For the full reference to the paper, see the lab publications page
Posted on 19 December 2016
The publication, by lab members James Butler, Jason Cosgrove, Kieran Alden, Jon Timmis, and Mark Coles, is now available from the Frontiers in Immunology web site. In the perspective, we make the case for applying model-driven experimentation using two case studies which combined simulations with experiments to identify mechanisms driving lymphoid tissue formation and function, and then discuss potential applications of this experimental paradigm to identify novel therapeutic targets for TLT pathology.
The molecular and cellular processes driving the formation of secondary lymphoid tissues have been extensively studied using a combination of mouse knockouts, lineage specific reporter mice, gene expression analysis, immunohistochemistry and flow cytometry. However, the mechanisms driving the formation and function of tertiary lymphoid tissue (TLT) experimental techniques have proven to be more enigmatic and controversial due to differences between experimental models and human disease pathology. Systems-based approaches including data-driven biological network analysis (Gene Interaction Network, Metabolic Pathway Network, Cell-Cell signalling & cascade networks) and mechanistic modelling afford a novel perspective from which to understand TLT formation and identify mechanisms that may lead to the resolution of tissue pathology. In this perspective, we make the case for applying model-driven experimentation using two case studies which combined simulations with experiments to identify mechanisms driving lymphoid tissue formation and function, and then discuss potential applications of this experimental paradigm to identify novel therapeutic targets for TLT pathology.
Posted on 13 November 2016
This tutorial, available from the CPT website, follows our previous work on the application of argumentation in the development of models of biological systems, with the focus being specific to the engineering approaches that underlie the development of these models. The paper uses our ongoing work on Leishmania as its focus, stepping through the process through which argumentation can be used to increase cconfidence in a generated computer model. The paper is the result of a collaboration between members of YCIL (Jon Timmis, Kieran Alden, Mark Coles, Paul Kaye) and SimOmics Ltd (Paul Andrews, Ed Clark, Becky Naylor, Adam Nellis), a spin-out company formed and directed by Jon Timmis and Mark Coles.
This tutorial promotes good practice for exploring the rationale of systems pharmacology models. A safety systems engineering inspired notation approach provides much needed rigour and transparency in development and application of models for therapeutic discovery and design of intervention strategies. Structured arguments over a model's development, underpinning biological knowledge, and analyses of model behaviours, are constructed to determine the confidence that a model is fit for the purpose for which it will be applied.
Posted on 27 January 2016
Recent work led by Kieran Alden has been accepted for publishing in the IEEE Journal Transactions on Computational Biology and Bioinformatics.
This paper focuses on using simulation as a tool for performing time lapse experiments, and uses our Secondary Lymphoid Organ development study as an exemplar of this approach. By performing analyses at multiple simulation timepoints, we have been able to suggest that SLO development may be biphasic, with different pathways being most influential in each. To encourage wider adoption of this approach, the paper describes how we have extended functionality in our Spartan package to permit this type of analysis on other models.
The full paper, entitled "Extending and Applying Spartan to Perform Temporal Sensitivity Analyses for Predicting Changes in Influential Biological Pathways in Computational Models" will appear in pre-print version this week. The final version will be made available here once this is made available.
Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behaviour
exhibited by a computational model at various simulated timepoints, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve-hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur.
Posted on 17 November 2015
Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent-based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. Our tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM-specific strengths have yielded success in the area of preclinical mechanistic modeling.
The paper is open access and available from the CPT Pharmacometrics & Systems Pharmacology website
Posted on 10 November 2015
This meeting, taking place on the 16th and 17th November 2015, aims to discuss state of the art in vivo imaging, advanced meta-approaches of genomics/transcriptomics, and novel concepts in computer modelling of infection to drug development, and the understanding of pathogenesis of leishmaniasis and other intracellular pathogens. Paul Kaye has been invited to introduce our work on a multisale model to minimise animal usage in leishmaniasis drug development. Kieran Alden will then present an overview of computational modelling in immunology, and deliver a tutorial on YCIL's application of computational approaches in studying Leishmaniasis. Mark Coles has been invited to present his work on combining computational modelling with experimentation to understand immune system function.
