Forthcoming events in this series


Fri, 23 May 2025

11:00 - 12:00
L2

Modelling infectious diseases within-host

Dr Ruth Bowness
(Dept. Maths Science, University of Bath)
Abstract

During the talk I will describe my research on host-pathogen interactions during lung infections. Various modelling approaches have been used, including a hybrid multiscale individual-based model that we have developed, which simulates pulmonary infection spread, immune response and treatment within in a section of human lung. The model contains discrete agents which model the spatio-temporal interactions (migration, binding, killing etc.) of the pathogen and immune cells. Cytokine and oxygen dynamics are also included, as well as Pharmacokinetic/Pharmacodynamic models, which are incorporated via PDEs. I will also describe ongoing work to develop a continuum model, comparing the spatial dynamics resulting from these different modelling approaches.  I will focus in the most part on two infectious diseases: Tuberculosis and COVID-19.

Fri, 16 May 2025

11:00 - 12:00
L4

Round the clock: circadian gene expression, growth and division in cyanobacteria

Dr Bruno Martins
(School of Life Sciences, University of Warwick)
Abstract

Circadian clocks generate autonomous daily rhythms of gene expression in anticipation of daily sunlight and temperature cycles in a variety of organisms. The simples and best characterised of all circadian clocks in nature is the cyanobacterial clock, the core of which consists of just 3 proteins - KaiA, KaiB and KaiC - locked in a 24-h phosphorylation-dephosphorylation loop. Substantial progress has been made in understanding how cells generate and sustain this rhythm, but important questions remain: how does the clock maintain resilience in the face of internal and external fluctuations, how is the clock coupled to other cellular processes and what dynamics arise from this coupling? We address these questions using an interdisciplinary approach combining time-lapse microscopy and modelling. In this talk, I will first characterise the clock's free-running robustness and explore how the clock buffers environmental noise and genetic mutations. Our stochastic model predicts how the clock filters out such noise, including fast light fluctuations, to keep time while remaining responsive to environmental shifts, revealing also that the wild-type operates at a noise optimum. Next, I will focus on how the clock interacts with the other major cellular cycle, the cell division cycle. Our single-cell data shows that the clock couples to the division rate and expression of cell cycle-dependent factors using both frequency modulation and amplitude modulation strategies, with implications for cell growth and cell size control. Our findings illustrate how simple systems can exhibit complex dynamics, advancing our understanding of the interdependency between gene circuits and cellular physiology.  
 

Fri, 09 May 2025

11:00 - 12:00
L4

5 years after COVID: what did modellers get right and wrong?

Professor Matt Keeling
(Dept of Mathematics University of Warwick)
Abstract
The COVID-19 pandemic represented a major challenge to many sectors of society. It also provided the opportunity for epidemiological modellers to prove their worth. Much of the modelling was performed to extremely tight deadlines and was underpinned by noisy and often biased data. 
5 years on, and with the benefit of hindsight, I’ll present a personal perspective of what went well, what went badly and lessons for next time. I’ll cover many aspects, but pay particular attention to vaccination, roadmaps, Omicron and building collaborative networks. 


 

Fri, 02 May 2025

14:00 - 15:00
L3

Some theoretical results about responses to inputs and transients in systems biology

Prof Eduardo Sontag
(Departments of Electrical and Computer Engineering and of Bioengineering Northeastern University )
Abstract

This talk will focus on systems-theoretic and control theory tools that help characterize the responses of nonlinear systems to external inputs, with an emphasis on how network structure “motifs” introduce constraints on finite-time, transient behaviors.  Of interest are qualitative features that are unique to nonlinear systems, such as non-harmonic responses to periodic inputs or the invariance to input symmetries. These properties play a key role as tools for model discrimination and reverse engineering in systems biology, as well as in characterizing robustness to disturbances. Our research has been largely motivated by biological problems at all scales, from the molecular (e.g., extracellular ligands affecting signaling and gene networks), to cell populations (e.g., resistance to chemotherapy due to systemic interactions between the immune system and tumors; drug-induced mutations; sensed external molecules triggering activations of specific neurons in worms), to interactions of individuals (e.g., periodic or single-shot non-pharmaceutical “social distancing'” interventions for epidemic control). Subject to time constraints, we'll briefly discuss some of these applications.

