Accurate First-principle Model Research Articles

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling.

Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.

Estimation of Complete Discrete Multivariate Probability Distributions from Scarce Data with Application to Risk Assessment and Fault Detection

This paper presents a method of estimating discrete multivariate probability distributions from scarce historical data. Of particular interest is the estimation of the probabilities of rare events. The method is based on maximizing the information entropy subject to equality constraints on the moments of the estimated probability distributions. Two criteria are proposed for optimal selections of the moment functions. The method models nonlinear and nonmonotonic relations with an optimal level of model complexity. Not only does it allow for the estimation of the probabilities of rare events, but, together with Bayesian networks, it also provides a framework to model fault propagation in complex highly interactive systems. An application of this work is in risk assessment and fault detection using Bayesian networks, especially when an accurate first-principles model is not available. The performance of the method is shown through an example.

Data-Based LQI Controller Design from Plant Data

The performance of model-based controller design is strongly influenced by the quality of the underlying model. However, the development of an accurate first-principle model is not a trivial task. In this research, a data-based LQI controller design method directly from plant data is developed. By incorporating the just-intime learning technique, LQI design can be carried out without the need of a first-principle model, which is normally assumed to be available in the traditional LQI design. Simulation results are presented to illustrate the proposed method and a comparison with its conventional counterparts is made.