The tutorial will demonstrate the YCIL web-based tool that provides a stochastic simulation of the immune response to Leishmania: the formation of a granuloma. The simulation is based on a stochastic petri-net (details of which will be covered in Kieran's talk earlier in the day). The simulation has a number of parameters, which will be explored and the effect on the progression of the granuloma observed. In addition, interventions will be performed with simple drug effects on the host and changes in the ability of the immune system to respond will be observed. We will also show how the results in the Leishmania model publication in PLoS Computational Biology can be replicated.
Posted on 21 October 2015
The paper, entitled Suppression of AGO2 by miR-132 as a determinant of miRNA-mediated silencing in human primary endothelial cells, identifies an autoregulatory feedback mechanism that involves AGO2 suppression by miR-132.
The full paper can be found at the International Journal of Biochemistry and Cell Biology website.
The abundance of miR-132 ranges from constitutively high in the brain where it is necessary for neuronal development and function, to inducible expression in haematopoietic and endothelial cells where it controls angiogenesis and immune activation. We show that expression of AGO2, a protein central to miRNA-mediated gene silencing and miRNA biogenesis, is negatively regulated by miR-132. Using HeLa cells, we demonstrate that miR-132 interacts with the AGO2 mRNA 3'UTR and suppresses AGO2 expression and AGO2-dependent small RNA-mediated silencing. Similarly, miR-132 over-expression leads to AGO2 suppression in primary human dermal lymphatic endothelial cells (HDLECs). During phorbol myristate acetate (PMA)-activation of HDLECs, miR-132 is induced in a CREB-dependent manner and inhibition of miR-132 results in increased AGO2 expression. In agreement with the role of AGO2 in maintenance of miRNA expression, AGO2 suppression by miR-132 affects the steady state levels of miR-221 and miR-146a, two miRNAs involved in angiogenesis and inflammation, respectively. Our data demonstrate that the miRNA-silencing machinery is subject to autoregulation during primary cell activation through direct suppression of AGO2 by miR-132.
Posted on 15 April 2015
Our immune system has a vital role in keeping us healthy by detecting and responding to pathogens and viruses, while making sure our own healthy cells are not identified as threats. Understanding how this complex system works, and how we can use therapies to help it, is no longer a question just for biologists, but one for mathematicians, engineers, physicists, and computer scientists too. One way we can better understand our immune system is to build computer simulations of how it works.
Posted on 2 February 2015
Steph, a BBSRC CASE PhD student with Jon Timmis and Marika Kullberg and industrial supervison from GSK, has been chosen as one of the scientists taking part in the immunology zone (www.imascientist.org.uk) where children get the opportunity to ask questions anonymously that the scientists have to answer. The competition runs 9th-20th March and, after the first week, at the end of each day the people asking the questions vote for who gave the best answers and then people with the fewest votes get eliminated.
Posted on 14 January 2015
The paper, by YCIL researchers Kieran Alden, Paul Andrews, Fiona Polack, Mark Coles, and Jon Timmis, describes how argument notation (specifically Goal-Structuring Notation) can be used to document and justify the process of simulation development: from conception through to implementation and statistical analysis. The approach is inspired from safety-critical software engineering. To encourage and promote the use of this approach, we exemplify the use of argumentation using our model of secondary lymphoid organ development, and release a free online tool in which the argument structure can be built: Artoo. The Artoo tool is available from the software section of this website.
This paper is open-access and available from the Journal of the Royal Society Interface website.
The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Peyer's Patches. The argumentation structure is captured using Artoo (www.york.ac.uk/ycil/software/artoo), our Web-based tool for constructing fitness-for-purpose arguments, using a notation based on the safety-critical goal structuring notation. We show how argumentation helps in making the design and structured analysis of a model transparent, capturing the reasoning behind the inclusion or exclusion of each biological feature and recording assumptions, as well as pointing to evidence supporting model-derived conclusions.
