Mathematical Biology and Medicine

Recent Mathematical Biology and Medicine seminars

  • Luisa Cutillo (Leeds)
    An automated spectral clustering: theory and applications
    Wed 20 Nov 2019
  • Magnus Rattray (The University of Manchester, UK)
    Using Gaussian processes to infer pseudotime and branching from single-cell data
    Wed 30 Oct 2019
  • Robert West (University of Leeds, UK)
    Stochastic population dynamics with coloured external noise
    Wed 6 Nov 2019
  • Maria Carmen Romano (Aberdeen)
    Direct and indirect feedbacks in translation
    13 Nov 20191pm
  • Chris Abbosh (University College London)
    15 Mar 2019
    Coffee and tea will be available 11:30-11:50 in the Maths Common Room (level 9, School of Mathematics).
    Minimal residual disease and non-invasive tracking of tumour heterogeneity in non-small cell lung cancer using liquid biopsy
    The Cancer Research UK funded study TRACERx (TRAcking Cancer Evolution through therapy (Rx)) aims to recruit patients undergoing surgery for early-stage non-small-cell lung cancer (NSCLC). Patients donate blood pre- and post- operatively alongside multi-region sampled primary tumour tissue. Up to 40 percent of patients undergoing surgery for NSCLC experience relapse of their disease. At this point patients are approached for relapse tissue sampling. TRACERx therefore provides a strong platform for evaluating circulating tumor DNA (ctDNA) in early-stage NSCLC and a minimal residual disease (MRD) setting.
    In this presentation I will discuss data arising from a phylogenetic, patient-specific approach to ctDNA evaluation in the first 100 patients analysed as part of TRACERx and highlight clinicopathological factors associated with ctDNA detection in early-stage NSCLC suggesting the existence of distinct tumour phenotypic characteristics that predict the presence of ctDNA at measurable quantities in plasma. Additionally, I will discuss tumor volume limit of detection analyses and highlight potential implications of our findings within the context of early cancer detection. Finally, I will examine data arising from the ctDNA profiling of post-operative plasma samples taken in the adjuvant setting. This data demonstrates that ctDNA detection is specific for NSCLC relapse and that ctDNA detection precedes clinical diagnosis of NSCLC relapse, additionally I will provide examples of using phylogenetic ctDNA profiling to characterize the subclonal nature of relapsing NSCLC. To complete this presentation I will focus on steps being taken to translate these technology to clinic in industry led trials.
  • 23 Oct 2018. Ana Victoria Ponce Ana Victoria Ponce (Institute of Applied Mathematics, Heidelbergh University)
    Quantitative frameworks for understanding cancer cell invasion through in vitro scratch assays
    Scratch assays are standard in vitro experimental methods for studying cell migration. In these experiments, a scratch is made on a cell monolayer and imaging of the recolonisation of the scratched region is performed to quantify cell migration rates. This experimental technique is commonly used in the pharmaceutical industry to identify new compounds that may promote cell migration in wound healing; and to evaluate the efficacy of potential drugs that inhibit cancer invasion.
    In this talk, I will introduce two mathematical frameworks that I have been working on, motivated by these experiments. First, I will introduce a new migration quantification method that fits experimental data more closely, provides a more accurate statistical classification of the migration rate between different assays, and is able to analyse experimental data of lower quality than existing classical quantification methods. The method's robustness is validated using in vitro and in silico data.
    Then, I will introduce an age-structured population model that aims to explain the two phases of proliferation in scratch assays. The cell population is modelled by a McKendrick-von Foerster partial differential equation. The conditions under which the model captures this two-phase behaviour are presented.
  • 9 Oct 2018. Murad Banaji, (Middlesex University)
    Inheritance of behaviours in bio/chemical networks
    Understanding the possible behaviours of systems of chemical reactions is at the heart of systems biology. Chemical reaction network (CRN) theory focusses on what can be said about CRNs based on knowledge of the reaction network structure, but without precise data about rates of reaction. Results can be negative: certain behaviours are ruled out for certain reaction networks; or positive: certain behaviours must occur in reaction networks with certain structure.
    Within this general theme, a frequent, but imprecise, question is the following. Can the presence of certain "motifs" or "subnetworks" in a CRN be sensibly used to make predictions about dynamical behaviours of the network? The answer turns out to depend on the class of networks studied, the dynamical behaviour of interest and, crucially, the chosen notion of "motif" or "subnetwork". My talk will focus on some recent results where the behaviours in question are multistationarity and oscillation. Network modifications such as adding or deleting reactions, adding or deleting species from reactions, and inserting intermediates into a reaction are considered for their effects on multistationarity and oscillation. For example, it can be shown that both behaviours are inherited in the induced subnetwork partial ordering on fully open CRNs, but the same does not hold for general CRNs. Under certain mild assumptions, growing a CRN by inserting intermediates into reactions preserves the capacity for multistationarity in a CRN. The theorems are proved using essentially local techniques -- the implicit function theorem and regular and singular perturbation theory -- and hold for various kinetics including (but not exclusively) mass action kinetics.
  • 2 Oct 2018. Ignacio Moraga (Dundee)
    Mapping Cytokine Pleitropy: from cell surface receptor dynamics to signalling diversification and functional selectivity
    A hallmark of cytokine biology is functional selectivity - the ability to elicit differential cellular responses through the same cell surface receptor. The molecular mechanisms regulating this process have so far remained unclear, which has hindered the translation of these ligands to the clinic. Here I will present two ongoing projects in the lab addressing functional pleiotropy by two important immuno-modulatory cytokines, IL-6 and IL-27.
    IL-6 acts as a central regulator of the immune response by eliciting a potent pro-inflammatory response. IL-6 induces Th-17 cell differentiation and inhibition of T regulatory (reg) and Th-1 cell differentiation. In addition, IL-6 signalling is often found deregulated in human diseases, making this cytokine highly relevant for human health. We have used a multidisciplinary approach encompassing the engineering of surrogate IL-6 ligands with different receptor complex half-lives, their biophysical characterization, the study of their surface dynamics and endosomal traffic, as well as characterization of their signalling patterns and functional properties to identify cellular and molecular determinants contributing to IL-6 functional diversity. Our results reveal a complex relationship between IL-6 surface receptor half-life and the endosomal compartment in defining amplitude and quality of the signalling and biological responses induced by IL-6.
    IL-6 and IL-27 share gp130 as their main signalling receptor, but IL-27 elicits a potent anti-inflammatory response with the differentiation of T regulatory cells and the production of IL-10 as hallmark phenotypes. How activation of the same signalling pathways by these two cytokines through a shared receptor (gp130) results in opposite biological responses is not well defined. Our results show that STAT3 is activated to a similar extent by both IL-6 and IL-27, whereas STAT1 is activated more sustainably by IL-27 than by IL-6. The more sustained STAT1 activation by IL-27 was not the result of differential phosphatase activity regulation, or new protein synthesis, but it did required continuous JAK activation. Mutation of Y613F in IL-27Rα almost completely abrogated STAT1 activation by IL-27, indicating that IL-27R? provides high affinity binding sites for STAT1. We propose a model where the binding affinity of STATs for cytokine receptor intracellular domains controls signalling identity and functional specificity by cytokines.