Fri, 02 May 2025

11:00 - 12:00
L4

Do the shapes of tumour cell nuclei influence their infiltration?

Professor Karthik Bharath
(School of Mathematical Sciences University of Nottingham)
Abstract

The question can be formulated as a statistical hypothesis asserting that the distribution of the shapes of closed curves representing outlines of cell nuclei in a spatial domain is independent of the distribution of their locations. The key challenge in developing a procedure to test the hypothesis from a sample of spatially indexed curves (e.g. from an image) lies in how symmetries in the data are accounted for: shape of a curve is a property that is invariant to similarity transformations and reparameterization, and the shape space is thus an infinite-dimensional quotient space. Starting with a convenient geometry for the shape space developed over the last few years, I will discuss dependence measures and their estimates for spatial point processes with shape-valued marks, and demonstrate their use in testing for spatial independence of marks in a breast cancer application.  

Fri, 14 Mar 2025

11:00 - 12:00
L4

Hierarchical inference for more mechanistic functional response models using machine learning

Prof Ben Lambert
(Dept of Statistics, University of Oxford)
Abstract

Consumer-resource interactions are central to ecology, as all organisms rely on consuming resources to survive. Functional responses describe how a consumer's feeding rate changes with resource availability, influenced by processes like searching for, capturing, and handling resources. To study functional responses, experiments typically measure the amount of food consumed—often in discrete units like prey—over a set time. These experiments systematically vary prey availability to observe how it affects the consumer's feeding behaviour. The data generated by such experiments are often analysed using differential equation-based models. Here, we argue that such models do not represent a realistic data-generating process for many such experiments and propose an alternative stochastic individual-based model. This class of models, however, is expensive for inference, and we use machine learning methods to expedite fitting these models to data. We then use our method to do generalised linear model-based inference for a series of experiments conducted on a stickleback fish. Our methodology is made available to others in a Python package for Bayesian hierarchical inference for stochastic, individual-based models of functional responses.

 

Fri, 07 Mar 2025

11:00 - 12:00
L4

Nonlocal advection-diffusion for modelling organism space use and movement

Prof Jonathan Potts
(Department of Computer Science The University of Sheffield)
Abstract

How do mobile organisms situate themselves in space?  This is a fundamental question in both ecology and cell biology but, since space use is an emergent feature of movement processes operating on small spatio-temporal scales, it requires a mathematical approach to answer.  In recent years, increasing empirical research has shown that non-locality is a key aspect of movement processes, whilst mathematical models have demonstrated its importance for understanding emergent space use patterns.  In this talk, I will describe a broad class of models for modelling the space use of interacting populations, whereby directed movement is in the form of non-local advection.  I will detail various methods for ascertaining pattern formation properties of these models, fundamental for answering the question of how organisms situate themselves in space, and describe some of the rich variety of patterns that emerge. I will also explain how to connect these models to data on animal and cellular movement.

Fri, 28 Feb 2025

11:00 - 12:00
L4

Machine learning interatomic potentials to dynamics-preserving coarse-graining strategies

Dr Matthias Sachs
(Department of Mathematics University of Birmingham)
Abstract

Recent progress in the development of equivariant neural network architectures predominantly used for machine learning interatomic potentials (MLIPs) has opened new possibilities in the development of data-driven coarse-graining strategies. In this talk, I will first present our work on the development of learning potential energy surfaces and other physical quantities, namely the Hyperactive Learning framework[1], a Bayesian active learning strategy for automatic efficient assembly of training data in MLIP and ACEfriction [2], a framework for equivariant model construction based on the Atomic Cluster Expansion (ACE) for learning of configuration-dependent friction tensors in the dynamic equations of molecule surface interactions and Dissipative Particle Dynamics (DPD). In the second part of my talk, I will provide an overview of our work on the simulation and analysis of Generalized Langevin Equations [3,4] as obtained from systematic coarse-graining of Hamiltonian Systems via a Mori-Zwanzig projection and present an outlook on our ongoing work on developing data-driven approaches for the construction of dynamics-preserving coarse-grained representations.