Posted on 11 December 2014
Richard joined the group in 2010, and was supervised by Prof. Jon Timmis and Prof Eva Qwarnstrom (Dept Cardiovascular Science, Sheffield Medical School). His research focused on Agent-Based Modelling and Simulation of the intracellular NF-kB Signalling Pathway.
Richard joined the University of Lancaster in 2013 as a Management & Business Development Fellow, in the Management School.
Posted on 1 October 2014
Paul Kaye led the York team, which includes Jon Timmis, Mark Coles, and Dimitris Lagos, in entering the Virtual Infectious Disease Challenge competition, sponsored by the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3R's), aiming to develop a reliable computer-based model of the dynamics of infection and treatment response within an individual host. Jon and Mark are part of the SimOmics team, which is a new spin-out from the University developing decision support tools for clinical trials.
This work follows the successful bid for £100,000 to carry out early proof-of-concept studies, work conducted in YCIL by Simon Hickinbotham, Jon Timmis, and Paul Kaye. This proof-of-concept was presented to NC3R's, successfully competing against others to win £1 million in the final round of the challenge.
The computer model that the team will now develop further is intended to help predict the efficacy of drugs, vaccines and other treatments for leishmaniasis. Use of this technology is expected to significantly reduce the number of rodents needed in the pre-clinical stages of drug and vaccine development, given that a typical rodent efficacy study for new antibiotics or vaccines might involve up to 100 animals per candidate drug.
Posted on 30 September 2014
Our paper in Computation, by Richard Williams, Jon Timmis, and our collaborator Eva Qwarnstrom at the University of Sheffield, reviews the current state of computational modelling of the nuclear factor-kappa B (NF-ΚB) signalling pathway.
Our paper in the R Journal, by Kieran Alden, Mark Read (now at University of Sydney), Paul Andrews, Jon Timmis, and Mark Coles, demonstrates how to apply our spartan package in the analysis of a simulation, with our work on Peyer's Patch development used as an example.
Abstracts of these papers:
Williams et al:
In this review article, we discuss the current state of computational modelling of the nuclear factor-kappa B (NF-ΚB) signalling pathway. NF-ΚB is a transcription factor, which is ubiquitous within cells and controls a number of immune responses, including inflammation and apoptosis. The NF-ΚB signalling pathway is tightly regulated, commencing with activation at the cell membrane, signal transduction through various components within the cytoplasm, translocation of NF-ΚB into the nucleus and, finally, the transcription of various genes relating to the innate and adaptive immune responses. There have been a number of computational (mathematical) models developed of the signalling pathway over the past decade. This review describes how these approaches have helped advance our understanding of NF-ΚB control.
Alden et al:
In attempts to further understand the dynamics of complex systems, the application of computer simulation is becoming increasingly prevalent. Whereas a great deal of focus has been placed in the development of software tools that aid researchers develop simulations, similar focus has not been applied in the creation of tools that perform a rigorous statistical analysis of results generated through simulation: vital in understanding how these results offer an insight into the captured system. This encouraged us to develop spartan, a package of statistical techniques designed to assist researchers in understanding the relationship between their simulation and the real system. Previously we have described each technique within spartan in detail, with an accompanying immunology case study examining the development of lymphoid tissue. Here we provide a practical introduction to the package, demonstrating how each technique is run in R, to assist researchers in integrating this package alongside their chosen simulation platform
Posted on 24 September 2014
We will be in the Yorkshire Museum Hospitium between 5pm and 9pm, demonstrating how we can move from the laboratory mouse to the computer mouse.
Work is being funded at York to find ways of reducing, refining and replacing the use of animals in medical-related research and this exhibition will showcase a few current examples.
Normally new medicines are tested for safety and effectiveness in mammals like mice. We will explain, using special bug and immune cell robots, a touch screen simulator and other hands-on activities, how we are developing an artificial immune system to try to achieve a more efficient and animal free method of screening new drugs. Also, a new and exciting type of mathematical modelling used in toxicology will be on display.
For more Information on this event, see the YORNight event pages.