  • Tuesday 12 June 2018
    Veronika Bernhauerova (Institut Pasteur>
    Density-Dependent Viral Clearance During Respiratory Virus Infections
    Respiratory infections caused by influenza A virus (IAV) or parainfluenza virus (PIV) cause a significant amount of morbidity and mortality each year. Understanding how virus is controlled by immune responses is critical to combatting these infections and developing effective therapeutics. To understand the mechanisms of viral control, we infected mice with either influenza A/PR8 or mouse adapted PIV (sendai virus) and measured viral loads and CD8 T cells over the infection course. We then developed two kinetic models that quantify the different phases of viral decay, and used a rigorous ensemble method to fit the models to our data. Our models implicitly or explicitly account for CD8 T cell-mediated viral clearance and indicate that IAV and PIV are cleared in a density-dependent manner regardless of mechanism. Each model exhibits strong sensitivity to changes in select parameters involved in infected cell clearance, which significantly affect infection duration and viral loads. We further examined parameter behavior to determine how the models are related and better interpret the results of models that implicitly account for viral control in cases where immunological data is absent. Together, our models provide well-characterized representations of respiratory infection dynamics and insight into the regulation of IAV and PIV control.
  • Monday 15 May 2018
    Mario Castro (Madrid)
    The physics of cauliflowers Some fascinating natural shapes present cauliflower-like structures. Surfaces of thin films, turbulent and combustion fronts, geological formations or biological systems are strikingly similar in spite of their diversity. In all cases, one can recognize a typical motif independently of the scale of observation. These appealing morphologies combine two apparently contradictory features: a hierarchical (fractal) structure and disorder (randomness). Fractal geometry is a useful tool to describe natural shapes but, to gain physical insight a theoretical framework that captures the way that they can be produced is needed. We present a compact dynamical equation for evolving surfaces that produces cauliflower-like structures and has a large degree of universality. This nonlinear equation allows us to identify non-locality, nonconservation and randomness, as the main mechanisms controlling the formation of these ubiquitous shapes. To test our theory at different scales, we have grown thin film nano-structures by Chemical Vapor Deposition and measured the scaling properties of (centimeter size) cauliflower plants. Besides, control of the expermental system also allows us to control the patterns and produce experimental observations of first and second order "phase"-transitions reminiscent of statistical mechanics systems but in a non-equilibrium context.
  • 6 December 2017 Frank Ball (Nottingham)
    Inference for emerging epidemics among a population of households .pdf
    Households are a key component of human population structure and have significant impact on disease dynamics. This talk, based on joint work with Laurence Shaw (Nottingham Trent University), is concerned with estimation of parameters governing an SIR (susceptible-infective-recovered) epidemic among a population of households from observation of the early, exponentially growing phase of an epidemic. Specifically, it is assumed that an estimate of the exponential growth rate is available from general data on an emerging epidemic and more-detailed, household-level data are available in a sample of households. Parameter estimates obtained using the final size distribution of single-household epidemics are usually biased owing to the emerging nature of the epidemic. An alternative method, which accounts correctly for the emerging nature of the epidemic, is developed by exploiting the asymptotic theory of supercritical branching processes. The methodology is illustrated by simulations which demonstrate that it is feasible for finite populations and numerical studies are used to explore how changes to the parameters governing the spread of an epidemic affect the bias of estimates based on single-household final size distributions.
  • Tuesday 31 Oct 2017 Graham Donovan University of Auckland
    Clustered ventilation defects in asthma
    Clustered ventilation defects are a hallmark of asthma, typically seen via imaging studies during asthma attacks. The mechanisms underlying the formation of these clusters is of great interest in understanding asthma. Because the clusters vary from event to event, many researchers believe they occur due to dynamic, rather than structural, causes. This talk will cover recent progress in understanding the mathematics behind clustered ventilation defect formation, interactions with structural factors, and the implications for understanding asthma and its treatment.
  • 25 October 2017 Mike Brockhurst University of Sheffield
    Horizontal gene transfer (HGT) allows evolution to proceed by adaptive leaps via gain of new functional traits, such as antibiotic resistance. Bacterial comparative genomics reveals extensive horizontal gene transfer facilitated by mobile genetic elements (MGE) but little is known about how the ecology of microbial communities and their environments affects HGT dynamics. Moreover, intragenomic conflict between MGEs and their bacterial hosts is common and impedes HGT. I will present data from experimental evolution of environmental Pseudomonas species in soil microcosms showing that gene mobilisation and transfer is most likely when the MGE is useless, and that conflicts between chromosomes and MGEs can be rapidly resolved by diverse mechanisms of compensatory evolution of the MGE or the host, which ameliorates the cost of the MGE allowing its survival
  • 18 October 2017 Vahid Shahrezaei Imperial College London
    Cell size and growth rate regulation of stochastic gene expression .pdf
    Cells adopt their physiology globally in response to different growth conditions. This includes changes in cell division rate, cell size, and also in gene expression. These global physiological changes are expected to affect noise in gene expression in addition to average expression. Gene expression is inherently stochastic and the amount of noise in proteins depend on parameters of gene expression and cell division cycle. Here we use models of stochastic gene expression inside growing and dividing cells to study the effect of cell division rate and cell size on noise in gene expression. In the second part of the talk, I discuss how the single molecule RNA Fish data can be used to infer specific form of global regulation of gene expression by cell size.
  • 10 October 2017 Karl Wienand University of Munich
    Evolution in a randomly changing environment .pdf
    Cooperation between individuals is beneficial for the population as a whole, but not for those who sacrifice their resources and energy to the common good. Despite this disadvantage, cooperators could survive, for example if their competitors, by chance, go extinct. In nature, this competition plays out in unpredictable conditions. In this talk, I will show how this environmental randomness intertwines with the population's own, leveling the playing field in the competition. As a result, the survival chance of cooperating individuals is much improved.
  • 4 October 2017 Julian Hiscox (Liverpool)
    Analysis of the innate and adaptive response in Ebola virus disease - delineating survival and fatal outcomes and correlates of protection
    The Ebola virus outbreak in West African between 2013-2016 was unpresented in scale and revealed hitherto unknown aspects of virus biology. As part of the European Mobile Laboratory deployment in Guinea, left over diagnostic samples were analyzed to investigate Ebola virus disease (EVD), and what delineated survival from a fatal infection, focusing on the peripheral blood transcriptome. Follow on studies have tracked survivors over the course of over a year to map the adaptive response and how this compares to vaccinated individuals. The transcriptome data, complemented by immunological analysis, revealed EVD is a complex process influenced by the strength of the host response, the underlying burden of infection, bacterial translocation across the gut and potential differences in viral and host genetics. The long-term study of Ab and T cell responses in survivors of EVD has implications for vaccine evaluations and support for correlates of protection. The evidence also suggests the existence of sub symptomatic survivors that has implications for control strategies.
  • Tuesday 6 June 2017 Kevin Burrage(Visiting Professor Oxford University and Queensland University of Technology)
    Unlocking datasets by calibrating populations of models to data density: a study in atrial electrophysiology
    The understanding of complex physical or biological systems nearly always requires a characterisation of the variability that underpins these processes. In addition, the data used to calibrate such models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), that is multiple copies of a single mathematical model but with different parameter values.
    To date this calibration has been limited to selecting models that produce outputs that fall within the ranges of the dataset, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a dataset. We demonstrate the benefits of our technique using a dataset from a cardiac atrial electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche-Ramirez-Nattel model for human atrial action potentials.