References:

[1] van der Oord, C., Sachs, M., Kovács, D.P., Ortner, C. and Csányi, G., 2023. Hyperactive learning for data-driven interatomic potentials. npj Computational Materials

[2] Sachs, M., Stark, W.G., Maurer, R.J. and Ortner, C., 2024. Equivariant Representation of Configuration-Dependent Friction Tensors in Langevin Heatbaths. to appear in Machine Learning: Science & Technology

[3] Leimkuhler, B. and Sachs, M., 2022. Efficient numerical algorithms for the generalized Langevin equation. SIAM Journal on Scientific Computing

[4] Leimkuhler, B. and Sachs, M., 2019. Ergodic properties of quasi-Markovian generalized Langevin equations with configuration-dependent noise and non-conservative force. In Stochastic Dynamics Out of Equilibrium: Institut Henri Poincaré, 2017 

 

 

Fri, 21 Feb 2025

11:00 - 12:00
L4

Epithelial-mesenchymal plasticity at scale: AI-powered insights from single cells and spatial transcriptomics

Prof Maria Secrier
(Department of Genetics, Evolution and Environment University College London)
Abstract

The epithelial to mesenchymal transition (EMT) is a key cellular process underlying cancer progression, with multiple intermediate states whose molecular hallmarks remain poorly characterized. In this talk, I will describe AI-powered and ecology-inspired methods recently developed by us to provide a multi-scale view of the epithelial-mesenchymal plasticity in cancer from single cell and spatial transcriptomics data. First, we employed a large language model similar to the one underlying chatGPT but tailored for biological data (inspired by scBERT methodology), to predict individual stable states within the EMT continuum in single cell data and dissect the regulatory processes governing these states. Secondly, we leveraged spatial transcriptomics of breast cancer tissue to delineate the spatial relationships between cancer cells occupying distinct states within the EMT continuum and various hallmarks of the tumour microenvironment. We introduce a new tool, SpottedPy, that identifies tumour hotspots within spatial transcriptomics slides displaying enrichment in processes of interest, including EMT, and explores the distance between these hotspots and immune/stromal-rich regions within the broader environment at flexible scales. We use this method to delineate an immune evasive quasi-mesenchymal niche that could be targeted for therapeutic benefit. Our insights may inform strategies to counter immune evasion enabled by EMT and offer an expanded view of the coupling between EMT and microenvironmental plasticity in breast cancer.

Fri, 14 Feb 2025

11:00 - 12:00
L4

Computational investigation of single-scale and multi-scale heterogeneous immune responses during cancer evolution

Prof Raluca Eftimie
(Mathematics Laboratory Université de Franche-Comté, Besançon)
Abstract

Tumour microenvironment is characterised by heterogeneity at various scales: from various cell populations (immune cells, cancerous cells, ...) and various molecules that populate the microenvironment (cytokines, chemokines, extracellular vesicles, …); to phenotype heterogeneity inside the same cell population (e.g., immune cells with different phenotypes and different functions); as well as temporal heterogeneity in cells’ phenotypes (as cancer evolves through time) and spatial heterogeneity.
In this talk we overview some mathematical models and computational approaches developed to investigate different single-scale and multi-scale aspects related to heterogeneous immune responses during cancer evolution. Throughout the talk we emphasise the qualitative vs. quantitative results, and data availability across different scales

Fri, 07 Feb 2025

11:00 - 12:00
L4

Self-organized patterning in complex biological fluids

Dr Giulia Celora
(Mathematical Institute University of Oxford)
Abstract

Understanding how living systems dynamically self-organise across spatial and temporal scales is a fundamental problem in biology; from the study of embryo development to regulation of cellular physiology. In this talk, I will discuss how we can use mathematical modelling to uncover the role of microscale physical interactions in cellular self-organisation. I will illustrate this by presenting two seemingly unrelated problems: environmental-driven compartmentalisation of the intracellular space; and self-organisation during collective migration of multicellular communities. Our results reveal hidden connections between these two processes hinting at the general role that chemical regulation of physical interactions plays in controlling self-organisation across scales in living matter

