Inference of genetic networks using S-system: information criteria for model selection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Identification of chemical reaction mechanism from batch process data
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Parameter estimation in modulated, unbranched reaction chains within biochemical systems
Computational Biology and Chemistry
Inference of genetic networks using linear programming machines: application of a priori knowledge
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A prior knowledge based approach to infer gene regulatory networks
ISB '10 Proceedings of the International Symposium on Biocomputing
A parameter estimation approach for non-linear systems biology models using spline approximation
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Feasible prediction in S-system models of genetic networks
Expert Systems with Applications: An International Journal
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Smooth functional tempering for nonlinear differential equation models
Statistics and Computing
Reconstructing metabolic networks using interval analysis
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
Inference of Biological S-System Using the Separable Estimation Method and the Genetic Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Optimizing ethanol production selectivity
Mathematical and Computer Modelling: An International Journal
Multiscale Denoising of Biological Data: A Comparative Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Reverse engineering of gene regulatory networks from biological data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Fuzzy Intervention in Biological Phenomena
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Wavelet-based Multiscale Filtering of Genomic Data
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Inferring large scale genetic networks with S-system model
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Parameter Estimation of Biological Phenomena: An Unscented Kalman Filter Approach
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
State and parameter estimation for nonlinear biological phenomena modeled by S-systems
Digital Signal Processing
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Rationale: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. Results: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a 'universal function' that is computed directly from the data by cross-validated training of an artificial neural network (ANN). Conclusions: Without preprocessing, the inverse problem of determining structure from metabolic or proteomic profile data is challenging and computationally expensive. The combination of system decoupling and data fitting with universal functions simplifies this inverse problem very significantly. Examples show successful estimations and current limitations of the method. Availability: A preliminary Web-based application for ANN smoothing is accessible at http://bioinformatics.musc.edu/webmetabol/. S-systems can be interactively analyzed with the user-friendly freeware PLAS© (http://correio.cc.fc.ul.pt/~aenf/plas.html) or with the MATLAB module BSTLab (http://bioinformatics.musc.edu/bstlab/), which is currently being beta-tested.