Posted on 4 September 2014
Our inaugural meeting will bring together world leading researchers to discuss the application of computational and mathematical approaches to assist with key immunological challenges. We will consider how we can maximise the potential of computational and mathematical approaches, the role of these approaches in drug discovery, and current methods tools and techniques available to generate novel biological insights from models.
Please note that attendance is by invitation only.
For invitees, the agenda of speakers at this event, details of the venue, and suggested accommodation options can be found on our Events page
Posted on 26 August 2014
The paper is available from the Royal Society Interface website.
Abstract: We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.
Posted on 3 July 2014
The event, on Sunday 3rd July, sees the start of Day 2 of the Tour De France, and will feature a marquee demonstrating the range of research completed at the University of York. As part of this, YCIL will have a stand in the marquee, where we will demonstrate how our immune system functions through the use of robots, and demonstrate our tool that can help simulate interventions to treat Leishmaniasis. If you have a ticket for the event, why not come over and take a look
Posted on 25 June 2014
Daniel is now working as a Research Associate at the University of Sheffield, using agent-based techniques to predict episodic alcohol drinking behaviours in the British population.
Posted on 18 June 2014
Members of the lab recently attended the 'York Festival of Ideas: Science out of the Lab' event in Parliament Square, to demonstrate how computational techniques can be used to understand how our immune system works. Immune cell robots were used to demonstrate the dynamics of the immune system during a response to pathogens. Visitors also had the opportunity to try out LeishSim, a computational tool developed by a team led by Paul Kaye (Funded by National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs)) that simulates potential treatment approaches for leishmaniasis, and has the potential to significantly accelerate therapeutic development for this neglected disease.
More information on NC3Rs can be found at www.nc3rs.org.uk
Posted on 20 May 2014
Kieran Alden, Paul Andrews, Jon Timmis and Mark Coles have had their paper, "Utilising a Simulation Platform to Understand the Effect of Domain Model Assumptions" accepted for publication in Natural Computing. This paper examines the assumptions that underpin the labs model of Peyer's Patch development, and the impact these decisions have on the predictions the simulator makes. The paper will appear later in 2014.
The lab have also had a number of papers accepted for presentation at ALife 2014: The 14th International Conference on the Synthesis and Simulation of Living Systems, to be held in New York in July 2014. These papers will be included in the conference proceedings published by MIT Press. These papers are:
Posted on 6 May 2014
The studentship will seek to utilise stochastic agent based computational modelling to simulate immune cell dynamics and function in silico. This will involve development of a multi-scale agent based model capturing key components of the immune system including key cell types (e.g. T cells, dendritic cells, stromal cells), secreted factors (e.g. cytokines, chemokines, growth factors) and the immune microenvironments (tissues, blood, and draining lymph nodes) using a suite of technologies developed in the York Computational Immunology Laboratory. One of the key computational challenges will be incorporating “omics” datasets into the computational model. The project will involve developing, calibrating and analysing the computational model using clinical data-sets from on-going trials at GSK. The simulator will be written in Java. This project will provide new insights into how therapeutic antibodies modulate cellular behaviour in the context of whole organ physiology and advance the application of computational modelling in understanding mechanisms of immune function.
Posted on 5 May 2014
Computer simulation approaches are taking an increasingly important role in many areas of the physical and social sciences. Whilst there are many appealing reasons for their use, it can be a non-trivial task to interpret or justify simulation result with respect to the system of study in the real world. The construction of simulators necessitates making assumptions (simplifications, approximations, abstractions, etc) during both the modelling and implementation stages. In this talk, entitled "Engineering Transparent Simulations for Science" I will present our ongoing efforts to engineer simulations in a transparent way in order that their results can be meaningfully analysed and challenged. I will highlight the need to understand the underlying model that the simulator implements, examine the importance of calibrating simulations, and discuss an approach to openly documenting simulator design and use.