Fri, 31 Jan 2025

11:00 - 12:00
L4

Adventures in Mathematical Biology

Dr Kit Yates
(Dept of Mathematical Sciences Bath University)
Abstract

In this talk I will give a number of short vignettes of work that has been undertaken in my group over the last 15 years. Mathematically, the theme that underlies our work is the importance of randomness to biological systems. I will explore a number of systems for which randomness plays a critical role. Models of these systems which ignore this important feature do a poor job of replicating the known biology, which in turn limits their predictive power. The underlying biological theme of the majority our work is development, but the tools and techniques we have built can be applied to multiple biological systems and indeed further afield. Topics will be drawn from, locust migration, zebrafish pigment pattern formation, mammalian cell migratory defects, appropriate cell cycle modelling and more. I won't delve to deeply into anyone area, but am happy to take question or to expand upon of the areas I touch on.

Fri, 24 Jan 2025

11:00 - 12:00
L4

Combining computational modelling, deep generative learning and imaging to infer new biology

Prof Simon Walker-Samuel
(Dept of Imaging, UCL)
Abstract

Deep learning algorithms provide unprecedented opportunities to characterise complex structure in large data, but typically in a manner that cannot easily be interpreted beyond the 'black box'. We are developing methods to leverage the benefits of deep generative learning and computational modelling (e.g. fluid dynamics, solid mechanics, biochemistry), particularly in conjunction with biomedical imaging, to enable new insights into disease to be made. In this talk, I will describe our applications in several areas, including modelling drug delivery in cancer and retinal blood vessel loss in diabetes, and how this is leading us into the development of personalised digital twins.

Fri, 17 Jan 2025

11:00 - 12:00
L3

Do individuals matter? - From psychology, via wound healing and calcium signalling to ecology

Dr Ivo Siekmann
(School of Computer Science and Mathematics, Liverpool University)
Abstract
Should models in mathematical biology be based on detailed representations of individuals - biomolecules, cells, individual members of a population or agents in a social system? Or, alternatively, should individuals be described as identical members of a population, neglecting inter-individual differences? I will explore this question using recent examples from my own research.
 
In the beginning of my presentation I will ask you how you are feeling. Evaluating your answers, I will show how differences in personality can be represented in a model based on differential equations. I will then present an individual-based cell migration model based on the Ornstein-Uhlenbeck process that can help to design textured surfaces that enhance wound healing. In ecosystems, organisms that make decisions based on studying their environment such as fish might interact with populations that are unable of complex behaviour such as plankton. I will explain how piecewise-deterministic Markov (PDMP) models can be used for representing some populations as individuals and others as populations. PDMPs can also be used for modelling how interacting calcium channels generate calcium signals in cells. Finally, I will present a reaction-diffusion model of the photosynthetic activity of phytoplankton that explains how oxygen minimum zones emerge in the ocean.
Fri, 06 Dec 2024

11:00 - 12:00
L5

Spatial mechano-transcriptomics of mouse embryogenesis

Prof Adrien Hallou
(Dept of Physics University of Oxford)
Abstract

Advances in spatial profiling technologies are providing insights into how molecular programs are influenced by local signalling and environmental cues. However, cell fate specification and tissue patterning involve the interplay of biochemical and mechanical feedback. Here, we propose a new computational framework that enables the joint statistical analysis of transcriptional and mechanical signals in the context of spatial transcriptomics. To illustrate the application and utility of the approach, we use spatial transcriptomics data from the developing mouse embryo to infer the forces acting on individual cells, and use these results to identify mechanical, morphometric, and gene expression signatures that are predictive of tissue compartment boundaries. In addition, we use geoadditive structural equation modelling to identify gene modules that predict the mechanical behaviour of cells in an unbiased manner. This computational framework is easily generalized to other spatial profiling contexts, providing a generic scheme for exploring the interplay of biomolecular and mechanical cues in tissues.

