Forthcoming events in this series


Fri, 19 Feb 2021

14:00 - 15:00
Virtual

Mathematical models of targeted cancer therapies

Professor Dominik Wodarz
(Department of Population Health and Disease Prevention University of California Irvine)
Abstract

The talk will discuss the use of mathematical models for understanding targeted cancer therapies. One area of focus is the treatment of chronic lymphocytic leukemia with tyrosine kinase inhibitors. I will explore how mathematical approaches have helped elucidate the mechanism of action of the targeted drug ibrutinib, and will discuss how evolutionary models, based on patient-specific parameters, can make individualized predictions about treatment outcomes. Another focus of the talk is the use of oncolytic viruses to kill cancer cells and drive cancers into remission. These are viruses that specifically infect cancer cells and spread throughout tumors. I will discuss mathematical models applied to experimental data that analyze virus spread in a spatially structured setting, concentrating on the interactions of the virus with innate immune mechanisms that determine the outcome of virus spread.  

Fri, 12 Feb 2021

14:00 - 15:00
Virtual

Geroprotectors, multimorbidity and frailty: why we need AI approaches in the quest to extend healthspan

Professor Ilaria Bellantuono
(Department of Oncology and Metabolism The Medical School Sheffield)
Abstract

Human life expectancy has been increasing steadily over the last century but this has resulted in an increasing incidence of age-related chronic diseases. Over 60% of people over the age of 65 will suffer from more than one disease at the same time (multimorbidity) and 25-50% of those over 80 years old develop frailty, defined as an accumulation of deficits and loss of reserve. Multimorbidity and frailty have complex medical needs and are strongly associated with disability and hospitalization. However, current treatments are suboptimal with problems of polypharmacy due to the fact that each disease is treated individually. Geroprotectors target fundamental mechanisms of ageing common to multiple age-related diseases and shows promise in delaying the onset of multimorbidity and frailty in animal models. However, their clinical testing in patients has been challenging due to the high level of complexity in the mode of action of geroprotectors and in the way multimorbidity and frailty develop.

 The talk will give an overview of these problems and make the case for the use of AI approaches to solve some of those complex issues with a view of designing appropriate clinical trials with geroprotectors to prevent age-related multimorbidity and frailty and extend healthspan.

Fri, 05 Feb 2021

14:00 - 15:00
Virtual

Evolutionary therapy

Professor Alexander Anderson
(Moffitt Cancer Centre)
Abstract

Our current approach to cancer treatment has been largely driven by finding molecular targets, those patients fortunate enough to have a targetable mutation will receive a fixed treatment schedule designed to deliver the maximum tolerated dose (MTD). These therapies generally achieve impressive short-term responses, that unfortunately give way to treatment resistance and tumor relapse. The importance of evolution during both tumor progression, metastasis and treatment response is becoming more widely accepted. However, MTD treatment strategies continue to dominate the precision oncology landscape and ignore the fact that treatments drive the evolution of resistance. Here we present an integrated theoretical, experimental and clinical approach to develop treatment strategies that specifically embrace cancer evolution. We will consider the importance of using treatment response as a critical driver of subsequent treatment decisions, rather than fixed strategies that ignore it. Through the integrated application of drug treatments and drug holidays we will illustrate that, evolutionary therapy can drive either tumor control or extinction. Our results strongly indicate that the future of precision medicine shouldn’t be in the development of new drugs but rather in the smarter evolutionary application of preexisting ones.

Fri, 29 Jan 2021

14:00 - 15:00
Virtual

Emergent robustness of bacterial quorum sensing in fluid flow

Dr Mohit Dalwadi
(Dept of Maths University of Oxford)
Abstract

Bacteria use intercellular signalling, or quorum sensing (QS), to share information and respond collectively to aspects of their surroundings. The autoinducers that carry this information are exposed to the external environment. Consequently, they are affected by factors such as removal through fluid flow, a ubiquitous feature of bacterial habitats ranging from the gut and lungs to lakes and oceans.

We develop and apply a general theory that identifies and quantifies the conditions required for QS activation in fluid flow by systematically linking cell- and population-level genetic and physical processes. We predict that cell-level positive feedback promotes a robust collective response, and can act as a low-pass filter at the population level in oscillatory flow, responding only to changes over slow enough timescales. Moreover, we use our model to hypothesize how bacterial populations can discern between increases in cell density and decreases in flow rate.