Posted on 28 April 2014
Our immune systems keep us well, successfully defending us from bacteria, viruses and parasites that continually try to invade our bodies. Most of what we know about how the immune system works comes from studies on mice. However there are key differences between humans and mouse immune systems. Thus we are working on developing computer models of human immune system and developing artificial human immune systems in a test tube. Together we believe these technologies have the potential to significantly advance the process of developing new therapeutics while reducing the number of animals used in developing new medicines. Can computer mice replace laboratory mice? Can we replicate complex immune responses in a dish? Not yet, however we believe the technology described in this presentation will have a very significant impact in reducing and replacing the use of animals in research.
Posted on 19 March 2014
In addition to the analysis that could be performed previously, version 1.3 of Spartan includes a new technique that can perform parameter sampling for and analysis of simulations that were developed using Netlogo.
A publication detailing this addition is currently in review.
Posted on 26 February 2014
The C2D2 grant has allowed us to hire Dr Kieran Alden, who will return to YCIL in March to work for a year supporting various projects across YCIL.
Posted on 22 November 2013
In this study, we model the progression of disease as seen during experimental infection and also faithfully predicted many of the changes in cellular composition seen within granulomas over time. We have identified a previously unappreciated level of inter-granuloma diversity in terms of the development of anti-leishmanial activity.
Experimental visceral leishmaniasis, caused by infection of mice with the protozoan parasite Leishmania donovani, is characterized by focal accumulation of inflammatory cells in the liver, forming discrete “granulomas” within which the parasite is eventually eliminated. To shed new light on fundamental aspects of granuloma formation and function, we have developed an in silico Petri net model that simulates hepatic granuloma development throughout the course of infection. The model was extensively validated by comparison with data derived from experimental studies in mice, and the model robustness was assessed by a sensitivity analysis. The model recapitulated the progression of disease as seen during experimental infection and also faithfully predicted many of the changes in cellular composition seen within granulomas over time. By conducting in silico experiments, we have identified a previously unappreciated level of inter-granuloma diversity in terms of the development of anti-leishmanial activity. Furthermore, by simulating the impact of IL-10 gene deficiency in a variety of lymphocyte and myeloid cell populations, our data suggest a dominant local regulatory role for IL-10 produced by infected Kupffer cells at the core of the granuloma.
Posted on 15 November 2013
In the paper, titled "Determining Disease Intervention Strategies Using Spatially Resolved Simulations" we detail ARTIMMUS, a simulation of the murine autoimmune disease EAE that spans multiple cellular and spatial compartments and where a clinical disease course precedes spontaneous recovery.
Predicting efficacy and optimal drug delivery strategies for small molecule and biological therapeutics is challenging due to the complex interactions between diverse cell types in different tissues that determine disease outcome. Here we present a new methodology to simulate inflammatory disease manifestation and test potential intervention strategies in silico using agent-based computational models. Simulations created using this methodology have explicit spatial and temporal representations, and capture the heterogeneous and stochastic cellular behaviours that lead to emergence of pathology or disease resolution. To demonstrate this methodology we have simulated the prototypic murine T cell-mediated autoimmune disease experimental autoimmune encephalomyelitis, a mouse model of multiple sclerosis. In the simulation immune cell dynamics, neuronal damage and tissue specific pathology emerge, closely resembling behaviour found in the murine model. Using the calibrated simulation we have analysed how changes in the timing and efficacy of T cell receptor signalling inhibition leads to either disease exacerbation or resolution. The technology described is a powerful new method to understand cellular behaviours in complex inflammatory disease, permits rational design of drug interventional strategies and has provided new insights into the role of TCR signalling in autoimmune disease progression.
Posted on 11 October 2013
Confidence in adopting an in silico approach can be increased with the inclusion of a fully argued approach in tool implementation, as adopted in the engineering of safety-critical systems. Artoo allos the user to do this, constructing an argument in Goal-Structuring-Notation-like notation.
A publication describing Artoo is currently in preparation.
Posted on 29 September 2013
Dr Husakova has come to learn about the model and simulation development process and tools used at YCIL as part of a European Union funded research mobility project.
Posted on 13 July 2013
This emerging work, conducted by Mark Read, Jon Timmis, Louis Rose, Hannah Leonova and Magnus Tripp, is described in more detail in our extended abstract that will be published in the conference proceedings.