    Our approach accurately captures the variability inherent in the experimental population, allows for uncertainty quantification and also allows us to identify the differences underlying stratified data as well as the effects of drug block.
  • 7 December 2016
    Rosalind Allen (University of Edinburgh)
    How do antibiotics work? Linking antibiotic inhibition to bacterial physiology using theoretical models and experiments
    Many experiments on bacterial action and the evolution of resistance are done in standardized lab conditions, yet bacteria in infections experience complex growth conditions, which can vary in time and space. Exposure to nutrients can also vary between different infections. We have investigated how the richness of the nutrient medium affects the efficacy of action of ribosome-targeting antibiotics. We find apparently conflicting results: some ribosome targeting antibiotics work better on rich media while others work better on poor media. These results can be explained by a simple mathematical model, which also makes predictions for the dynamical effects of antibiotics which are potentially clinically relevant but have yet to be tested.
  • 30 November
    Ramon Grima (University of Edinburgh)
    Incorporating volume-exclusion effects in stochastic spatial models of biochemical systems
    Spatial stochastic effects in biochemical reaction systems have been mostly studied via the reaction-diffusion chemical master equation (RDME), a spatial discrete stochastic formulation of chemical kinetics which assumes well-mixing on small local scales and point-like interactions between molecules. However, molecules are not points and experiments show that volume exclusion effects are important due to the highly non-dilute nature of the intracellular environment. I will here describe our recent work on modifying the RDME using scaled particle theory and other methods to describe volume exclusion effects on the diffusion and reaction of molecules in non-dilute media. For homogenous media, the new RDME can be solved exactly in equilibrium conditions. For heterogeneous media, I will show that stochastic simulations using this new RDME and a mean-field theory based on it, are in excellent agreement with the considerably more computationally expensive technique of Brownian dynamics.
  • 23 November
    Jolanda M. Smit. Department of Medical Microbiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
    Early events in dengue and chikungunya virus infection
    Dengue virus (DENV) and Chikungunya virus (CHIKV) are rapidly emerging arthropod-borne viruses that cause a wide range of disease symptoms. In case of dengue, increased disease severity is associated with pre-existing DENV antibodies and high circulating virus titers, which suggests that antibodies directly influence the infectious properties of the virus.
    I will discuss our recent findings on the early events in DENV and CHIKV infection. We applied live cell imaging and single virus tracking to unravel the route of cell entry and microarray analysis to identify the cellular responses upon infection in the absence and presence of antibodies. For DENV, emphasis will be on the critical determinants in antibody-dependent enhancement (ADE) of infection. For CHIKV, emphasis will be on the route of cell entry, molecular mechanism of membrane fusion, and how neutralizing antibodies interfere with these processes.
    Antibody-bound DENV particles were observed to enter though a novel phagocytosis-like pathway that is distinct from entry in absence of antibodies. We observed that antibody-bound particles are captured and engulfed by the cell through active formation of actin-induced membrane protrusions. Macrophages actively sense and capture antibody-DENV particles located away from its cell body. The distinct route of entry and trafficking behavior likely increases the fusion potential of the virus. Indeed, DENV particles internalized via antibodies appear to have a higher chance to induce membrane fusion. The enhanced fusion potential of the virus is a key factor in ADE of infection.
    CHIKV cell entry is a very rapid process. The vast majority of particles that fuse first co-localize with clathrin. The time from initial colocalization with clathrin till the moment of membrane fusion was on average 1.7 minutes, highlighting the ast nature of the cell entry process of CHIKV. Membrane fusion was predominantly observed from within Rab5-positive endosomes and often occurred within 40 seconds post-delivery to endosomes. CHIKV fusion is strongly promoted by sphingolipid and cholesterol in the target membrane. Potent neutralizing antibodies against the envelope glycoprotein E2 and E1 were found to act at distinct stages of the virus life cycle, e.g. virus cell binding, membrane fusion and viral egress. Antibodies that inhibit membrane fusion were found to block the membrane fusion reaction at distinct steps.
  • 21 November 2016
    Stephen Coombes. University of Nottingham.
    Next generation neural field modelling Neural mass models have been actively used since the 1970s to model the coarse grained activity of large populations of neurons and synapses. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. In this talk I will first discuss a theta-neuron network model that has recently been shown to admit to an exact mean-field description for instantaneous pulsatile interactions. I will then show that the inclusion of a more realistic synapse model leads to a mean-field model that has many of the features of a neural mass model coupled to an additional dynamical equation that describes the evolution of network synchrony. I will further show that this next generation neural mass model is ideally suited to understanding beta-rebound. This is readily observed in MEG recordings whereby hand movement causes a drop in the beta power band attributed to a loss of network synchrony. Existing neural mass models are unable to capture this phenomenon since they do not track any notion of network coherence (only firing rate). I will finish my talk by presenting some preliminary results for the spatio-temporal pattern formation properties of a neural field version of this model.
  • 17 November 2016
    Michael Shapiro. Tufts University, Boston, USA
    Modeling the cyclic pattern of infection in Epstein-Barr virus
    Epstein-Barr virus is perhaps the most successful human pathogen, infecting over 90% of the human population world wide. It hijacks the normal life cycle of our B-cells to set up an infection that is usually benign and lasts the life of the human host. In doing so, it transits a cycle of infective stages within the body. We model the interactions between the virus and the host immune system using a set of coupled differential equations that track this cycle of infection and the host response. We'll spend a little bit of time looking under the hood of this model and see why it explains this stability. We will look at validation of the model and see how the model can appraise biological hypotheses. In addition to stable life-long infection, EBV infection can also undergo runaway, life-threatening proliferation, for example in post-transplant lympho- proliferative disorder. We will see that adding a little more realism to the model can explain this breakdown of stability. We will identify a pair of factors involved in this runaway proliferation.
  • Monday 31st October 2016
    3:00pm in MALL 1 & 2 (School of Mathematics, level 8)
    Ruth Baker (University of Oxford)
    Cell biology processes: model building and validation using quantitative data
    Cell biology processes such as motility, proliferation and death are essential to a host of phenomena such as development, wound healing and tumour invasion, and a huge number of different modelling approaches have been applied to study them. In this talk I will explore a suite of related models for the growth and invasion of cell populations. These models take into account different levels of detail on the spatial locations of cells and, as a result, their predictions can differ depending on the relative magnitudes of the various model parameters. To this end, I will discuss how one might determine the applicability of each of these models, and the extent to which inference techniques can be used to estimate their parameters, using both cell- and population-level quantitative data.
  • Dr Matthew Spencer (University of Liverpool) Compositional dynamics of coral reefs
    Wednesday, October 19th 2016 at 12noon
    Venue: MALL 1, level 8 in the School of Mathematics
    Coral reefs are of great biological and socioeconomic importance. On a coral reef, we usually have estimates of the relative abundance of each kind of organism, but not of absolute abundances. Similar kinds of data arise in other situations such as environmental DNA sequence data for microorganisms, and fossil pollen data for forests. Relative abundances are an example of compositional data, lying in a simplex. I will show how we can build simple stochastic models for the dynamics of compositional data, fit them to observations, and use them to answer questions about coral reef dynamics and conservation.