Fri, 22 Jan 2021

14:00 - 15:00
Virtual

Paradigms for data-driven discovery and control in biological systems

Professor Nathan Kutz
(Dept of Applied Mathematics University of Washington)
Abstract

A major challenge in the study of biological systems is that of model discovery: turning data into reduced order models that are not just predictive, but provide insight into the nature of the underlying system that generated the data. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data.  Such data-driven methods can be used in the biological sciences where rich data streams are affording new possibilities for the understanding and characterization of complex, networked systems.  In neuroscience, for instance, the integration of these various concepts (reduced-order modeling, equation-free, machine learning, sparsity, networks, multi-scale physics and adaptive control) are critical to formulating successful modeling strategies that perhaps can say something meaningful about experiments.   These methods will be demonstrated on a number of neural systems.  I will also highlight how such methods can be used to quantify cognitive and decision-making deficits arising from neurodegenerative diseases and/or traumatic brain injuries (concussions).

Fri, 04 Dec 2020

14:00 - 15:00
Virtual

Family analysis with mendelian Imputations

Professor Austine Kong
(Nuffield Department of Population Health University of Oxford)
Abstract

Genotype-phenotype associations can be results of direct effects, genetic nurturing effects and population stratification confounding (The nature of nurture: Effects of parental genotypes, Science, 2018, Deconstructing the sources of genotype-phenotype associations in humans, Science, 2019). Genotypes from parents and siblings of the proband can be used to statistically disentangle these effects. To maximize power, a comprehensive framework for utilizing various combinations of parents’ and siblings’ genotypes is introduced. Central to the approach is mendelian imputation, a method that utilizes identity by descent (IBD) information to non-linearly impute genotypes into untyped relatives using genotypes of typed individuals. Applying the method to UK Biobank probands with at least one parent or sibling genotyped, for an educational attainment (EA) polygenic score that has a R2 of 5.7% with EA, its predictive power based on direct genetic effect alone is demonstrated to be only about 1.4%. For women, the EA polygenic score has a bigger estimated direct effect on age-at-first-birth than EA itself.

Fri, 27 Nov 2020

14:00 - 15:00
Virtual

Plant puzzle cell shape is an adaptation to a developmental constraint based on mechanical stress and isotropic growth

Dr Richard Smith
(John Innes Centre Norwich Research Park)
Abstract

The puzzle-shaped cells that appear in the epidermis of many plants are a striking example of a complex cell shape. Since shape in an organism is often thought to be closely related to its function, it suggests that these unusual shapes must have some functional benefit to the plant. We 
propose that the creation of these complex shapes is an effective strategy to reduce mechanical stress in the cell wall. Although the 
formation of these shapes requires highly anisotropic and non-uniform growth at the sub-cellular level, it appears to be triggered by 
isotropic growth at the organ level. Analysis of cell shape over multiple species is consistent with the idea that the puzzle is in 
response to a developmental constraint, and that the mechanism is like to be conserved among higher plants.

Fri, 20 Nov 2020

14:00 - 15:00
Virtual

Digging genomes and structuromes: towards “ab initio” computational biology

Professor Aleksandr Sahakyan
(Weatherall Institute for Molecular Medicine)
Abstract

 “In this talk, I shall present the past research track passing through quantum mechanical studies of small molecules to biomolecules, to proteome-wide big data analyses and computational genomics. Next, the ongoing research in our group will be presented that builds upon the expertise on different levels of information processing in life (genome, transcriptome, proteins, small molecules), to develop self-consistent “first principles” models in biology with a wide spectrum of usage. The immediate benefits and the targeted processes will be described covering different layers of the central dogma of biology, multigenic diseases and disease driver/passenger mutation predictions."

Fri, 13 Nov 2020

14:00 - 15:00
Virtual

Algebraic systems biology

Professor Heather Harrington
(Mathematical Insitute, Oxford)
Abstract

Signalling pathways can be modelled as a biochemical reaction network. When the kinetics are to follow mass-action kinetics, the resulting
mathematical model is a polynomial dynamical system. I will overview approaches to analyse these models with steady-state data using
computational algebraic geometry and statistics. Then I will present how to analyse such models with time-course data using differential
algebra and geometry for model identifiability. Finally, I will present how topological data analysis can be help distinguish models
and data.

