Inference for epidemic models with time-varying infection rates: Tracking the dynamics of oak processionary moth in the UK.

Invasive pests pose a great threat to forest, woodland, and urban tree ecosystems. The oak processionary moth (OPM) is a destructive pest of oak trees, first reported in the UK in 2006. Despite great efforts to contain the outbreak within the original infested area of South-East England, OPM continues to spread.

Guided proposals for efficient weighted stochastic simulation.

Rare event probabilities play an important role in the understanding of the behavior of biochemical systems. Due to the intractability of the most natural Markov jump process representation of a system of interest, rare event probabilities are typically estimated using importance sampling. While the resulting algorithm is reasonably well developed, the problem of choosing a suitable importance density is far from straightforward.

Paediatric Rehabilitation Ingredients Measure: a new tool for identifying paediatric neurorehabilitation content.

Aim: To develop an instrument (Paediatric Rehabilitation Ingredients Measure [PRISM]) for quantitative estimation of contents of interdisciplinary neurorehabilitation for use in studies of relationships between rehabilitation treatment delivered and severity-adjusted outcomes after acquired brain injury (ABI).

Method: The measure was developed using an ingredients-mediators-outcomes model consistent with the International Classification of Functioning, Disability and Health, a literature review, and other current initiatives in the development of rehabilitation treatment taxonomies, with item codevelopment in workshops with rehabilitation professionals. Interrater reliability was assessed in inpatient and residential paediatric rehabilitation settings.

Diagnostics for assessing the linear noise and moment closure approximations.

Solving the chemical master equation exactly is typically not possible, so instead we must rely on simulation based methods. Unfortunately, drawing exact realisations, results in simulating every reaction that occurs. This will preclude the use of exact simulators for models of any realistic size and so approximate algorithms become important.

Bayesian inference for Markov jump processes with informative observations.

In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC. Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process.

Simulation of stochastic kinetic models.

A growing realization of the importance of stochasticity in cell and molecular processes has stimulated the need for statistical models that incorporate intrinsic (and extrinsic) variability. In this chapter we consider stochastic kinetic models of reaction networks leading to a Markov jump process representation of a system of interest. Traditionally, the stochastic model is characterized by a chemical master equation.

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo.

Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process.

Bayesian inference for a wave-front model of the neolithization of Europe.

We consider a wave-front model for the spread of neolithic culture across Europe, and use Bayesian inference techniques to provide estimates for the parameters within this model, as constrained by radiocarbon data from southern and western Europe. Our wave-front model allows for both an isotropic background spread (incorporating the effects of local geography) and a localized anisotropic spread associated with major waterways. We introduce an innovative numerical scheme to track the wave front, and use Gaussian process emulators to further increase the efficiency of our model, thereby making Markov chain Monte Carlo methods practical.

Gross energy metabolism in mice under late onset, short term caloric restriction.

Late onset, short-term moderate caloric restriction (CR) may have beneficial health effects. A 26% CR regime induced at 14 months of age for 70 days in male C57Bl/6 (ICRFa) mice resulted in a reduction in body mass of 17%. A decrease in daily energy expenditure was associated with decreased body mass in CR mice.

Bayesian sequential inference for stochastic kinetic biochemical network models.

As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we explore the Bayesian estimation of stochastic kinetic rate constants governing dynamic models of intracellular processes. The underlying model is replaced by a diffusion approximation where a noise term represents intrinsic stochastic behavior and the model is identified using discrete-time (and often incomplete) data that is subject to measurement error.