  • Persistence and extinction for populations modelled by stochastic differential equations or piecewise deterministic Markov processes
    Alex Hening (University of Oxford)
    Date and time: Wednesday, 5 October 2016 at 12noon
    In recent years there has been a growing interest in the study of the dynamics of interacting stochastic populations. A key question in population biology is understanding the conditions under which populations coexist or go extinct. Coexistence can be facilitated or negated by both biotic interactions and environmental fluctuations. We analyse a very general framework of n populations that live in a stochastic environment and which can interact nonlinearly (through competition for resources, predator-prey behaviour etc). We look at models given by stochastic differential equations (SDE) or piecewise deterministic Markov processes (PDMP). In both settings we give sharp conditions under which the populations converge exponentially fast to their unique stationary distribution as well as conditions under which some populations go extinct exponentially fast. The analysis is done by studying the properties of the invariant measures of the system that are supported on the boundary of the domain. Our results generalize and extend most of the SDE and PDMP models that appear in the literature. In particular, we extend results on two dimensional Lotka-Volterra models (both SDE and PDMP), two dimensional predator-prey models and two predator and one prey models. We also show how one can use our methods to classify the dynamics of any two-dimensional system satisfying some mild assumptions. This is joint work with Dang Nguyen.
  • Human immune responses in space and time
    Prof. Donna L. Farber (Columbia University Medical Centre, NY)
    Date and time: Tuesday 13 September 2016 at 11am (notice the unusual date and time)
    While the majority of human immune studies rely on the sampling of peripheral blood, innate and adaptive immune responses are initiated, function and maintained in diverse tissue sites throughout the body. The early innate cells that encounter pathogens at key entry points such as the lungs and intestines include dendritic cells (DC) which migrate to tissue-draining lymphoid tissue for T cell priming and initiation of adaptive immune responses. T cells activated in lymphoid tissues differentiate to effector T cells which infiltrate diverse infection sites and can generate non-circulating tissue resident memory T cells for maintaining protective responses to site-specific infections. At present, our understanding of tissue-localized innate and adaptive immune responses is based largely on mouse models, and we lack fundamental knowledge on how innate and adaptive cells are organized and function in human tissues. We have set up a novel human tissue resource where we obtain blood and multiple lymphoid, mucosal, endocrine and other peripheral tissue sites from individual organ donors through a research protocol with LiveOnNY, the organ procurement organization for New York City. This resource has given us unprecedented access to tissue sites not previously been investigated in humans, and our studies revealed tissue compartmentalization of T cell subsets (based on phenotype and function) starting early in life and persisting through the seventh decade of life. Here, we have delved into mechanisms for how T cells are maintained and become compartmentalized in different sites, by T cell clonal analysis through T cell receptor sequencing, by whole transcriptome profiling of tissue-resident versus circulating subsets, and an extensive analysis and mapping of human DC subsets in diverse lymphoid and mucosal sites. Our results indicate tissue and regional effects on T cell maintenance, from na´ve to memory, with long-lived na´ve T cells persisting through in situ homeostasis in specific lymph nodes (LN), and memory T cells adapting transcriptional programs for tissue residence. DC populations further exhibit tissue-specific properties, with certain mucosal draining LN sites for active priming, while others are marked by quiescence. Together, our results provide new insights into how human immune responses are controlled via tissue specific localization.
  • 25 May: Victoria Sanz-Moreno (Randall Division of Cell and Molecular Biophyics, King's College London)
    Beyond cell migration: multidisciplinary approaches to study cytoskeletal dynamics in cancer Rho GTPases are molecular switches that control the cytoskeleton. Deregulation of Rho GTPases can result in aberrant function and disease, including cancer. The spreading of cancer cells from one part of the body to another, called metastasis, is one of the biggest causes of cancer death. To metastasise, tumor cells must move through tissues and cross tissue boundaries, which requires cell motility, remodeling of cell-cell contacts and interactions with the extracellular matrix. Rho GTPase activity controls actomyosin contractility, adhesive forces and matrix degradation, all necessary for cells to migrate and disseminate efficiently. In order to understand how migration is sustained over time in cancer cells, my lab has used a combination of ?OMICs?, state of the art microscopy in 3D matrices, molecular biology, animal models and patient data analysis (1,2,3,4). Furthermore, I will discuss how Rho GTPase signalling and actomyosin contractility establish a series of feedback loops with transcriptional programs that control not only cell migration but other important cell decisions.
  • Wed 27 April at 12noon.
    Marc de Kamps (School of Computing, Leeds) Computational geometry for modelling neural populations
    The predominant method for modeling populations is by creating a large number of instances of individual model neurons. When individual neurons are indistinguishable from each other, one may just as well use population density functions. Such a function describes the probability of finding an individual neuron in a certain part of state space. The function evolves under the deterministic neural dynamics as well as a bombardment of external input spikes, which is modeled by a stochastic process. In the past synaptic input has often been modeled as white noise and using so-called leaky-integrate-and-fire model neurons, this leads to an Ornstein-Uhlenbeck process. Firing rates can be estimated from the population by calculating first exit times. Neural circuits can then be modeled as systems of coupled Fokker-Planck equations. In the real brain synapses are not small, density profiles are not continuous, and spike trains are not always described well by white noise. We present a numerical method that can handle discontinuous density profiles, large jumps and Poisson Master equations, and as such constitute a considerable generalization of population density techniques as used in neuroscience. We also have made some inroads into non-Markov processes. The method is not based on finite differences, or elements, but uses techniques from computational geometry. We will present some simulations of neural circuits that are difficult to obtain by any other method.
  • Wed 6 April at 12noon
    Rebecca Fitzgerald and Maria Secrier (MRC Cancer Unit, Cambridge)
    Unravelling the complexity of oesophageal cancer genomes and relevance for clinical management Oesophageal adenocarcinoma has increased rapidly in the western world, but the underlying causes for this are not well understood. It commonly arises on the background of Barrett's oesophagus, which is associated with reflux of acid and bile, even though it is not known whether these are mutagenic. Overall, the mutational processes that contribute to this cancer are unclear. We sought to investigate the aetiology and sub-classification of oesophageal adenocarcinoma by characterizing the mutational patterns in the genomes of 119 chemo-naive oesophageal adenocarcinoma patients for which whole-genome sequencing data was available. Using non-negative matrix factorization methodology described by Alexandrov et al. (Nature, 2013) as well as other probabilistic approaches that look at the distribution of nucleotide substitutions throughout the genome, we explored the signatures of risk factor-induced mutational processes operative in this cancer. Based on the mutational signature composition of the samples, we were able to characterise three main subgroups with therapeutic relevance: i) dominated by BRCA/APOBEC signatures with prevalent defects in the homologous recombination pathway; ii) T>G mutational pattern, possibly related to gastric acid reflux, and associated with a high mutational load and neoantigen burden; iii) complex C>A substitution pattern with evidence of reduced genomic instability. Based on the aetiology and molecular characteristics of the disease, we suggest distinct therapeutic approaches for the three defined subgroups as addition to the current standard of care. We also show this mutational signature-based classification can bypass spatial sample heterogeneity and could be used as a high-throughput stratification strategy in the clinic through sequencing at lower coverage.
