Gene Regulatory Network Inference and Validation Using Relative Change Ratio Analysis and Time-Delayed Dynamic Bayesian Network

The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the in silico network inference challenge of DREAM3 in 2008. Here we report the details of our approach and its performance on the synthetic challenge datasets. In our methodology, we first developed a model called relative change ratio (RCR), which took advantage of the heterozygous knockdown data and null-mutant knockout data provided by the challenge, in order to identify the potential regulators for the genes. With this information, a time-delayed dynamic Bayesian network (TDBN) approach was then used to infer gene regulatory networks from time series trajectory datasets. Our approach considerably reduced the searching space of TDBN; hence, it gained a much higher efficiency and accuracy. The networks predicted using our approach were evaluated comparatively along with 29 other submissions by two metrics (area under the ROC curve and area under the precision-recall curve). The overall performance of our approach ranked the second among all participating teams

ENDOCANNABINOID-BASED NANOPARTICLES TARGETED TO THE SYNOVIUM FOR THE TREATMENT OF ARTHRITIS

Chronic inflammatory joint disease represents an emerging public health issue, occupying a sizeable proportion of the adult population in the industrialized world. Recently, there has been a resurgence of interest in marijuana and its natural and synthetic derivatives, cannabinoid receptor agonists and antagonists, as well as chemically related compounds, for their therapeutic potential as both an anti-inflammatory and analgesic. Whilst the benefits of endocannabinoid-based treatments appear promising, very few studies have investigated the use of the self-assembled nanoparticles (NPs) for targeted drug delivery. In this study, the nanostructure mesophase behaviour of a series of mixed monoethanolamide lipids of oleoylethanolamide (OEA) and linoylethanolamide (LEA) into higher order NP structures for the encapsulation and delivery of drugs was investigated. In addition to drug encapsulation, active targeting through the conjugation of a synovium-targeting peptide, HAP-1, to the surface of these NP’s was used to facilitate selective accumulation of therapeutic agents the inflamed joint. The inhibitory cytokine effects of these targeted NPs was demonstrated in vitro, and in vivo using an adjuvant induced arthritis model of inflammation. The ability to deliver endocannabinoid based NPs to specific sites of the body mediating pharmacological endocannabinoid-like effects to influence key physiological pathways, provides a novel drug delivery system and medicinal potential to treat many diseases in many fields of medicine in which inflammation is a key feature of the disease

The dynamic chain event graph.

The Chain Event Graph (CEG) is a type of tree-based graphical model that accommodates all discrete Bayesian Networks as a particular subclass. It has already been successfully used to capture context-specific conditional independence structures of highly asymmetric processes in a way easily appreciated by domain experts. Being built from a tree, a CEG has a huge number of free parameters that makes the class extremely expressive but also very large. Exploring the enormous CEG model space then makes it necessary to design bespoke algorithms for this purpose. All Bayesian algorithms for CEG model selection in the literature are based on the Dirichlet characterisation of a family of CEGs spanned by a single event tree. Here I generalise this framework for a CEG model space spanned by a collection of different event trees. A new concept called hyper-stage is also introduced and provides us with a framework to design more efficient algorithms. These improvements are nevertheless insufficient to scale up the model search for more challenging applications. In other contexts, recent analyses of Bayes Factor model selection using conjugate priors have suggested that the use of such prior settings tends to choose models that are not sufficiently parsimonious. To sidestep this phenomenon, non-local priors (NLPs) have been successfully developed. These priors enable the fast identification of the simpler model when it really does drive the data generation process. In this thesis, I define three new families of NLPs designed to be applied specifically to discrete processes defined through trees. In doing this, I develop a framework for a CEG model search which appears to be both robust and computationally efficient. Finally, I define a Dynamic Chain Event Graph (DCEG). I develop object-recursive methods to fully analyse a particularly useful and feasibly implementable new subclass of these models called the N Time-Slice DCEG (NT-DCEG). By exploiting its close links with the Dynamic Bayesian Network I show how the NT-DCEG can be used to depict various structural and Granger causal hypotheses about a studied process. I also show how to construct from the topology of this graph intrinsic random variables which exhibit context-specific independences that can then be checked by domain experts. Throughout the thesis my methods are illustrated using examples of multivariate processes describing inmate radicalisation in a prison, and survey data concerning childhood hospitalisation and booking a tourist train