The purpose of this seminar is to provide a stimulating discussion platform between applied and pure mathematicians, mathematical biologists, statisticians, data scientists, on one side, and colleagues from biology, health, and environmental research on the other. We aim to learn more about each others' work and highlight emerging modelling opportunities. Every talk will be geared to non-experts, so come along if you like to learn new things and if you are looking to develop new collaborations.
In Semester A (2024-2025), we meet at 13:00pm (unless otherwise specified) on Mondays (in-person) in MB-503. The seminar is hybrid and can be also attended via this zoom link.
The organisers:
Natasha Blitvic, Weini Huang, Rainer Klages, Kostas Papafitsoros
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Phytoplankton, microscopic photosynthetic marine organisms, are vitally important for maintaining a habitable planet. These organisms have short lifetimes such that evolution is possible on the timescale of years. Experimental studies suggest that phytoplankton can rapidly evolve to climate changes. Adaptation is inherently a stochastic processes and the rate of adaptation will depend on many things including population size. To understand phytoplankton adaptation, we must couple an understanding of how evolution acts on multi-trait phenotypes, with the impact of environmental fluctuations and transport of microbes by ocean currents on selection pressures. Ultimately, these stochastic, individual level dynamics then need to be incorporated into deterministic ecosystem models in order to understand and predict shifts in global carbon cycling.
Bio: Naomi M. Levine is a Gabilan Assistant Professor at the University of Southern California where she holds joint appointments in the Departments of Marine and Environmental Biology, Quantitative and Computational Biology, and Earth Sciences. She received her B.A. in geosciences from Princeton University and her Ph.D. in chemical oceanography from the MIT-WHOI Joint Program. Levine’s research focuses on understanding the interactions between climate and marine microbial ecosystem composition and function. The Levine lab is developing innovative, interdisciplinary numerical models that allow them to understand how dynamics occurring at the scale of individual microbes impact large-scale ecosystem processes such as rates of global carbon cycling. Levine is an Alfred P. Sloan Research Fellow, a Simons Foundation Early Career Investigator and an NSF CAREER award recipient.
Ecologists often view changes in the species composition of an area as signs of damage that should be avoided and reversed. This thinking has influenced international agreements for the protection of aquatic biodiversity, such as the Ramsar Convention on Wetlands and the EU's Water Framework Directive. I will present recent work by Jacob O'Sullivan and myself that shows that a simple two-parameter model can explain such changes as resulting from entirely natural processes. We find that the distribution of the number of sites occupied by species, the Occupancy Frequency Distribution, for three different groups of species consistently adheres to a simple log-series distribution. We explain this distribution in terms of a "birth-death" process, where "birth" corresponds to colonisations of patches and "death" to extirpations. The process implies a distribution for the time species persist in a collection of patches (which ecologists call a "metacommunity"), and this distribution agrees excellently with observations. The surprising finding that complex biological processes unfolding over decades in landscapes spanning tens of kilometres can be described by a simple mathematical model invite further mathematical study of such systems.
Short summary: I will tell a story of how quantitative thinking has helped us overcome experimental challenges in a synthetic yeast cooperative community.
About the speaker: Wenying has been trained in both Math and Biology as her majors and has worked with the evolution of synthetic yeast cooperative community and mathematical modelling of community dynamics in the past decades. We are very happy to have Wenying coming over to QMUL and share her exciting research with us. More info at https://iris.ucl.ac.uk/iris/browse/profile?upi=WSHOU61
An essential trait of cells is their ability to adapt to the environment. This requires cells to modulate their physiology in order to thrive as an individual or as a group of cells. At the heart of cellular adaptation is gene expression which controls what genetic information is converted into physiological output. Traditionally, gene expression is measured in bulk, averaged across entire cell populations. However, individual cells can behave very differently to each other even if they are genetically identical and exposed to the same environment; behaviour that remains obscured by the bulk activity. Individualism may present a selective advantage to the cell under certain environmental conditions, but not others. In this seminar I will present our recent findings on the molecular mechanisms that drive the decision to switch between individual and group behaviour of bacteria.
Genomic instability allows cancer cells to rapidly vary the number of copies of each chromosome (karyotype) through chromosome missegregation events during mitosis, enabling genetic heterogeneity that leads to tumor metastasis and drug resistance. We construct a Markov chain that describes the evolution of the karyotypes of cancer cells. The Markov chain is based on a stochastic model of chromosome missegregation which incorporates the observed fact that individual chromosomes contain proliferative and anti-proliferative genes, leading to cells with varying fitness levels and allowing for Darwinian selection to occur. We analyze the Markov chain mathematically, and we use it to predict the long-term distribution of karyotypes of cancer cells. We then adapt it to study the behavior of tumors under targeted therapy and to model drug resistance.This is joint work with Sam Bakhoum and Ashley Laughney
Please not this seminar talk will be on Tuesday 11:00, different from our standard seminar time.