  • 2nd March: Mar Rodriguez Girondo (Leiden University Medical Center, Leiden, The Netherlands)
    On the combination and added value of omic datasets in prediction problems
    Proteomics, metabolomics and related omics research fields are revolutionising bio-molecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue or other. The data itself is typically of very high dimension for each omic measurement. Increasingly, clinical studies include several sets of such omics measures available for each patient, measuring the response at distinct level of biology. In predictive research, whether diagnostic or prognostic, these novel complex data structures pose new statistical challenges, which are part due to the high-dimensional nature of each omics measure, but also to the fact that distinct measurement platforms are typically employed for each omics data source which implies systematic differences of measurement scale, precision, normalisation approach and so on. In this talk, integration of different sources of omic biomarkers with prediction of health traits as ultimate goal is addressed. Specific topics covered are strategies for combination of distinct high-dimensional omic markers for improved prediction and assessment of added-value of omic sources over either traditional risk scores or other omics measures. The main findings are illustrated via simulations and with two biomedical applications: firstly we study the role of different sources of proteomics markers in cancer research, and secondly we investigate metabolomics and transcriptomics in relation to BMI and obesity.
  • Wednesday 10 February 2016
    Utkir Rozikov, Institute of Mathematics, Tashkent, Uzbekistan.
    Stochastic dynamical systems in population biology
    The talk is devoted to the properties of nonlinear (stochastic) dynamical systems, which arise in problems of mathematical genetics and population biology. Many results about the asymptotic behaviour of trajectories of such dynamical systems will be presented and several open problems will be discussed.
  • Wednesday 6 January 2016
    David Sansom, UCL Institute of Immunity and Transplantation
    Understanding CTLA-4: a quantitative checkpoint for T cell activation?
  • Wednesday 25 November
    Bartlomiej Waclaw (Edinburgh)
    Biological evolution in spatially-structured populations
    Biological evolution is not only an important, unifying concept in biology, but it is also relevant for practical problems such as antimicrobial resistance and cancer. Many models of biological evolution assume well-mixed populations where each organism experiences the same conditions independently of its spatial location. This is appropriate for modelling "wet lab" experiments with microorganisms growing in a chemostat or a shaken flask. However, naturally occurring populations are rarely well-mixed and typically have some spatial structure. Examples range from animal populations (including humans) to bacterial biofilms to solid tumours. In this talk I will discuss how adding space to models of biological evolution affect their predictions, and how these predictions can be tested experimentally.
  • Tuesday 3rd November 2015
    Erwin Frey (LMU Munich)
    Evolutionary Games of Condensates
    Condensation phenomena occur in many systems, both in classical and quantum mechanical contexts. Typically, the entities that constitute a system collectively concentrate in one or multiple states during condensation. For example, particular strategies are selected in zero-sum games, which are generalizations of the children's game Rock-Paper-Scissors. These winning strategies can be identified with condensates.In our work, we apply the theory of evolutionary zero-sum games to explain condensation in bosonic systems when quantum coherence is negligible. Only recently has it been shown that a driven-dissipative gas of bosons may condense not only into a single, but also into multiple non-degenerate states. This phenomenon may occur when a system of non-interacting bosons is weakly coupled to a reservoir and is driven by an external time-periodic force (Floquet system). On a mathematical level, this condensation is described by the same coupled birth-death processes that govern the dynamics of evolutionary zero-sum games. We illuminate the physical principles underlying the condensation and find that the vanishing of relative entropy production determines the condensates. Condensation proceeds exponentially fast, but the system of condensates never comes to rest: The occupation numbers of condensates oscillate, which we demonstrate for a Rock-Paper-Scissors game of condensates.
  • 21 October 2015
    Orkun S. Soyer (University of Warwick)
    Two key approaches in modern biology are systems and synthetic biology. Between these two powerful paradigms, a surprisingly neglected aspect is the fact that complex biological systems as well as their building blocks are the result of evolution. How can we explain evolution of complex biological systems from sub-cellular to cellular levels? Do common evolutionary processes, such as fluctuating environments, leave fingerprints in the architecture or dynamics of these systems? Are there inherent trade-offs in these systems , and if so, how does evolution work around such trade-offs? If we can answer these questions, can the resulting evolutionary insights be useful in our quest to (re)design biological systems in synthetic biology? I will illustrate computational and experimental approaches towards answering such questions and deciphering the evolutionary processes that can lead to complex cellular systems. In particular, I will describe a set of recent projects where we applied a combination of in silico evolution and mathematical modeling to decipher molecular mechanisms of ultrasensitivity and multistability in signaling networks. Time permitting, I will use the last part of the talk, to describe expansion of this type of approaches to the study of cellular interactions. This part of the talk will feature our ongoing work in a recent large-scale project on understanding microbial metabolic interactions and designing such interactions for biomethane production.
  • 20 May 2015: Rich Savage (Warwick)
    Sniffing out disease
    Disease smells.
    More specifically, many diseases change the body's production of various Volatile Organic Compounds (VOCs), meaning that they change how the body smells. We are now able to build machines to measure this in blood/urine/faeces/breath, opening up new ways to diagnose a wide range of diseases, from cancer to infectious disease.
    The data are often complex and high-dimensional, which means they can be challenging to analyse, but a range of modern statistical, signal processing, and machine learning methods are allowing us to get more out of the data and hence improve our ability to use VOCs to make medical diagnoses.
  • 13 May 2015: Christina Cobbold (Glasgow)
    Pathogens on the move: Mathematics of evading the immune system
    Mathematical models of infectious diseases have a long history with early models offering important insight into epidemics and guidance for designing effective vaccination strategies. Increasing availability of genetic data offers new opportunities to understand the fundamental mechanisms of how an infection plays out within a single host. In this talk I will present a mathematical model for African sleeping sickness, a potentially fatal disease caused by the parasite trypanosome. Typanosomes are among the many parasites that exhibit genetic variation as a mechanism to sustain chronic infections within their hosts. Other examples where genetic variation is an important mechanism for pathogens to evade the immune system include influenza and malaria infection. In the case of the trypanosome parasite it obtains protection from the hosts immune response by switching between genetically distinct parasite variants. Typical trypanosome infections consist of oscillations in the level of infection present in the hosts, where each peak is composed of a group of genetically distinct parasite variants. In this talk I will present a mathematical model used to investigate within-host dynamics of African trypanosomes and examine the role and limitations of parasite genetic variation. In particular I will discuss how trypanosome infection may play out differently in different hosts.
  • Wednesday 6th May 2015
    Dr Ian Hall
    "Developing a toolbox of models to mitigate bioterrorism and emerging disease public health threats"
    The Microbial Risk Assessment and Behavioural Science team has a remit to provide evidence based operational analysis, scientific advice and technological solutions to inform both preparedness for, and the response to, the public health threats arising from deliberate release and new and (re-) emerging infectious diseases. This talk will focus on the mathematical and statistical modelling work of the team. It is essential to have a suite of tools pre-prepared for an outbreak investigation and so this talk will cover the proposed models to infer the source terms of an infectious agent causing human cases and touch on the decision support tools that use this evidence. To provide evidence based advice the assumptions and data used in the models require a clear understanding and so this talk will also touch on the challenges currently being considered with dose response, population movement, behavioural science and ecology associated with these diseases.
