Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model

Going Around Again: Modelling Standing Ovations with a Flexible Agent-based Simulation Framework

We describe how we have used the CoSMoS process to trans- form a computer simulation originally developed for the simulation of plant development for use in modelling aspects of audience behaviour. An existing agent-based simulator is re-factored to simulate a completely dierent type of agent in 2D space. This is possible and desirable because the original simulator was designed with the intention that it could eas- ily be use to model a variety of dierent agents interacting in 2D and 3D space. The resulting simulation will be used to simulate the phe- nomena of standing ovations in audiences as a model system of tipping point behaviour. Continued development of this simulator, assisted by the CoSMoS process, has resulted in a general purpose lightweight sim- ulation framework

The architecture of a digital forensic readiness management system

A coordinated approach to digital forensic readiness (DFR) in a large organisation requires the management and monitoring of a wide variety of resources, both human and technical. The resources involved in DFR in large organisations typically include staff from multiple departments and business units, as well as network infrastructure and computing platforms. The state of DFR within large organisations may therefore be adversely affected if the myriad human and technical resources involved are not managed in an optimal manner. This paper contributes to DFR by proposing the novel concept of a digital forensic readiness management system (DFRMS). The purpose of a DFRMS is to assist large organisations in achieving an optimal level of management for DFR. In addition to this, we offer an architecture for a DFRMS. This architecture is based on requirements for DFR that we ascertained from an exhaustive review of the DFR literature. We describe the architecture in detail and show that it meets the requirements set out in the DFR literature. The merits and disadvantages of the architecture are also discussed. Finally, we describe and explain an early prototype of a DFRMS.http://www.elsevier.com/locate/cosehb201

Lrig1-expression confers suppressive function to CD4+ cells and is essential for averting autoimmunity via the Smad2/3/Foxp3 axis

AbstractRegulatory T cells (Treg) are CD4+ T cells with immune-suppressive function, which is defined by Foxp3 expression. However, the molecular determinants defining the suppressive population of T cells have yet to be discovered. Here we report that the cell surface protein Lrig1 is enriched in suppressive T cells and controls their suppressive behaviors. Within CD4+ T cells, Treg cells express the highest levels of Lrig1, and the expression level is further increasing with activation. The Lrig1+ subpopulation from T helper (Th) 17 cells showed higher suppressive activity than the Lrig1- subpopulation. Lrig1-deficiency impairs the suppressive function of Treg cells, while Lrig1-deficient naïve T cells normally differentiate into other T cell subsets. Adoptive transfer of CD4+Lrig1+ T cells alleviates autoimmune symptoms in colitis and lupus nephritis mouse models. A monoclonal anti-Lrig1 antibody significantly improves the symptoms of experimental autoimmune encephalomyelitis. In conclusion, Lrig1 is an important regulator of suppressive T cell function and an exploitable target for treating autoimmune conditions.</jats:p

Investigating The Dynamics of Hepatic Inflammation Through Simulation

Inflammation is a fundamental mechanism for the body to induce repair and healing in tissues, and exacerbated inflammatory responses are associated with a wide variety of diseases and disorders. Categorising the various cells, proteins, and precise mechanisms involved in initiating and driving inflammation poses significant challenges, due to the complex interplay that occurs between them. In this thesis, I will introduce a deadly parasitic disease called Visceral Leishmaniasis (VL) as a case study in using computational modelling techniques to elucidate the mechanisms underpinning inflammation. During VL infection, inflammatory aggregations of immune system cells form, these are called granulomas. Granulomas function to contain and subsequently remove infection. Whilst immunological studies have provided insights into the structure and function of granulomas, there remains a breadth of questions which laboratory techniques are currently incapable of answering. As such, the challenges facing biologists from a scientific perspective will be addressed, I will then argue after a thorough review of the relevant literature, that agent-based computational modelling is a logical choice for research into granuloma formation, and that such models can help answer some outstanding questions in the field. The thesis presents the process of designing and developing the first spatially resolved model of liver localised granuloma formation during VL. The development and use of modelling and simulation to study granulomas has involved close collaboration with immunologists at all stages through conceptualisation, modelling, implementation, and also results interpretation. I describe the use of established statistical techniques to instill confidence in both the model, and the results it can produce through simulation. Through iterative hypothesis generation and testing, the research undertaken has allowed for several predictions to be made, some of which have biological significance and which were later validated experimentally. Specifically, transcriptomic data analysis revealed that both infected and uninfected Kupffer cells are equally capable of responding to infection in a similar manner, something which wasn't previously evident in the literature. Using this transcriptomic data, I investigated through simulation, several experimental scenarios and elucidated a novel mechanism of immune system regulation in the liver microenvironment. Using an experimental model of Leishmania donovani infection, I demonstrated that such an immune regulatory mechanism can be overcome with the expansion of early promoter cells called Natural Killer T cells

An Agent-Based Model of the IL-1 Stimulated Nuclear Factor-kappa B Signalling Pathway