Fri, 06 Nov 2020

14:00 - 15:00
Virtual

Infection, inflammation, and intervention: mechanistic modelling of epithelial cells in COVID-19

Dr Dan Nicolau and Dr Nabil Fadai
(School of Mathematical Sciences Queensland University of Technology Brisban)
Abstract

While the pathological mechanisms in COVID-19 illness are still poorly understood, it is increasingly clear that high levels of pro-inflammatory mediators play a major role in clinical deterioration in patients with severe disease. Current evidence points to a hyperinflammatory state as the driver of respiratory compromise in severe COVID-19 disease, with a clinical trajectory resembling acute respiratory distress syndrome (ARDS) but how this “runaway train” inflammatory response emergences and is maintained is not known. In this talk, we present the first mathematical model of lung hyperinflammation due to SARS- CoV-2 infection. This model is based on a network of purported mechanistic and physiological pathways linking together five distinct biochemical species involved in the inflammatory response. Simulations of our model give rise to distinct qualitative classes of COVID-19 patients: (i) individuals who naturally clear the virus, (ii) asymptomatic carriers and (iii–v) individuals who develop a case of mild, moderate, or severe illness. These findings, supported by a comprehensive sensitivity analysis, points to potential therapeutic interventions to prevent the emergence of hyperinflammation. Specifically, we suggest that early intervention with a locally-acting anti-inflammatory agent (such as inhaled corticosteroids) may effectively blockade the pathological hyperinflammatory reaction as it emerges.

 

 

Fri, 30 Oct 2020

14:00 - 15:00
Virtual

Harnessing experimentally-validated mathematical models to forecast influenza-mediated pathology

Professor Amber Smith
(Department of Pediatrics University of Tennessee Health Science Center)
Abstract

Influenza viruses infect millions of individuals each year and cause a significant amount of morbidity and mortality. Understanding how the virus spreads within the lung, how efficacious host immune control is, and how each influences acute lung injury and disease severity is critical to combat the infection. We used an integrative model-experiment exchange to establish the dynamical connections between viral loads, infected cells, CD8+ T cells, lung injury, and disease severity. Our model predicts that infection resolution is sensitive to CD8+ T cell expansion, that there is a critical T cell magnitude needed for efficient resolution, and that the rate of T cell-mediated clearance is dependent on infected cell density. 
We validated the model through a series of experiments, including CD8 depletion and whole lung histomorphometry. This showed that the infected area of the lung matches the model-predicted infected cell dynamics, and that the resolved area of the lung parallels the relative CD8 dynamics. Additional analysis revealed a nonlinear relation between disease severity, inflammation, and lung injury. These novel links between important host-pathogen kinetics and pathology enhance our ability to forecast disease progression.

Fri, 23 Oct 2020

14:00 - 15:00
Virtual

MultiMAP: dimensionality reduction of multiple datasets by manifold approximation and projection

Dr Sarah Teichmann
(Wellcome Genome Campus Wellcome Sanger Institute)
Abstract

Multi-modal data sets are growing rapidly in single cell genomics, as well as other fields in science and engineering. We introduce MultiMAP, an approach for dimensionality reduction and integration of multiple datasets. MultiMAP embeds multiple datasets into a shared space so as to preserve both the manifold structure of each dataset independently, in addition to the manifold structure in shared feature spaces. MultiMAP is based on the rich mathematical foundation of UMAP, generalizing it to the setting of more than one data manifold. MultiMAP can be used for visualization of multiple datasets as well as an integration approach that enables subsequent joint analyses. Compared to other integration for single cell data, MultiMAP is not restricted to a linear transformation, is extremely fast, and is able to leverage features that may not be present in all datasets. We apply MultiMAP to the integration of a variety of single-cell transcriptomics, chromatin accessibility, methylation, and spatial data, and show that it outperforms current approaches in run time, label transfer, and label consistency. On a newly generated single cell ATAC-seq and RNA-seq dataset of the human thymus, we use MultiMAP to integrate cells across pseudotime. This enables the study of chromatin accessibility and TF binding over the course of T cell differentiation.

Fri, 16 Oct 2020

14:00 - 15:00
Virtual

Stochastic modeling of reaction-diffusion processes in biology

Prof Hye-Won Kang
(Dept of Maths & Statistics University of Maryland)
Abstract

 Inherent fluctuations may play an important role in biological and chemical systems when the copy number of some chemical species is small. This talk will present the recent work on the stochastic modeling of reaction-diffusion processes in biochemical systems. First, I will introduce several stochastic models, which describe system features at different scales of interest. Then, model reduction and coarse-graining methods will be discussed to reduce model complexity. Next, I will show multiscale algorithms for stochastic simulation of reaction-diffusion processes that couple different modeling schemes for better efficiency of the simulation. The algorithms apply to the systems whose domain is partitioned into two regions with a few molecules and a large number of molecules.