  • 29 April 2015: Thomas Woolley (Oxford)
    Cellular blebs: pressure-driven, axisymmetric, membrane protrusions
    Human muscle undergoes an age-related loss in mass and function. Preservation of muscle mass depends, in part, on stem cells, which navigate along muscle fibres in order to repair damage. Critically, these stem cells have been observed to undergo a new type of motion that uses cell protrusions known as blebs, which protrude from the cell and permit it to squeeze in between surrounding material.
    By solving the diffusion equation in polar coordinates we have mathematically investigated this blebbing phenomenon with a particular focus on characterizing the effect of age on cell migration. Our results have then been fitted to experimental data allowing us demonstrate that young cells move in a random ??memoryless?? manner, whereas old cells demonstrate highly directed motion, which would inhibit the chances of a cell finding and repairing damaged tissue.
    Further, we have constructed a mechanical model for the problem of pressure-driven blebs based on force and moment balances of an axisymmetric shell. Through investigating multiple extensions of this model we find numerous results concerning size, shape and limiting factors of blebs. Finally, leading us to consider much simpler equations which allow us to connect motion to mechanical properties of the cell, thus, coming full circle in our research.
  • 18 March 2015: Peter Young (York)
    Diversity and evolution of bacterial genomes
    We can think of the bacterial genome as having two parts: the core genome does the basic housekeeping and is much the same in all members of the species, while the accessory genome has packages of genes that are not essential to the operation of the cell, but can be very useful in coping with aspects of the real world. Each bacterium we isolate has a different combination of these genes, which are passed from one bacterium to another, sometimes even between different species (horizontal gene transfer, HGT) Bacteria are like smartphones. Each phone comes out of the factory with standard hardware and operating system (core genome), but gains a unique combination of capabilities through apps (accessory genes) downloaded through the internet (by HGT).
    My group has recently published* an analysis of the genomes of 72 isolates of the bacterium Rhizobium leguminosarum. I will present some of the findings and hope the audience will provide inspiration for more and better analyses of bacterial genomes in the future.
    * Kumar, N., Lad, G., Giuntini, E., Kaye, M. E., Udomwong, P., Shamsani, N. J., Young, J. P. W. & Bailly, X. (2015). Bacterial genospecies that are not ecologically coherent: population genomics of Rhizobium leguminosarum. Open Biology, 5(1), 140133. DOI:10.1098/rsob.140133
  • A.J. (Hannes) Pretorius (Leeds)
    25 Feb 2015
    Cell lineage visualisation
    Abstract: Cell lineages are descriptions of the developmental history of cell populations produced by combining time-lapse high-throughput imaging with image processing. Biomedical researchers study cell lineages to understand fundamental processes, such as cell differentiation and the pharmacodynamic action of anticancer agents. Yet, the analysis of cell lineages is hindered by their complexity and insufficient capacity for visual analysis. We present a novel approach for interactive visualisation of cell lineages. Based on an understanding of cellular biology and high-throughput methodology, we identify three requirements: multimodality (cell lineages combine spatial, temporal, and other properties), symmetry (related to lineage branching structure), and synchrony (related to temporal alignment of cellular events). We address these by combining visual summaries of the spatiotemporal behaviour of an arbitrary number of lineages, including variation from average behaviour, with node-link representations that emphasise the presence or absence of symmetry and synchrony. We illustrate the merit of our approach by presenting a real-world case study where the cytotoxic action of the anticancer drug topotecan was determined.
  • Barbara Szomolay (Warwick)
    Wednesday 11 Feb 2015
    Systems approaches in T cell immunity
    The interaction between T cell receptors (TCRs) and peptides is highly degenerate: a single TCR may recognize about one million different peptides in the context of a single MHCI molecule. On the other hand, TCR recognition is fundamentally peptide- and/or MHC-specific: the functional sensitivity, which can be viewed as experimental realisation of the TCR triggering rate, is large enough only for minute fraction of all possible ligands. TCR triggering rate and degeneracy are mathematical concepts that will be discussed in relation to McKeithan's kinetic proofreading model, which was extended to take into account the interaction between the co-receptor CD8 and MHCI molecule. In the rest of the talk I will outline an approach that uses length-matched combinatorial peptide library scan data to search protein databases and rank peptides in order of likelihood recognition. This CPL-based database screening can, to a large extent, accurately identify the pathogen that triggered the CD8 T cell.
  • 29 Oct 2014: Kevin Burrage (Oxford)
    From cells to tissue: modelling the electrophysiology of the human heart
  • 12 Nov 2014: Richard Blythe (Edinburgh)
    The evolution of combinatorial communication by natural selection
  • 19 Nov 2014: Tibor Antal (Edinburgh)
    Multi-type branching processes: from bacteria to cancer
  • 26 Nov 2014: Samir Suweis (Padova)
    From Patterns to Principles: Statistical Physics of Ecological Networks
  • 3 Dec 2014: Ulrich Dobramysl (Oxford)
    Environmental vs demographic variability in two-species predator-prey models
  • 21 May 2014: Francesca Buffa (Oxford) Bioinformatics Approaches to Biomarker Discovery in Cancer Research
    Genomic biomarker studies are generating an unprecedented amount of data and information in large clinical cohorts of individuals with cancer. The aim has been to identify specific genomic profiles associated with the aggressiveness of the disease - so called prognostic biomarkers - and profiles associated with the benefit from specific treatments - referred to as predictive biomarkers. Different study design and approaches have been exploited to generate, optimize and validated prognostic and predictive genomic profiles or signatures. I will illustrate some of the major studies and the approaches that have been adopted, and I will discuss the challenges that we still need to address in order to translate these potential biomarkers into useful clinical tools.
  • 7 May 2014: Radek Erban (Oxford) Mathematical Methods for Multiscale Modelling in Molecular, Cell and Population Biology I will discuss methods for spatio-temporal modelling in molecular, cell and population biology. Three classes of models will be considered:
    1. microscopic (molecular-based, individual-based) models which are based on the simulation of trajectories of molecules (individuals) and their localized interactions (for example, reactions);
    2. mesoscopic (lattice-based) models which divide the computational domain into a finite number of compartments and simulate the time evolution of the numbers of molecules (numbers of individuals) in each compartment; and
    3. macroscopic (deterministic) models which are written in terms of mean-field reaction-diffusion-advection partial differential equations (PDEs) for spatially varying concentrations.
    In the first part of my talk, I will discuss connections between the modelling frameworks. I will consider chemical reactions both at a surface and in the bulk. In the second part of my talk, I will present hybrid (multiscale) algorithms which use models with a different level of detail in different parts of the computational domain.
    The main goal of this multiscale methodology is to use a detailed modelling approach in localized regions of particular interest (in which accuracy and microscopic detail is important) and a less detailed model in other regions in which accuracy may be traded for simulation efficiency. I will also discuss hybrid modelling of chemotaxis where an individual-based model of cells is coupled with PDEs for extracellular chemical signals.
