Fri, 31 May 2024

14:00 - 15:00
L3

Cytoneme-mediated morphogenesis

Prof Paul Bressloff
(Dept of Mathematics Imperial College London)
Abstract

Morphogen protein gradients play an essential role in the spatial regulation of patterning during embryonic development.  The most commonly accepted mechanism of protein gradient formation involves the diffusion and degradation of morphogens from a localized source. Recently, an alternative mechanism has been proposed, which is based on cell-to-cell transport via thin, actin-rich cellular extensions known as cytonemes. It has been hypothesized that cytonemes find their targets via a random search process based on alternating periods of retraction and growth, perhaps mediated by some chemoattractant. This is an actin-based analog of the search-and-capture model of microtubules of the mitotic spindle searching for cytochrome binding sites (kinetochores) prior to separation of cytochrome pairs. In this talk, we introduce a search-and-capture model of cytoneme-based morphogenesis, in which nucleating cytonemes from a source cell dynamically grow and shrink until making contact with a target cell and delivering a burst of morphogen. We model the latter as a one-dimensional search process with stochastic resetting, finite returns times and refractory periods. We use a renewal method to calculate the splitting probabilities and conditional mean first passage times (MFPTs) for the cytoneme to be captured by a given target cell. We show how multiple rounds of search-and-capture, morphogen delivery, cytoneme retraction and nucleation events lead to the formation of a morphogen gradient. We proceed by formulating the morphogen bursting model as a queuing process, analogous to the study of translational bursting in gene networks. We end by briefly discussing current work on a model of cytoneme-mediated within-host viral spread.

Fri, 17 May 2024

14:00 - 15:00
L3

Some consequences of phenotypic heterogeneity in living active matter

Dr Philip Pearce
(Dept of Mathematics UCL)
Abstract

In this talk I will discuss how phenotypic heterogeneity affects emergent pattern formation in living active matter with chemical communication between cells. In doing so, I will explore how the emergent dynamics of multicellular communities are qualitatively different in comparison to the dynamics of isolated or non-interacting cells. I will focus on two specific projects. First, I will show how genetic regulation of chemical communication affects motility-induced phase separation in cell populations. Second, I will demonstrate how chemotaxis along self-generated signal gradients affects cell populations undergoing 3D morphogenesis.

Fri, 10 May 2024

14:00 - 15:00
L3

The determining role of cell adhesions for force transmission, mechanical activity and stiffness sensing in cells and tissues

Dr Carina Dunlop
(Dept of Mathematics University of Surrey)
Abstract

The role of tissue stiffness in controlling cell behaviours ranging from proliferation to signalling and activation is by now well accepted. A key focus of experimental studies into mechanotransduction are focal adhesions, localised patches of strong adhesion, where cell signalling has been established to occur. However, these adhesion sites themselves alter the mechanical equilibrium of the system determining the force balance and work done. To explore this I have developed an active matter continuum description of cellular contractility and will discuss recent results on the specific role of spatial positioning of adhesions in mechanotransduction. I show using energy arguments why the experimentally observed arrangements of focal adhesions develop and the implications this has for stiffness sensing and cellular contractility control. I will also show how adhesions play distinct roles in single cells and tissue layers respectively drawing on recent experimental work with Dr JR Davis (Manchester University) and Dr Nic Tapon (Crick Institute) with applications to epithelial layers and organoids.

Fri, 03 May 2024

14:00 - 15:00
L3

Epidemiological modelling with behavioural considerations and to inform policy making

Dr Edward Hill
(Dept of Mathematics University of Warwick)
Abstract
Many problems in epidemiology are impacted by behavioural dynamics, whilst in response to health emergencies prompt analysis and communication of findings is required to be of use to decision makers. Both instances are likely to benefit from interdisciplinary approaches. This talk will feature two examples, one with a public health focus and one with a veterinary health focus.
 
In the first part, I will summarise work originally conducted in late 2020 that was contributed to Scientific Pandemic Influenza Group on Modelling, Operational sub-group (SPI-M-O) of SAGE (Scientific Advisory Group for Emergencies) on Christmas household bubbles in England. This was carried out in response to a policy involving a planned easing of restrictions in England between 23–27 December 2020, with Christmas bubbles allowing people from up to three households to meet throughout the holiday period. Using a household model and computational simulation, we estimated the epidemiological impact of both this and alternative bubble strategies that allowed extending contacts beyond the immediate household.

(Associated paper: Modelling the epidemiological implications for SARS-CoV-2 of Christmas household bubbles in England in December 2020. https://doi.org/10.1016/j.jtbi.2022.111331)

In the second part, I will present a methodological pipeline developed to generate novel quantitative data on farmer beliefs with respect to disease management, process the data into a form amenable for use in mathematical models of livestock disease transmission and then refine said mathematical models according to the findings of the data. Such an approach is motivated by livestock disease models traditionally omitting variation in farmer disease management behaviours. I will discuss our application of this methodology for a fast, spatially spreading disease outbreak scenario amongst cattle herds in Great Britain, for which we elicited when farmers would use an available vaccine and then used the attained behavioural groups within a livestock disease model to make epidemiological and health economic assessments. 