The transcription factor NF-κB is a biological component that is central to the regulation of genes involved in the innate immune system. Dysregulation of the pathway is known to be involved in a large number of inflammatory diseases. Although considerable research has been performed since its discovery in 1986, we are still not in a position to control the signalling pathway, and thus limit the effects of NF-κB within promotion of inflammatory diseases. We believe that computational modelling and simulation of the NF-κB signalling pathway will complement wet-lab experimental approaches, and will facilitate a more comprehensive understanding of this example of a complex biological system. In this study, we have developed an agent-based model of the IL-1 stimulated NF-κB signalling pathway, which has been calibrated to wet- lab data at the single-cell level. Through rigorous software engineering, which followed a principled approach to design and development by adherence to the CoSMoS process, we believe our model provides an abstracted view of the underlying real-world system, and can be used in a predictive capacity through in silico experimentation. A novel approach to domain modelling has been presented, which uses linear and multivariate statistical techniques to complement the Unified Modelling Language. Furthermore, in silico experimentation with the newly developed agent-based model, has confirmed the robust yet fragile nature of the signalling pathway. We have discovered that the pathway is robust to perturbations of cell membrane receptor component number, intermediate component number, and the temporal lag between cell membrane receptor activation and subsequent activation of IKK. Conversely however, in silico experimentation predicts that the pathway is sensitive to changes in the ratio of free IκBα to NF-κB, and fragile to basal dissociation of NF-κB-IκBα outside of a narrow range of probabilities

Annual report town of Milan, Milan, New Hampshire for the year ending December 31, 2013.

This is an annual report containing vital statistics for a town/city in the state of New Hampshire

Bacterial host attribution and bioinformatic characterisation of enteric bacteria Salmonella enterica and Escherichia coli from different hosts and environments

With the advent of relatively low cost whole genome sequencing (WGS), it is now possible to obtain sequences from large numbers of bacterial strains and interrogate their core and accessory genomes in relation to associated metadata. While there are some bacterial species with preferred hosts, especially in terms of disease, there has been no real systematic genomic investigation of host and niche specificity of ’generalist’ bacteria, i.e., those that can be isolated from multiple hosts and environments. The main aim of this research was to determine if host and/or niche-specific proteins can be identified for ’multi-host adapted’ bacteria such as E. coli and Salmonella Typhimurium (STm) in order to predict the ’origin’ of a strain and its zoonotic potential from its sequence. Two datasets of ’multi-host’ bacteria were analysed: 1,203 STm isolates from 4 hosts (avian, bovine, human and swine) and E. coli from 6 hosts (avian, bovine, canine, environmental, human and swine). Based on classical core genome analysis such as core phylogeny, multilocus sequence typing and phylo-grouping, no strong correlations with host were identified. The accessory genome was also investigated for host-based associations, and accessory host associated proteins (HAP) were identified for each of the bacteria/ host groups. These proteins were used to build a machine learning (ML) classifier - support vector machine (SVM) - to predict the isolation host of the bacterial isolates. The majority of the isolates from both species were predicted correctly with prediction accuracy ranging from 67% to 90%. For both bacterial species the most challenging were bovine and swine host groups as these two had many features in common. The approach allowed not only prediction of host based on WGS but also an assessment of how much the genome of particular isolates resembled the features of the genomes of the same species isolated from other hosts. This allowed ’generalist’ and ’specialist’ strains from each host group to be estimated as well as the sequences that indicate successful transmission potential between hosts. This work also showed that diverse collections of E. coli or STm can be used as a baseline for prediction and quantification of zoonotic potential as was demonstrated with E. coli O157 and Salmonella serovar Typhi. Overall this part of the research indicated marked host restriction for both STm and E. coli, with only limited isolate subsets exhibiting host promiscuity based on predicted protein content. ML can be successfully applied to interrogate source attribution of bacterial isolates and has the capacity to predict zoonotic potential. Using the same ML approach, another question was asked about how similar are the known zoonotic pathogens. When studied apart, E. coli O157 can be classified further into human and bovine isolates with only a small proportion of bovine isolates predicted as ’human’, pointing to the specific cattle strains that are potentially a more serious threat to human health. This approach was tested with 2 independent sets of O157 human outbreak strains with traced-back isolates from animals and food. The outbreak strains independent of the origin were scored as ’human’. This finding has profound implications for public health management of disease because interventions in cattle, such a vaccination, could be targeted at herds carrying strains of high zoonotic potential. The final section the thesis research was based on the STm dataset and compared different ML approaches to test which algorithm performed best for host prediction. Dimensionality reduction techniques as well as unsupervised and supervised ML were applied to HAP. Dimensionality reduction techniques and unsupervised ML were not able to split the dataset by host and produced different results which could be challenging to interpret correctly in terms of biological significance of the factors that influenced clustering. On the other hand, all three supervised classifiers resulted in very comparable high levels of prediction (over 95%). Thus, the choice of supervised classifier for host prediction should be based on the knowledge of the end-user as well as on requirements for any further analysis. To conclude, accessory genomes were successfully used for extraction of host associated proteins as well as for prediction of source host and quantification of zoonotic potential for bacteria species that can be isolated from multiple hosts. The methods described here can be applied to other bacteria and overall have implications for monitoring, identification and targeted interventions associated with potentially zoonotic infections. The results are completely dependent on the dataset quality which should be as large and diverse as possible. The research highlights the predictive potential of such algorithms but also the need for bacterial sequences to be gathered with as much useful metadata as possible, including isolation host