Fri, 19 Jun 2020

14:00 - 15:00
Virtual

Multi-scale modelling to predict strain in the femoral neck during level walking

Dr Xinshan (Shannon) Li
(Department of Mechanical Engineering University of Sheffield)
Abstract

Femoral neck response to physiological loading during level walking can be better understood, if personalized muscle and bone anatomy is considered. Finite element (FE) models of in vivo cadaveric bones combined with gait data from body-matched volunteers were used in the earlier studies, which could introduce errors in the results. The aim of the current study is to report the first fully personalized multiscale model to investigate the strains predicted at the femoral neck during a full gait cycle. CT-based Finite element models (CT/FE) of the right femur were developed following a validated framework. Muscle forces estimated by the musculoskeletal model were applied to the CT/FE model. For most of the cases, two overall peaks were predicted around 15% and 50% of the gait. Maximum strains were predicted at the superior neck region in the model. Anatomical muscle variations seem to affect femur response leading to considerable variations among individuals, both in term of the strains level and the trend at the femoral neck.
 

Fri, 12 Jun 2020

14:00 - 15:00
Virtual

Live-modelling the temporal regulation of mesoderm specification

Dr Berta Verd
(University of Cambridge)
Abstract

Pattern formation emerges during development from the interplay between gene regulatory networks (GRNs) acting at the single cell level and cell movements driving tissue level morphogenetic changes. As a result, the timing of cell specification and the dynamics of morphogenesis must be tightly cross-regulated. In the developing zebrafish, mesoderm progenitors will spend varying amounts of time (from 5 to 10hrs) in the tailbud before entering the pre-somitic mesoderm (PSM) and initiating a stereotypical transcriptional trajectory towards a mesodermal fate. In contrast, when dissociated and placed in vitro, these progenitors differentiate synchronously in around 5 hours. We have used a data-driven mathematical modelling approach to reverse-engineer a GRN that is able to tune the timing of mesodermal differentiation as progenitors leave the tailbud’s signalling environment, which also explains our in vitro observations. This GRN recapitulates pattern formation at the tissue level when modelled on cell tracks obtained from live-imaging a developing PSM. Our “live-modelling” framework also allows us to simulate how perturbations to the GRN affect the emergence of pattern in zebrafish mutants. We are now extending this analysis to cichlid fishes in order to explore the regulation of developmental time in evolution.

 

Fri, 05 Jun 2020

14:00 - 15:00
Virtual

Teaching nonlinear dynamics to biologists

Professor Alan Garfinkel
(Samueli School of Engineering UCLA)
Abstract

There is a need for a new kind of maths course, to be taught, not to mathematics students, but to biologists with little or no maths background. There have been many recent calls for an upgrade to the mathematical background of biologists: undergraduate biology students need to understand the role of modeling and dynamics in understanding ecological systems, evolutionary dynamics, neuroscience, physiology, epidemiology, and the modeling that underlies the concept of climate change. They also need to understand the importance of feedback, both positive and negative, in creating dynamical systems in biology.

 Such a course is possible. The most important foundational development was the 20th century replacement of the vague and unhelpful concept of a differential equation by the rigorous geometric concept of a vector field, a function from a multidimensional state space to its tangent space, assigning “change vectors” to every point in state space. This twentieth-century concept is not just more rigorous, but in fact makes for superior pedagogy. We also discuss the key nonlinear behaviors that biological systems display, such as switch-like behavior, robust oscillations and even chaotic behavior.

 This talk will outline such a course. It would have a significant effect on the conduct of biological research and teaching, and bring the usefulness of mathematical modeling to a wide audience.

