Systems and Synthetic biology of receptor tyrosine kinases for the development of novel therapeutics for ischaemic diseases, stroke and cancer"
Synthetic Biology is a revolutionary new area of biological research that uses engineering design principles to build ("synthesize") novel biological functions and systems. Receptor tyrosine kinases (RTK) have essential roles in controlling cellular proliferation, migration, differentiation and gene expression, and defects in RTK signalling underlie numerous diseases, including cancer, heart disease and stroke. Cells receive multiple RTK and other signalling inputs and must integrate these to maintain normal cell functions and adapt to changes in the microenvironment. However, the underlying mechanisms of signal integration are still poorly understood. This project will use techniques from the new fields of Systems and Synthetic Biology to develop quantitative models of specific RTK regulatory mechanisms. Synthetic Biology approaches will be used to re-engineer these regulatory mechanisms in order to test and refine the models.
Possible applications: pharmacology; toxicology, therapeutic manipulation of signalling pathways relevant to ischaemic disease, inflammation and angiogenic pathologies.
Roberta Cretella, Timothy Palmer and Mark Girolami. "Investigating cytokine receptor signalling and its inhibitory regulation in vascular endothelial cells: a model based approach"
In vascular endothelial cells, members of the interleukin-6 family of cytokine promote in- flammatory conditions through activation of the Janus kinase-signal transducer and activator of transcription (JAK/STAT) pathway. An interplay of different regulatory mechanisms in the cascade prevents the excessive receptor signalling, but in abnormal cells in which down- regulation fails, JAK/STAT signalling may promote progression from hypertrophy to heart failure.
Identifying inflammatory mechanisms in endothelial cells and inhibiting the pro- inflammatory signalling cascades involved could prevent or reverse excessive inflamma- tory conditions, but the situation is further complicated by the observation that cytokine- activated signalling cascades are also controlled by separate signalling modules including those initiated by the intracellular messenger cyclic AMP, producing a complex system of cross-regulation and activation.
A mathematical model describing such a system may allow one to predict the behaviour of endothelial cells to specific stimulation and to find potential treatments to act in a pointed way where feedback mechanisms fail to provide a signal regulation. Despite its functional significance, this crosstalk network has not been integrated into a coherent mathematical model of IL-6 receptor signalling and many interacting mechanisms remain still unclear also at the biological level.
A preliminary study of the system is presented. A set of ordinary differential equations are derived to describe the core of the signalling pathway. Bayesian approach is used for model inference from experimental data, model comparison, model based experiments and hypotheses
E.R. Morrissey, M.A. Juarez and N.J. Burroughs. "On reverse engineering gene interaction networks using time course data with repeated measurements"
Understanding gene regulatory networks is key in Systems Biology. Reverse engineering such networks is thus paramount and a plethora of literature dealing with the problem has developed in recent years. These approaches often assume one observed time series for each gene. However, gene expression data is normally obtained by destructive sampling and the idea of a longitudinal time series becomes problematic. This is because a single individual is not followed throughout the experiment, but rather a population of cells or individuals maintained in controlled, homogeneous conditions are sampled and their gene expression measured. Thus, rather than "real'' gene expression measurements, we are faced with a set of surrogate measures. In addition to the uncertainty involved in the sampling process, it is well known that gene expression measurement technologies, such as microarrays, render noisy data and frequently exhibit outliers. When repeated measurements are available, time series used for reverse engineering gene interaction networks are commonly obtained as a (weighted) average of these replicates and therefore these sources of uncertainty are ignored.
By acting as though the averaged series was the sole source of information, the additional information and uncertainty present in the repeated measurements is neglected and some biases can be passed on to the estimated network. Neglecting the variability within the replicates can have severe effects when fitting a linear model, with perhaps the most important being the so-called attenuation of the coefficient estimates. This attenuation can in turn render a underestimation of the interaction coefficients within the network, with a spurious sense of security given the concomitant underestimation of the variability of these estimates.
In the sequel we present a model that takes into account repeated measurements of time course gene expression data for estimating the topology of a gene interaction network. To ease the presentation, we assume an underlying first-order linear autoregressive process, AR(1), for the interaction network. As gene expression measurements frequently exhibit heavier-than-normal tail behaviour, we model the measurement process with Student-t errors. The model fitting is illustrated with data from the circadian clock in Arabidopsis thaliana.
E.R. Morrissey, M.A. Juarez, K. Denby and N.J. Burroughs. "Inferring the topology of a non-linear sparse gene regulatory network using fully Bayesian spline regression"
We propose a semi-parametric Bayesian model, based on penalised splines, for the recovery of an interaction network topology from longitudinal data. Our motivation is inference of gene regulatory networks from low resolution microarray time series (10-50 time points). Such biological networks are known to be sparse, imposed in the network by augmenting the model with parent indicators and providing these with either an overall or gene-wise hierarchical structure. We give conditions for posterior propriety under a broad class of frequently used improper priors. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus we provide a more informative, proper prior and analyse sensitivity. The posterior is analytically intractable and numerical methods are needed. A Metropolis-within-Gibbs sampler is proposed, with a novel Metropolis-Hastings step for sampling the topology and the spline coefficients simultaneously. We also construct a linear model for comparison purposes. The models are illustrated using syntheticand gene expression data drawn from ODE models and an experimental data set for the circadian rhythm.