Zoom link: https://qmul-ac-uk.zoom.us/j/81250086190
A key challenge for microbes is to locate nutrient hotspots amid nutrient deserts ill-suitable for growth. We will discuss several challenges and trade-offs involved in the search behavior. Specifically, I describe our work studying risk-reward trade-offs in search behavior, where we quantify the cost and benefit of bacterial motility to explain a behavioral dichotomy among marine bacteria. I use experiments to demonstrate how the chemotaxis pathway - used to navigate nutrient gradients - can amplify molecular noise to enhance their exploratory behavior even in areas without gradients. Finally, I will also explain how the interactions within chemosensory arrays - large signal-processing protein complexes - are tuned close to a critical point, on the border between order and chaos, and that this helps to leverage a sensory trade-off between response amplitude and speed.
Bio: Johannes Keegstra is a postdoctoral researcher based in the Environmental Microfluidics Group of Prof. Roman Stocker at ETH Zurich. He performed his PhD research in biophysics at the AMOLF research institute in Amsterdam, and before that studied physics in Delft and philosophy in Leiden. His work focusses microbes living in complex environments, linking molecular mechanisms to bacterial behaviour that affect biogeochemical cycles.
Antibiotic resistance is one of the major threats to human society prompting an urgent global response. Bacteria developed multiple strategies for antibiotic resistance by effectively reducing intracellular antibiotic concentrations or antibiotic binding affinities to their specific targets. In this talk, I will present a recently discovered pathway to antibiotic resistance that depends on the bacterial morphological transformation that promotes bacterial decrease of antibiotic influx to the cell. By analysing cell morphological data of different bacterial species under antibiotic stress, we find that bacterial cells robustly reduce the surface-to-volume ratio in response to most types of antibiotics. Using quantitative modelling we show that by reducing the surface-to-volume ratio, bacteria can effectively reduce intracellular antibiotic concentration by decreasing antibiotic influx. The model predicts that bacteria can also increase the surface-to-volume ratio to promote antibiotic dilution for membrane-targeting antibiotics, in agreement with data on membrane-transport inhibitors. Using the particular example of ribosome-targeting antibiotics, I will present a systems-level model for the regulation of cell shape under antibiotic stress and discuss feedback mechanisms that bacteria can harness to increase their fitness in the presence of antibiotics.
Bio: Nikola obtained PhD in theoretical and computational physics from Lehigh University in 2012. Nikola worked on experimental and theoretical aspects of bacterial adaptation to antibiotics during postdocs at Imperial College London, Edinburgh University and University College London. In 2022, Nikola became an independent group leader at Queen Mary University of London.
We present two generic SIR-like epidemic models with stochastic parameters, in which the dynamics self-organize to a critical state with suppressed exponential growth. More precisely, the dynamics evolve into a quasi-steady-state, where the effective reproduction rate fluctuates close to the critical value one for a long period, as indeed observed for different epidemics. In the first model, the rate at which each individual becomes infected changes stochastically in time with a heavy-tailed steady state. The second model assumes a random scale-free interaction network, which is redrawn periodically. In both models, criticality is obtained through a self-organization mechanism and does not require any feedback between the state of the epidemic and the behaviour of individuals.
Elucidating the principles of bacterial motility and navigation is key to understand many important phenomena such as the spreading of infectious diseases. A prime challenge of swimming bacteria is to purposefully and efficiently navigate in their habitat, e.g. the soil, which constitutes a complex, structured environment. In the first part of the talk, we will discuss Bayesian techniques for model inference from time discrete experimental tracking data in general. We showcase particularly methods to infer models with several layers of stochasticity, for example temporal noise and population heterogeneity. Furthermore, we demonstrate how challenges that arise when multidimensional dynamics is only partially observed, e.g. in the case of underdamped Langevin dynamics, colored noise or non-observed internal degrees of freedom, can be addressed. In the second part, we will specifically address the navigation strategy of bacterial swimmers in heterogeneous environments, combing experiments with the soil bacterium Pseudomonas putida as a model organism and active particle modeling. The motility pattern of these bacteria in bulk and agar will be discussed with a particular focus on (anomalous) transport properties. Finally, we will argue that switching between multiple modes of motility that differ in their speed and chemotactic responsiveness provides the basis for robust and efficient chemotaxis.
Collective phenomena arise in a plethora of systems, ranging from seasonal animal migration, to the self-organization of chemical and mechanical man-made machines. Such systems are commonly studied through continuous models. In this talk, a discrete modeling approach based off cellular automata is showcased as an alternative, by presenting two biologically motivated models. The first model considers a hierarchically structured tumor cell population consisting of cancer stem cells and totally differentiated stem cells, and studies the effect of differential mobility and inhibitory processes between both populations on the tumor's invasive behavior. The second model considers a population of swarming individuals and studies how an anisotropic interaction neighborhood at the individual level, akin to a limited vision field, affects the formation of density patterns at the population level.