 

Fri, 22 May 2020

14:00 - 15:00

TBA

To be announced
(To be announced)
Fri, 15 May 2020

14:00 - 15:00

To be announced

To be announced
(To be announced)
Fri, 06 Mar 2020

14:00 - 15:00
L3

Multiscale modelling of cell fate specification

Professor Adriana Dawes
(The Ohio State University)
Abstract

During development, cells take on specific fates to properly build tissues and organs. These cell fates are regulated by short and long range signalling mechanisms, as well as feedback on gene expression and protein activity. Despite the high conservation of these signalling pathways, we often see different cell fate outcomes in similar tissues or related species in response to similar perturbations. How these short and long range signals work to control patterning during development, and how the same network can lead to species specific responses to perturbations, is not yet understood. Exploiting the high conservation of developmental pathways, we theoretically and experimentally explore mechanisms of cell fate patterning during development of the egg laying structure (vulva) in nematode worms. We developed differential equation models of the main signalling networks (EGF/Ras, Notch and Wnt) responsible for vulval cell fate specification, and validated them using experimental data. A complex, biologically based model identified key network components for wild type patterning, and relationships that render the network more sensitive to perturbations. Analysis of a simplified model indicated that short and long range signalling play complementary roles in developmental patterning. The rich data sets produced by these models form the basis for further analysis and increase our understanding of cell fate regulation in development.

Fri, 28 Feb 2020

14:00 - 15:00
L3

Diffusion tensor cardiac magnetic resonance imaging to measure myocardial disarray in patients with hypertrophic cardiomyopathy

Dr Rina Ariga
(Radcliffe Department of Medicine University of Oxford)
Abstract

Sudden cardiac death is the most feared complication of Hypertrophic Cardiomyopathy. This inherited heart muscle disease affects 1 in 500 people. But we are poor at identifying those who really need a potentially life-saving implantable cardioverter-defibrillator. Measuring the abnormalities believed to trigger fatal ventricular arrhythmias could guide treatment. Myocardial disarray is the hallmark feature of patients who die suddenly but is currently a post mortem finding. Through recent advances, the microstructure of the myocardium can now be examined by mapping the preferential diffusion of water molecules along fibres using Diffusion Tensor Cardiac Magnetic Resonance imaging. Fractional anisotropy calculated from the diffusion tensor, quantifies the directionality of diffusion.  Here, we show that fractional anisotropy demonstrates normal myocardial architecture and provides a novel imaging biomarker of the underlying substrate in hypertrophic cardiomyopathy which relates to ventricular arrhythmia.

 

Fri, 21 Feb 2020

14:00 - 15:00
L2

Tensors in biological data and algebraic statistics

Dr Anna Seigal
(Mathematical Institute University of Oxford)
Abstract

Tensors are higher dimensional analogues of matrices, used to record data with multiple changing variables. Interpreting tensor data requires finding multi-linear stucture that depends on the application or context. I will describe a tensor-based clustering method for multi-dimensional data. The multi-linear structure is encoded as algebraic constraints in a linear program. I apply the method to a collection of experiments measuring the response of genetically diverse breast cancer cell lines to an array of ligands. In the second part of the talk, I will discuss low-rank decompositions of tensors that arise in statistics, focusing on two graphical models with hidden variables. I describe how the implicit semi-algebraic description of the statistical models can be used to obtain a closed form expression for the maximum likelihood estimate.

Fri, 14 Feb 2020

14:00 - 15:00
L3

Application of artificial neural networks to infer pharmacological molecular-level mechanisms of drug evoked clinical responses

Dr Jonathan Wagg
(Roche Pharmaceutical Research and Early Development)
Abstract

The pRED Clinical Pharmacology Disease Modelling Group (CPDMG) aims to better understand the biological basis of inter-patient variability of clinical response to drugs.  Improved understanding of how our drugs drive clinical responses informs which combination dosing regimens (“right drugs”) specific patient populations (“right patients”) are most likely to benefit from. Drug evoked responses are driven by drug-molecular-target interactions that perturb target functions. These direct, "proximal effects" (typically activation and/or inhibition of protein function) propagate across the biological processes these targets participate in via “distal effects” to drive clinical responses. Clinical Systems Pharmacology approaches are used by CPDMG to predict the mechanisms by which drug combinations evoke observed clinical responses. Over the last 5 years, CPDMG has successfully applied these approaches to inform key decisions across clinical development programs. Implementation of these approaches requires: (i) integration of prior relevant biological/clinical knowledge with large clinical and “omics” datasets; (ii) application of supervised machine learning (specifically, Artificial Neural Networks (ANNs)) to transform this knowledge/data into actionable, clinically relevant, mechanistic insights.  In this presentation, key features of these approaches will be discussed by way of clinical examples.  This will provide a framework for outlining the current limitations of these approaches and how we plan to address them in the future.