(Associated paper: Incorporating heterogeneity in farmer disease control behaviour into a livestock disease transmission model. https://doi.org/10.1016/j.prevetmed.2023.106019)
Fri, 26 Apr 2024

14:00 - 15:00
L3

Polynomial dynamical systems and reaction networks: persistence and global attractors

Professor Gheorghe Craciun
(Department of Mathematics and Department of Biomolecular Chemistry, University of Wisconsin-Madison)
Abstract
The mathematical analysis of global properties of polynomial dynamical systems can be very challenging (for example: the second part of Hilbert’s 16th problem about polynomial dynamical systems in 2D, or the analysis of chaotic dynamics in the Lorenz system).
On the other hand, any dynamical system with polynomial right-hand side can essentially be regarded as a model of a reaction network. Key properties of reaction systems are closely related to fundamental results about global stability in classical thermodynamics. For example, the Global Attractor Conjecture can be regarded as a finite dimensional version of Boltzmann’s H-theorem. We will discuss some of these connections, as well as the introduction of toric differential inclusions as a tool for proving the Global Attractor Conjecture.
We will also discuss some implications for the more general Persistence Conjecture (which says that solutions of weakly reversible systems cannot "go extinct"), as well as some applications to biochemical mechanisms that implement cellular homeostasis. 
 


 

Thu, 29 Feb 2024
16:00
L3

Martingale Benamou-Brenier: arthimetic and geometric Bass martingales

Professor Jan Obloj
(Mathematical Institute)
Further Information

Please join us for refreshments outside L3 from 1530.

Abstract

Optimal transport (OT) proves to be a powerful tool for non-parametric calibration: it allows us to take a favourite (non-calibrated) model and project it onto the space of all calibrated (martingale) models. The dual side of the problem leads to an HJB equation and a numerical algorithm to solve the projection. However, in general, this process is costly and leads to spiky vol surfaces. We are interested in special cases where the projection can be obtained semi-analytically. This leads us to the martingale equivalent of the seminal fluid-dynamics interpretation of the optimal transport (OT) problem developed by Benamou and Brenier. Specifically, given marginals, we look for the martingale which is the closest to a given archetypical model. If our archetype is the arithmetic Brownian motion, this gives the stretched Brownian motion (or the Bass martingale), studied previously by Backhoff-Veraguas, Beiglbock, Huesmann and Kallblad (and many others). Here we consider the financially more pertinent case of Black-Scholes (geometric BM) reference and show it can also be solved explicitly. In both cases, fast numerical algorithms are available.

Based on joint works with Julio Backhoff, Benjamin Joseph and Gregoire Leoper.  

This talk reports a work in progress. It will be done on a board.

Thu, 07 Mar 2024
16:00
L3

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes

Dr Emilio Ferrucci
(Mathematical Institute University of Oxford)
Further Information

Please join us for refreshments outside L3 from 1530.

Abstract

Predicting real-world phenomena often requires an understanding of their causal relations, not just their statistical associations. I will begin this talk with a brief introduction to the field of causal inference in the classical case of structural causal models over directed acyclic graphs, and causal discovery for static variables. Introducing the temporal dimension results in several interesting complications which are not well handled by the classical framework. The main component of a constraint-based causal discovery procedure is a statistical hypothesis test of conditional independence (CI). We develop such a test for stochastic processes, by leveraging recent advances in signature kernels. Then, we develop constraint-based causal discovery algorithms for acyclic stochastic dynamical systems (allowing for loops) that leverage temporal information to recover the entire directed graph. Assuming faithfulness and a CI oracle, our algorithm is sound and complete. We demonstrate strictly superior performance of our proposed CI test compared to existing approaches on path-space when tested on synthetic data generated from SDEs, and discuss preliminary applications to finance. This talk is based on joint work with Georg Manten, Cecilia Casolo, Søren Wengel Mogensen, Cristopher Salvi and Niki Kilbertus: https://arxiv.org/abs/2402.18477 .

Thu, 22 Feb 2024

12:00 - 13:00
L3

Structural identifiability analysis: An important tool in systems modelling

Michael Chappell
(University of Warwick)
Abstract

For many systems (certainly those in biology, medicine and pharmacology) the mathematical models that are generated invariably include state variables that cannot be directly measured and associated model parameters, many of which may be unknown, and which also cannot be measured.  For such systems there is also often limited access for inputs or perturbations. These limitations can cause immense problems when investigating the existence of hidden pathways or attempting to estimate unknown parameters and this can severely hinder model validation. It is therefore highly desirable to have a formal approach to determine what additional inputs and/or measurements are necessary in order to reduce or remove these limitations and permit the derivation of models that can be used for practical purposes with greater confidence.

Structural identifiability arises in the inverse problem of inferring from the known, or assumed, properties of a biomedical or biological system a suitable model structure and estimates for the corresponding rate constants and other model parameters.  Structural identifiability analysis considers the uniqueness of the unknown model parameters from the input-output structure corresponding to proposed experiments to collect data for parameter estimation (under an assumption of the availability of continuous, noise-free observations).  This is an important, but often overlooked, theoretical prerequisite to experiment design, system identification and parameter estimation, since estimates for unidentifiable parameters are effectively meaningless.  If parameter estimates are to be used to inform about intervention or inhibition strategies, or other critical decisions, then it is essential that the parameters be uniquely identifiable. 

Numerous techniques for performing a structural identifiability analysis on linear parametric models exist and this is a well-understood topic.  In comparison, there are relatively few techniques available for nonlinear systems (the Taylor series approach, similarity transformation-based approaches, differential algebra techniques and the more recent observable normal form approach and symmetries approaches) and significant (symbolic) computational problems can arise, even for relatively simple models in applying these techniques.

In this talk an introduction to structural identifiability analysis will be provided demonstrating the application of the techniques available to both linear and nonlinear parameterised systems and to models of (nonlinear mixed effects) population nature.


 
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