[1] Stein, A., Kizhuttil, R., Bak, M. and Noble, R.J., 2023. Selective sweep probabilities in spatially expanding populations. bioRxiv, 2023.11.27.568915.
[2] Lemant, J., Le Sueur, C., Manojlović, V. and Noble, R., 2022. Robust, universal tree balance indices. Systematic biology, 71(5), pp.1210-1224.
[3] Noble, R. and Verity, K., 2023. A new universal system of tree shape indices. bioRxiv, 2023.07. 17.549219.
[4] Bak, M., Colyer, B., Manojlović, V. and Noble, R., 2023. Warlock: an automated computational workflow for simulating spatially structured tumour evolution. arXiv preprint arXiv:2301.07808.
[5] Noble, R., Burri, D., Le Sueur, C., Lemant, J., Viossat, Y., Kather, J.N. and Beerenwinkel, N., 2022. Spatial structure governs the mode of tumour evolution. Nature ecology & evolution, 6(2), pp.207-217.
TBA
In this talk I will explore the potential pitfalls in calculating averages appropriately in different biological scenarios and suggest how to approach the problem in general. I consider averaging in foraging models, revisiting the “fallacy of averages” debate where different methods of working out payoff in foraging scenarios was considered. I will also look at related models including group sizes and how to measure them appropriately, and the problem of “length-biased” sampling from renewal theory. All of these problems are inter-related, with the correct selection of probability distributions central to the problem.
Many natural transport phenomena exhibit deviations from Brownian motion, known as ’anomalous diffusion’. Examples of these variations can be observed in a wide range of biological processes, including animal foraging, cellular signaling, and the spatial exploration by motile microorganisms. Besides the biological world, other processes described by anomalous diffusion include the spread of diseases, financial market trends, and climate records. Thus, investigating these phenomena enriches our understanding of transport processes in living matter systems as well as in many events in human life. Despite the interdisciplinary nature of anomalous diffusion, and the increasing interest in its study, investigating and characterising it remains challenging [1].
In my talk, I will discuss how I tackled this problem using a dual approach involving advanced data analysis tools and controlled experiments. Firstly, I will present a recent method, CONDOR, to systematically characterise anomalous diffusion data [2]. Unlike most advanced machine learning techniques, which operate as ’black boxes’, CONDOR combines classical statistical analysis to en- hance the understanding of the underlying diffusion processes in single trajectories, thereby shedding light on their physical nature. Finally, I will introduce a new experimental protocol, based on the use of colloidal particles, to reproduce anomalous diffusion dynamics under controllable conditions by tuning a minimal set of parameters.
[1] G. Muñoz-Gil et al., Objective comparison of methods to decode anomalous diffusion. Nat. Commun. 12, 6253 (2021).
[2] A. Gentili et al., Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR), J. Phys. A 54 314003 (2021).
Cell division is the process of replicating existing cells to create new progeny. It underlies the persistence of all life on earth and is the mechanism that allows the repair of damaged tissue. However, unregulated cell division is the defining feature of cancer and so, for most organisms, careful regulation of cell division is critical. It is this regulatory process that we wish to understand both to inform regenerative medicine and cancer biology. To identify key regulators of cell division, we have used a protein tethering approach to identify the role of regulators for segregating genetic material during cell division. We found that protein kinases and phosphatases appear to be critical, these are proteins that add and remove phosphate residues from proteins to control how they function. These kinases and phosphatases are already known to be critical for cell division, so we used whole proteome approaches to identify their most critical targets. We also use machine learning methodology using data about phosphorylation events across the cell to predict the key regulation circuits that control cell division.
Biography: Peter completed a PhD at the University of Edinburgh with Noreen Murray studying viral restriction. He undertook post-doctoral training with David Porteous, also at the University of Edinburgh and Rodney Rothstein, at Columbia University, New York studying genetic recombination and kinetochore function. In 2011 he started his own laboratory at the National Institute of Medical Research in London, which later became the Francis Crick Institute, to study fundamental regulation of cell division. In 2018 the lab moved to East London to the School of Biological and Behavioural Sciences at Queen Mary University of London.
Population genetics is the discipline investigating genome diversity within and between populations. It aims to elucidate the historical adaptive and neural processes that characterised species' evolution. Therefore, this discipline has fundamental impact in biomedical sciences and conservation biology, among other fields. Recently, deep learning algorithms have provided a novel inferential framework in population genetics. Here, I will discuss how generative models have allowed the creation of realistic synthetic genetic data and the inference of complex models. I will then introduce ongoing work on using generative adversarial networks to infer the past demography of Anopheles gambiae mosquitoes, and how these findings may aid genomic monitoring of malaria in sub-Saharan Africa.