Fri, 29 Nov 2024

11:00 - 12:00
L5

Algebraic approaches in the study of chemical reaction networks

Dr Murad Banaji
(Mathematical Institute University of Oxford)
Abstract

Underlying many biological models are chemical reaction networks (CRNs), and identifying allowed and forbidden dynamics in reaction networks may 
give insight into biological mechanisms. Algebraic approaches have been important in several recent developments. For example, computational 
algebra has helped us characterise all small mass action CRNs admitting certain bifurcations; allowed us to find interesting and surprising 
examples and counterexamples; and suggested a number of conjectures.   Progress generally involves an interaction between analysis and 
computation: on the one hand, theorems which recast apparently difficult questions about dynamics as (relatively tractable) algebraic problems; 
and on the other, computations which provide insight into deeper theoretical questions. I'll present some results, examples, and open 
questions, focussing on two domains of CRN theory: the study of local bifurcations, and the study of multistationarity.

Fri, 22 Nov 2024

11:00 - 12:00
L5

Bifurcations, pattern formation and multi-stability in non-local models of interacting species

Dr Valeria Giunta
( Dept of Maths Swansea University)
Abstract

Understanding the mechanisms behind the spatial distribution, self-organisation and aggregation of organisms is a central issue in both ecology and cell biology. Since self-organisation at the population level is the cumulative effect of behaviours at the individual level, it requires a mathematical approach to be elucidated.
In nature, every individual, be it a cell or an animal, inspects its territory before moving. The process of acquiring information from the environment is typically non-local, i.e. individuals have the ability to inspect a portion of their territory. In recent years, a growing body of empirical research has shown that non-locality is a key aspect of movement processes, while mathematical models incorporating non-local interactions have received increasing attention for their ability to accurately describe how interactions between individuals and their environment can affect their movement, reproduction rate and well-being. In this talk, I will present a study of a class of advection-diffusion equations that model population movements generated by non-local species interactions. Using a combination of analytical and numerical tools, I will show that these models support a wide variety of spatio-temporal patterns that are able to reproduce segregation, aggregation and time-periodic behaviours commonly observed in real systems. I will also show the existence of parameter regions where multiple stable solutions coexist and hysteresis phenomena.
Overall, I will describe various methods for analysing bifurcations and pattern formation properties of these models, which represent an essential mathematical tool for addressing fundamental questions about the many aggregation phenomena observed in nature.
 

Fri, 15 Nov 2024

11:00 - 12:00
L5

Lane formation and aggregation spots in foraging ant

Dr Maria Bruna
(Mathematical Institute University of Oxford)
Abstract

We consider a system of interacting particles as a model for a foraging ant colony, where each ant is represented as an active Brownian particle. The interactions among ants are mediated through chemotaxis, aligning their orientations with the upward gradient of a pheromone field. Unlike conventional models, our study introduces a parameter that enables the reproduction of two distinctive behaviours: the conventional Keller-Segel aggregation and the formation of travelling clusters without relying on external constraints such as food sources or nests. We consider the associated mean-field limit of this system and establish the analytical and numerical foundations for understanding these particle behaviours.

Fri, 08 Nov 2024

11:00 - 12:00
L5

Functional, neutral and selected heterogeneity in multi-cellular populations and human tissues

Dr David Tourigny
(School of Mathematics University of Birmingham)
Abstract
No biological system involves a single cell functioning in isolation. Almost all consist of highly connected networks of interacting individuals, which respond and adapt differently to signals and conditions within their local microenvironment. For example, human tissues and their cancers contain mosaics of genetic clones, and the transcriptomic and metabolic profiles from genetically identical cells are also highly heterogeneous. As the full extent of multi-cellular heterogeneity is revealed by recent experimental advances, computational and mathematical modelling can begin to provide a quantitative framework for understanding its biological implications. In this talk, I will describe some functional aspects of multi-cellular heterogeneity and explore the consequences for human health and disease.