Posted on 16 May 2013
The Biosystems paper reports a principled methodology for extending existing computational simulations to include elements of the system which are not previously explicitly considered. The paper employs the addition of the CD200 regulatory pathway to the ARTIMMUS agent-based simulation of experimental autoimmune encephalomyelitis (EAE) as a case study.
Posted on 17 April 2013
The BMC Bioinformatics paper reports findings from in silico experimentation into the regulation of CD8Treg mediated killing of Th1 cells by dendritic cells (DC). We show that CD4Th1 and CD8Treg cells do not need to be primed by the same DC, and that temporal and spatial constraints affect the CD8Treg CTL response to EAE.
Posted on 10 March 2013
The use of simulation to investigate biological domains will inevitably lead to the need to extend existing simulations as new areas of these domains become more fully understood. Such simulation extensions can entail the incorporation of additional cell types, molecules or molecular pathways, all of which can exert a profound influence on the simulation behaviour. Where the biological domain is not well characterised, a structured development methodology must be employed to ensure that the extended simulation is well aligned with its predecessor. We develop and discuss such a methodology, relying on iterative simulation development and sensitivity analysis. The utility of this methodology is demonstrated using a case study simulation of experimental autoimmune encephalomyelitis (EAE), a murine T cell-mediated autoimmune disease model of multiple sclerosis, where it is used to investigate the activity of an additional regulatory pathway. We discuss how application of this methodology guards against creating inappropriate simulation representations of the biology when investigating poorly characterised biological mechanisms.
Posted on 6 March 2013
Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis R Toolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and our lab website. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.
Posted on 16 February 2013
In human and canine visceral leishmaniasis and in various experimental models of this disease, host resistance is strongly linked to efficient granuloma development. However, it is unknown exactly how the granuloma microenvironment executes an effective antileishmanial response. Recent studies, including using advanced imaging techniques, have improved our understanding of granuloma biology at the cellular level, highlighting heterogeneity in granuloma development and function, and hinting at complex cellular, temporal, and spatial dynamics. In this mini-review, we discuss the factors involved in the formation and function of Leishmania donovani-induced hepatic granulomas, as well as their importance in protecting against inflammation-associated tissue damage and the generation of immunity to rechallenge. Finally, we discuss the role that computational, agent-based models may play in answering outstanding questions within the field.
Posted on 15 December 2012
Spartan is a set of statistical techniques that can be utilised to understand how robust a simulation is to parameter change, and to provide key insights into simulated pathways in order to generate novel biological hypotheses. Spartan can be downloaded from the software section of this website.
Posted on 29 August 2012
Jon, Richard and Mark are all presenting at ICARIS. Jon presents on how to build confidence in the models and simulations of the immune system, Richard on how to augment the domain modelling phase using various statistical techniques and Mark presents on the role of CD200 in regulation of auto-immunity. Copies of the presentations will be made available soon.
Posted on 28 August 2012
His abstract was accepted and he has been invited to give a talk in the parasitic diseases workshop, where he will discuss our recent work modelling and simulating the early granulomatous response in visceral leishmaniasis. The talk at ECI will take place in the Alsh-1 room of the Glasgow SECC between 15:45-17:00 on the 8th of September.
Posted on 26 July 2012
For the full reference to the paper, see the lab publications page
Posted on 7 June 2012
This methodology paper describes the modelling process that can be used in the development of a computer simulation of a biological process, and uses our lymphoid tissue development as a case study. We then demonstrate statistical tools that can be used to explore the system further and provide some biological insight using the simulator.
Posted on 30 April 2012
The paper reports findings from an ex vivo culture system that examines cell behaviour at the twelve hour timepoint in Peyer’s Patch development. Results from an agent-based simulation are included that show our simulation replicates cell behaviour that is statistically similar to that observed in the ex vivo culture system. We then show how sensitivity analysis approaches can be used to give further insight into what causes changes in cell behaviour.
For the full reference to the paper, see the lab publications page