Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours

FRM is funded by the Engineering and Physical Sciences Research Council (EPSRC).Spatial interactions between cancer and immune cells, as well as the recognition of tumour antigens by cells of the immune system, play a key role in the immune response against solid tumours. The existing mathematical models generally focus only on one of these key aspects. We present here a spatial stochastic individual-based model that explicitly captures antigen expression and recognition. In our model, each cancer cell is characterised by an antigen profile which can change over time due to either epimutations or mutations. The immune response against the cancer cells is initiated by the dendritic cells that recognise the tumour antigens and present them to the cytotoxic T cells. Consequently, T cells become activated against the tumour cells expressing such antigens. Moreover, the differences in movement between inactive and active immune cells are explicitly taken into account by the model. Computational simulations of our model clarify the conditions for the emergence of tumour clearance, dormancy or escape, and allow us to assess the impact of antigenic heterogeneity of cancer cells on the efficacy of immune action. Ultimately, our results highlight the complex interplay between spatial interactions and adaptive mechanisms that underpins the immune response against solid tumours, and suggest how this may be exploited to further develop cancer immunotherapies.PostprintPeer reviewe

The restorative role of annexin A1 at the blood–brain barrier

Annexin A1 is a potent anti-inflammatory molecule that has been extensively studied in the peripheral immune system, but has not as yet been exploited as a therapeutic target/agent. In the last decade, we have undertaken the study of this molecule in the central nervous system (CNS), focusing particularly on the primary interface between the peripheral body and CNS: the blood–brain barrier. In this review, we provide an overview of the role of this molecule in the brain, with a particular emphasis on its functions in the endothelium of the blood–brain barrier, and the protective actions the molecule may exert in neuroinflammatory, neurovascular and metabolic disease. We focus on the possible new therapeutic avenues opened up by an increased understanding of the role of annexin A1 in the CNS vasculature, and its potential for repairing blood–brain barrier damage in disease and aging