (NO(x)), cyclic guanosine monophosphate (cGMP), nitrotyrosine, medium-weight molecules (MCM) in the placenta were determined in women with gestosis during discoordinated and powerless labor.
The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology.
In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far.
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
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