Dynamical systems describe the changes in processes that arise naturally from their underlying physical principles, such as the laws of motion or the conservation of mass, energy or momentum. These models facilitate a causal explanation for the drivers and impediments of the processes. But do they describe the behaviour of the observed data? And how can we quantify the models' parameters that cannot be measured directly? Data to linear Dynamics (Data2LD) addresses these two questions by providing a methodology for estimating the solution; and the parameters of linear dynamical systems from incomplete and noisy observations of the processes.
These functions implement the Pipeline4DGEData, which consists of a series of statistical modelling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies.
Ordinary differential equations (ODEs) describe the dynamics of a continuously changing process by relating the process to its rates of change. Generalized smoothing aims to obtain an estimated functional entity that adheres to the data and incorporates domain-specific information defined by the ODE. This approach facilitates the expression of the parameters of the ODE explicitly in terms of the parameters defining the functional entity. Thus providing an efficient and easy-to-use estimation procedure that obtains estimates that have comparable accuracy to those produced by parameter cascading with proling approach.