 

Fri, 01 Nov 2024

11:00 - 12:00
L5

Applications of extreme statistics to cellular decision making and signaling

Prof Alan Lindsay
(Dept of Applied and Computational Maths University of Notre Dame)
Abstract

Cells must reliably coordinate responses to noisy external stimuli for proper functionality whether deciding where to move or initiate a response to threats. In this talk I will present a perspective on such cellular decision making problems with extreme statistics. The central premise is that when a single stochastic process exhibits large variability (unreliable), the extrema of multiple processes has a remarkably tight distribution (reliable). In this talk I will present some background on extreme statistics followed by two applications. The first regards antigen discrimination - the recognition by the T cell receptor of foreign antigen. The second concerns directional sensing - the process in which cells acquire a direction to move towards a target. In both cases, we find that extreme statistics explains how cells can make accurate and rapid decisions, and importantly, before any steady state is reached.

Fri, 25 Oct 2024

11:00 - 12:00
L5

Engineering Biology for Robust Turing Patterns

Prof Robert Endres
(Biological Physics Group Imperial College London)
Abstract

Turing patterns have long been proposed as a mechanism for spatial organization in biology, but their relevance remains controversial due to the stringent fine-tuning often required. In this talk, I will present recent efforts to engineer synthetic Turing systems in bacterial colonies, highlighting both successes and limitations. While our three-node gene circuit generates patterns, challenges remain in extending these results to broader contexts. Additionally, I will discuss our exploration of machine learning methods to address the inverse problem of pattern formation, helping the design process down the road. This work addresses the ongoing task in translating theory into robust biological applications, offering insights into both current capabilities and future directions.

Fri, 18 Oct 2024

11:00 - 12:00
L5

Novel multi-omics approaches to understand immune cell biology in health and disease

Prof Rachael Bashford-Rogers
(Dept of Biochemistry University of Oxford)
Abstract

Immunological health relies on a balance between the ability to mount an immune response against potential pathogens and tolerance to self. However, how we keep that balance in health and what goes wrong in disease is not well understood. Here, I will describe combination of novel experimental and computational approaches using multi-omics datasets, imaging and functional experiments to dissect the role and defects in immune cells across several disease areas in cancer and autoimmunity. We show how shared mechanisms that are disrupted across diseases, including cellular, migration, immuno-surveillance, regulation and activation, as well as the immunological features associated with better prognosis and immunomodulation.

Mon, 17 Jun 2024

11:00 - 12:00
L2

Mathematical modelling to support New Zealand’s Covid-19 response

Professor Mike Plank
(Dept of Mathematics & Statistics University of Canterbury)
Abstract

In this talk, I will describe some of the ways in which mathematical modelling contributed to the Covid-19 pandemic response in New Zealand. New Zealand adopted an elimination strategy at the beginning of the pandemic and used a combination of public health measures and border restrictions to keep incidence of Covid-19 low until high vaccination rates were achieved. The low or zero prevalence for first 18 months of the pandemic called for a different set of modelling tools compared to high-prevalence settings. It also generated some unique data that can give valuable insights into epidemiological characteristics and dynamics. As well as describing some of the modelling approaches used, I will reflect on the value modelling can add to decision making and some of the challenges and opportunities in working with stakeholders in government and public health.        

Fri, 14 Jun 2024

14:00 - 15:00
L3

Brain mechanics in the Data era

Prof Antoine Jerusalem
(Dept of Engineering Science University of Oxford)
Abstract

In this presentation, we will review how the field of Mechanics of Materials is generally framed and see how it can benefit from and be of benefit to the current progress in AI. We will approach this problematic in the particular context of Brain mechanics with an application to traumatic brain injury in police investigations. Finally we will briefly show how our group is currently applying the same methodology to a range of engineering challenges.

Fri, 07 Jun 2024

14:00 - 15:00
L3

Modeling the electromechanics of aerial electroreception

Dr Isaac Vikram Chenchiah
(School of Mathematics University of Bristol)
Abstract
Aerial electroreception is the ability of some arthropods (e.g., bees) to detect electric fields in the environment. I present an overview of our attempts to model the electromechanics of this recently discovered phenomenon and how it might contribute to the sensory biology of arthropods. This is joint work with Daniel Robert and Ryan Palmer.