Modeling gene expression from microarray expression data with state-space equations.

We describe a new method to model gene expression from time-course gene expression data. The modelling is in terms of state-space descriptions of linear systems. A cell can be considered to be a system where the behaviours (responses) of the cell depend completely on the current internal state plus any external inputs. The gene expression levels in the cell provide information about the behaviours of the cell. In previously proposed methods, genes were viewed as internal state variables of a cellular system and their expression levels were the values of the intemal state variables. This viewpoint has suffered from the underestimation of the model parameters. Instead, we view genes as the observation variables, whose expression values depend on the current intemal state variables and any external input. Factor analysis is used to identify the internal state variables, and Bayesian Information Criterion (BIC) is used to determine the number of the internal state variables. By building dynamic equations of the internal state variables and the relationships between the internal state variables and the observation variables (gene expression profiles), we get state-space descriptions of gene expression model. In the present method, model parameters may be unambiguously identified from time-course gene expression data. We apply the method to two time-course gene expression datasets to illustrate it.

MolCom: a method to compare protein molecules based on 3-D structural and chemical similarity.

This paper describes an improved method for conducting global feature comparisons of protein molecules in three dimensions and for producing a new form of multiple structure alignment. Our automated MolCom method incorporates an octtree strategy to partition and examine molecular properties in three-dimensional space at multiple levels of analysis. The MolCom method's multiple alignment is in the form of an octtree which locates regions in three-dimensional space where correspondence between molecules is identified based on a dynamic set of molecular features. MolCom offers a practical solution to the inherent compromise between computational complexity and analytical detail. MolCom is currently the only method that can analyze and compare a series of defined physicochemical properties using multiple, simultaneous levels of resolution. It is also the only method that provides a consensus structure outlining precisely where the similarity exists in three-dimensional space. Using a modest-sized collection of structural properties, separate experiments were conducted to calibrate MolCom and to verify that the spatial analyses and resulting structure alignments accurately identified both similar and dissimilar structures. The accuracy of MolCom was found to be over 99% and the similarity scores correlated strongly with the z-scores of the Alignment by Incremental Combinatorial Extension of the Optimal Path method.