  • 30 April 2014: M Carmen Romano (Aberdeen) Reduce, reuse, recycle: modelling of ribosome recycling in S. cerevisiae
  • 2 April 2014: Oreste Acuto (Oxford)
    THEMIS, a novel signaling rheostat in T cell development and function
    THEMIS (Thymocyte-expressed molecule involved in selection) is a T cell lineage-specific protein expressed at the highest levels in DP thymocytes and decreasing in SP thymocytes and mature T cells. THEMIS-deficient mice have severely reduced numbers of CD4 or CD8 single-positive (SP) thymocytes (1), yet development of TCR(γ δ+) intra-epithelial lymphocytes (IEL), TCR(αβ+) CD8(α α+) IELs, liver iNKT-cells and Tregs appears intact (3), suggesting that T(conv) are most affected. Upon TCR ligation THEMIS associates, via the adaptor GRB2, with the scaffolding protein LAT at the immunological synapse and becomes rapidly tyrosine-phosphorylated. Thus, THEMIS is a TCR-proximal signaling component required for Tconv development and may also be important for their function in peripheral lymphoid organs. However, THEMIS molecular function has remained elusive.
    We found that in thymocytes and T(conv) THEMIS formed a constitutive complex with SHP tyrosine phosphatases, apparently also dependent on GRB2 . Consistently, THEMIS-deficiency caused increased TCR-induced proximal signaling and augmented apoptotic cell death in DP thymocytes and T cells. Likewise, knockdown of SHP-1 expression augmented T cell activation and apoptosis. Moreover, Bim deficiency rescued the development of THEMIS-deficient SP thymocytes. Further data on the THEMIS:SHP complex topology, its regulation and the mechanism of apoptosis induction will be presented.
    Comprehensively, our in vitro and in vivo studies reveal that THEMIS-deficiency causes an altered perception of signal strength by the TCR signaling machinery, leading to inappropriate apoptosis. Thus, THEMIS:SHP complex enacts a negative feedback loop that modulates early TCR signaling, helping set the threshold between life and death during selection of DP thymocytes and mature Tconv activation. Such a device may adjust TCR signal input from a diverse, quasi-continuum, repertoire of affinities and ligand abundance to a ?customary? output to attain stereotypical/highly reproducible gene expression and reduce the occurrence of undesired responses (e.g., cell death; lineage deviation).
  • 26 March 2014: Susan Short (Leeds) Modelling whole body radiation dose using DNA damage in lymphocytes
    All radiotherapy exposes patients to dose beyond the intended tumour target. More modern radiotherapy delivery methods deliver more conformal dose to the tumour but may as a consequence expose the patients to higher whole body doses, which may in turn lead to increased risk of radiation induced cancer. Measuring whole body dose during treatment is not trivial and we have approached this by measuring DNA damage in nuclei of circulating lymphocytes from patients while they undergo treatment. These data clearly demonstrate the whole body exposure during treatment and suggest that this blood based measurement can give a read-out that could be used to compare different radiotherapy delivery techniques from the perspective of radiation risk. In order to improve the applicability of this assay we have explored methods to model dose distribution in the blood volume more accurately and measure the low dose scatter component separately.
  • 12 March 2014: Michael Shapiro (Tufts, Boston)
    Epidemics on networks
    The classical epidemic model of Kermack and McKendrick treats epidemics as spreading in a world in which every person is equally likely to infect any other person. In fact, this model arises as the limiting case of a network model where the graph in question is the full graph. More recent modeling has focused on the more general and perhaps more realistic case where the graph reflects social contacts between individuals. A simple question arises: Is the expected size of an epidemic monotone in the underlying data? That is, if I add links, raise transmission probabilities or increase the initial set of infectives, can the expected size of the resulting epidemic decrease? Surprisingly, the answer depends on the model. Since a realistic class of models does not exhibit this sort of monotonicity, we are left wondering if it ever fails in the real world and if not, why not
  • 26 Feb 2014: Mario Castro Ponce (Madrid)
    A mathematical approach to dengue.
    Dengue is a highly aggressive disease that has attracted a lot of interest in recent years. The progression of the disease at a population level has traditional epidemiological models for the infected-susceptible contacts. However, little is known about the interaction between the dengue virus and the host immune system, in particular the role of antibodies in the observed higher virulence after reinfections. In this talk I will address this second problem by means of stochastic equations.
  • 5 Mar 2014: Sergei Krivov (Leeds)
    Understanding complex dynamics
    Biological systems exhibit complex dynamics on many scales, with prominent examples being the dynamics of protein folding, enzyme catalysis, molecular motors, gene regulatory networks, diseases and population. One popular way to to describe such processes is to project the multidimensional dynamics on a few (single) collective variables. During such dimensionality reduction one loses information; hence the variables should be optimally selected to preserve the information of interest, in particular, the information about the dynamics. I will present one such approach, where a coordinate is optimized by explicitly considering the system dynamics, as well as its applications to the analysis of state of the art simulations of protein folding and the dynamics of recovery after kidney transplant. I will conclude by presenting a recently suggested new class of optimal coordinates - additive eigenvectors.
  • 27 Nov 2013 Jamie Wood (York)
    Modelling in respiration in Niesseria meningitidis and other abuses of Bayesian Parameter fitting
    I will present a recent venture into system biology, aiming to model the respiratory function of the well known pathogen, Niesseria Meningitidis, the primary causal agent of bacterial meningititis. A mathematical model is proposed and then fitted to empirical data by a unusual interweaving of empirical and mathematical tests. We seek to find a robust parametrisation of the biological system where there is good genetic control but limited previous knowledge of parameters; a situation is not uncommon in systems biology. I will then discuss some work relating to the use of the Asymmetric exclusion process to get a better explanation of the design of the respiratory pathways, and ultimately where I got stuck. Finally I will explain how this relates naval battles in the first world war.
  • 20 Nov 2013: Alex Ramsey (Thomson Ecology)
    Failure is not an option-maintaining the ecological fabric in a changing world
    Ongoing development means that species and habitats are increasingly under pressure globally. Legislation drives most ecological work in the UK with a particular focus on certain species such as great crested newts and bats. The role of an ecological consultancy is often challenging, maintaining ecological diversity whilst providing solutions to a client which are effective and compliant with legislation, but always trying to maximise diversity on site whilst maintaining the integrity and resilience of habitats, and many of these challenges are applicable on a global scale. A series of case studies are included to demonstrate the range of issues faced by ecologists.
  • 23 Oct 2013: Alexei Zaikin (UCL)
    Intracellular genetic intelligency and effect of stochasticity
    I will discuss results of theoretical modelling in very multi-disciplinary area between Systems Medicine, Synthetic Biology, Artificial Intelligence and Applied Mathematics. Multicellular systems, e.g. neural networks of a living brain, can learn and be intelligent. Some of the principles of this intelligence have been mathematically formulated in the study of Artificial Intelligence (AI), starting from the basic Rosenblatt's and associative Hebbian perceptrons and resulting in modern artificial neural networks with multilayer structure and recurrence. In some sense AI has mimicked the function of natural neural networks. However, relatively simple systems as cells are also able to perform tasks such as decision making and learning by utilizing their genetic regulatory frameworks. Intracellular genetic networks can be more intelligent than was first assumed due to their ability to learn. Such learning includes classification of several inputs or, the manifestations of this intelligence is the ability to learn associations of two stimuli within gene regulating circuitry: Hebbian type learning within the cellular life. However, gene expression is an intrinsically noisy process, hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. During the talk I will also include brief introductions/tutorials about Synthetic Biology, modelling of genetic networks and noise-induced ordering.
