Statistical inference for ordinary differential equations using gradient matching

A central objective of current systems biology research is explaining the interactions amongst components in biopathways. A standard approach is to view a biopathway as a network of biochemical reactions, which is modelled as a system of ordinary differential equations (ODEs). Conventional inference methods typically rely on searching the space of parameter values, and at each candidate, numerically solving the ODEs and comparing the output with that observed. After choosing an appropriate noise model, the form of the likelihood is defined, and a measure of similarity between the data signals and the signals described by the current set of ODE parameters can be calculated. This process is repeated, as part of either an iterative optimisation scheme or sampling procedure in order to estimate the parameters. However, the computational costs involved with repeatedly numerically solving the ODEs are usually high. Several authors have adopted approaches based on gradient matching, aiming to reduce this computational complexity. These approaches are based on the following two-step procedure. At the first step, interpolation is used to smooth the time series data, in order to avoid modelling noisy observations; in a second step, the kinetic parameters of the ODEs are either optimised or sampled, whilst minimising some metric measuring the difference between the slopes of the tangents to the interpolants, and the parameter-dependent time derivative from the ODEs. In this fashion, the ODEs never have to be numerically integrated, and the problem of inferring the typically unknown initial conditions of the system is removed, as it is not required for matching gradients. A downside to this two-step scheme is that the results of parameter inference are critically dependent on the quality of the initial interpolant. Alternatively, the ODEs can be allowed to regularise the interpolant and it has been demonstrated that it significantly improves the parameter inference accuracy and robustness with respect to noise. This thesis extends and develops methods of gradient matching for parameter inference and model selection in ODE systems in a systems biology context