  • 9 Oct 2013: Edwin Hawkins (Imperial)
    Construction and application of the Cyton Model: A stochastic model of lymphocyte proliferation, survival and differentiation
    Lymphocytes, the principal agents of adaptive immunity, undergo a typical pattern of response following stimulation in vivo: the cells proliferate, differentiate to effector cells, cease dividing and predominantly die, leaving behind a small proportion of long-lived memory and effector cells. Understanding the underlying processes that regulate this response is important for developing interventions to enhance immunity in immunocompromised individuals, or restrict the response in auto-immune patients. Using reductionist in vitro experimental methods, we have dissected the components of proliferation, survival and differentiation and how they interact to generate this response. Here, the population division tracking and single cell video microscopy experiments used to construct this model will be described. Our results demonstrate that a simple stochastic model of simultaneous competing processes, the Cyton model, provides an accurate tool for quantifying the lymphocyte response. Additionally, the applications of the Cyton model will be demonstrated. Specifically, how intrinsic genetic factors or changes to the external environment can regulate B lymphocyte fate. Furthermore, we will describe use of the Cyton model to identify novel therapeutic interventions for treatment of B lymphocyte driven autoimmune diseases.
  • 17 Sep 2013. Susan Holechek
    Bridging the gap: from experimental immunology to epidemiological models of chronic viral infections
  • 20 May 2013. Niyaz Ahmed (Hyderabad)
    Pathogen genomics, evolution and survival mechanisms: lessons from the two age-old pestilences
    With the advances in massively parallel, Next-Generation Sequencing (NGS) technologies, the scientific community is confronted by the challenge of data handling, management and heralding sustainable and testable ideas out of the genomic information. The NGS platforms not only help in real-time decipherment of various pathways, but also hold potentials for the maturation of OMICS sciences as a whole. We are involved with sequencing the genomes of pathogenic species and strains of Escherichia coli, Helicobacter pylori, Mycobacterium tuberculosis, Salmonella enterica, and Vibrio parahaemolyticus etc. for some time and these genomes are analyzed for dissecting various survival mechanisms based on new genes and functions. Our comparative genomic analyses, over the years, helped us identify several putative virulence encoding genes in the two major human pathogens, Mycobacterium tuberculosis and Helicobacter pylori. Some of these virulence factors possibly play crucial roles relevant in chronic persistence of these bacteria and provide them with survival advantages. We analyzed the signaling pathways pertaining to proapoptotic and or proinflammatory behavior of certain virulence factors from H. pylori and pathogenic mycobacteria. In the former organism, many of such genes are encoded by the ?plasticity region cluster? of the genome and we looked at functions of those proteins, from the cluster, which were predicted to be proinflammatory and/or proapoptotic. In M. tuberculosis, we studied novel genes/proteins that have predicted ribosome binding and thereby regulatory function as well as innate immune functions. We envision that such unrelated, yet finely orchestrated functional characteristics could be significant in controlling or optimizing the pathogen metabolic processes during infection, thereby ensuring long term survival of bacteria, in a dormant form. Better insights in to the mechanistic aspects of dormancy survival and or adaptive colonization would facilitate improved understanding of the blooms and boundaries of bacterial parasitism.
  • 8 May 2013: Walter Gregory (Leeds)
    Modelling residual disease volume and re-growth rates from response durations to aid in cancer trial design, interpretation and analysis
  • 24 April 2013 Thomas House (Warwick)
    Network epidemiology and its discontents
  • 10 April 2013: Frank Hilker (Bath)
  • 27 March 2013:. Sean Lawler (Leeds)
    Glioblastoma; a moving target
  • Wed 6 Mar 2013. Jean G Sathish, Dept of Molecular and Clinical Pharmacology and MRC Centre for Drug Safety Science, University of Liverpool
    Regulation of dendritic cell function by the transcription factor, Nrf2
    Dendritic cells (DCs) are key immune cells that are central to the initiation of adaptive immune responses. There are considerable ongoing efforts directed towards identifying new molecular pathways in DCs that could represent targets for pharmacological intervention in immune diseases. Redox homeostasis is important for a variety of cellular functions such as proliferation, apoptosis and intracellular signalling pathways such as the MAPK pathway. Nrf2 is a redox responsive transcription factor that has been implicated in the regulation of DC immune function. The transcriptional activity of Nrf2 contributes to cellular adaptation to oxidative and chemical stress. In this talk, Dr Jean Sathish will discuss findings from his research group on the role of Nrf2 in DC immune function and the intracellular signalling pathways that are subject to modulation by Nrf2 activity.
  • Mon 11 Feb 2013. 12 noon in MALL 2.
    Iren Bains. Immune Cell Biology, National Institute for Medical Research
    Emergence of the Helper: Cytotoxic (CD4: CD8) T cell ratio: using mathematical modelling to expose asymmetries in thymic development
  • 21 November Uwe Tauber (Virginia Tech)
    Stochastic predator-prey models: population oscillations, spatial correlations, and the effect of randomised rates"
  • 31 October Dipankar Nandi
    (Department of Biochemistry, Indian Institute of Science, Bangalore)
    Infection-induced thymic atrophy: studies on the death of CD4+CD8+ thymocytes during Salmonella enterica serovar Typhimurium Infection
  • 24 October Dr Ceri Williams
    Director of Operations of the Medical Technologies Innovation and Knowledge Centre
    Bridging the Technology Innovation Gap
  • 17 October Garrit Jentsch (AstraZeneca) Mathematical Modelling in Drug Discovery
  • 10 Oct. Domingo Salazar (Syngenta)
    A few examples of mathematical modeling in Syngenta, a world-leading plant science company.
  • 23 May 2012 Chris Greenman (University of East Anglia)
    Resolving Cancer Genomes with Next Generating Sequencing
  • 9 May 2012 Marcus Tindall (University of Reading)
    Understanding cholesterol regulation: An evolving story
  • 25 April 2012 Reidun Twarock (University of York)
    Viruses and Geometry - Where Symmetry meets Function
  • 11 April 2012: Sandro Azaele (University of Leeds)
    A spatially explicit model for linking ecological patterns
  • 21 March 2012 Andrew Teschendorff (University College London)
    Overcoming statistical challenges arising in epigenome-wide cancer studies
  • 30 November 2011 Alison Etheridge (Oxford)
    Modelling evolution in a spatial continuum
  • 23 November 2011 Stephen Webb (Glasgow)
    Computational modelling of cell migration and chemotaxis
  • 16 November 2011 Carlo Berzuini (Cambridge)
    Causal inference in genetic epidemiology: looking into mechanism
  • 19 October 2011 Thomas Fink (Cambridge)
    The relation between robustness, adaptability, and fitness
  • 18 May 2011 Jon Pitchford (York)
    Evolving stochastic strategies: analytical, statistical and computational approaches
  • Darren Wilkinson (Newcastle) 8 December 2010
    Modelling and learning for noisy cellular decisions: motility of Bacillus subtilis
  • David Westhead (Leeds) 1 December 2010
    Metabolic networks, horizontal gene transfer and evolution
  • Wally Gilks 24 November 2010
    (Statistical Genomics Group, Rothamsted Research and Department of Statistics, University of Leeds)
  • Jorge Carneiro (Instituto Gulbenkian) 10 November 2010
    Multiscale modelling of regulatory T cells and immunological tolerance
  • Christian Yates (Oxford) 27 October 2010
    United by noise: Randomness helps swarms stay together