BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Claire Guerrier (U. Côte d'Azur) DTSTART:20200501T161500Z DTEND:20200501T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/1 DESCRIPTION:Title: Modeling axon-myelin relationships: insights o n signal propagation and modulation\nby Claire Guerrier (U. Côte d'Az ur) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nThe profound funct ional significance of myelin is revealed by the severe neurological defici ts that are consequent upon multiple inherited or acquired demyelinating c onditions. Recent observations demonstrate that the dimensions of myelin sheaths relative to axon calibers can be modulated in response to adult ex perience with significant functional consequences. Despite the widely acc epted demonstration that adult myelin is adaptive and the rapidly growing evidence that such plasticity plays a key role in both normal and abnormal nervous system function\, the effects of such myelin and axonal organizat ion changes on signal propagation are not clearly understood. In this pro ject\, using data on myelin sheath thickness in relation to axonal diamete r coming from gene edited mice\, we investigate the effects of myelination on the propagation of electrical signals along these axons. We consider an electrical model based on cable theory and on Hodgkin-Huxley type forma lism to represent voltage gated channels at the nodes of Ranvier (NoR). U sing this model\, we investigate the effects of parameter sets correspondi ng to pathological myelin-axon-NoR organization\, on signal propagation. Using mathematical analysis and simulations\, we show that the different f requencies constituting a signal travel at their own speed\, that depends on the fiber properties. Although in normal axons and for a typical signa l\, the difference of speed for different frequencies is negligible\, in a bnormal demyelinated axons\, there are differences that perturbs signal pr opagation\, reducing the reliability of fiber transmission.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Mathieu Desroches (Inria\, Sophia Antipolis - Méditerranée Resea rch Centre) DTSTART:20200522T163000Z DTEND:20200522T173000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/2 DESCRIPTION:Title: Slow-fast analysis of neural bursters: old and new\nby Mathieu Desroches (Inria\, Sophia Antipolis - Méditerranée Research Centre) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nIn th is talk\, I will present recent work on multiple-timescale dynamical syste ms displaying complex oscillations with both slow and fast components. Af ter a brief review of bursting oscillations and the role of so-called spik e-adding transitions in square-wave bursters\, I will introduce a four-dim ensional extension of this scenario which creates small-amplitude slow (su b-threshold) oscillations in between bursts\, mediated by so-called canard solutions. In the second half of the talk\, I will revisit another type of four-dimensional bursting scenario with two slow variables\, namely par abolic bursting\, and provide explanations on how the spike-adding mechani sm in such bursters is also organised by canards but of a different type t han before. This will be showcased on several examples of parabolic burst ers\, both biophysical ones like the Plant model\, and simplified ones lik e theta models. Finally\, I will show how the burst-excitable structure o f networks of theta model may persist across scales up to some mean-field limit. [This is based on joint papers with D Avitabile (Amsterdam)\, GB E rmentrout (Pittsburgh)\, TJ Kaper (Boston)\, M Krupa (Nice) and S Rodrigue s (Bilbao)]\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Roeland Merks (Mathematical Institute and Institute of Biology\, L eiden University\, The Netherlands) DTSTART:20200508T163000Z DTEND:20200508T173000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/3 DESCRIPTION:Title: Collective cell nehavior and cell migration\nby Roeland Merks (Mathematical Institute and Institute of Biology\, Lei den University\, The Netherlands) as part of CRM-CAMBAM Seminar Series\n\n \nAbstract\nDuring embryonic development\, the behavior of individual cell s must be coordinated to create the large scale patterns and tissue moveme nts that shape the whole embryo. Apart from chemical signals exchanged be tween cells\, a prominent role is played by the extracellular matrix (ECM) \; these are the hard or jelly materials (e.g. collagens\, fibronectin) t hat form the micro-environment of many cells in tissues. To get a better grip on the role of the extracellular matrix in determining the behavior o f cells\, we are developing mathematical and computational approaches to a nalyse the interactions off the mechanics of cells and the extracellular m atrix (ECM) [2\, 3\, 4\, 5]. The cell models are usually based on the Cel lular Potts model\, whereas the ECM is model is based on a variety of appr oaches\, including the finite-element model and molecular dynamics. I hav e discussed how these mathematical approaches help to elucidate the regula tion of cell migration\, collective cell behavior during angiogenesis [2] and other mechanisms\, including immune cell migration and the evolution o f multicellularity.\n\nZoom Connection 12:15 (Time America/Montreal/Toront o)\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Pouya Bashivan (Université de Montréal\, MILA) DTSTART:20200515T163000Z DTEND:20200515T173000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/4 DESCRIPTION:Title: Analyze\, Predict & Control: A Pragmatic Appro ach to Understanding the Visual Brain\nby Pouya Bashivan (Université de Montréal\, MILA) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nW ithin tens of milliseconds\, our brain processes the patterns of light tha t strike the eyes in a series of six interconnected cortical areas called the ventral visual pathway. These areas form a necessary substrate for ou r ability to recognize objects and their relationships in the world. Curr ently\, particular deep artificial neural networks constitute our most acc urate models of the neural processing in the ventral visual pathway. In t his talk\, I will describe two recent works\, emphasizing the symbiotic re lationship between neuroscience and machine learning. First\, I will desc ribe how the visual knowledge encapsulated in an artificial neural network model can be utilized to control neural activity at single-neuron resolut ion in visual area V4 in rhesus macaques. I will demonstrate evidence of successful control within two settings: (i) neural “stretch”\, in whic h we synthesized images to stretch the maximal firing rate of any single t argeted neural site well beyond its naturally occurring maximal rate\, and (ii) neural population state control\, in which we synthesized images to control a population of neurons into a experimenter-desired pattern of act ivity. Second\, I will discuss an approach for discovery of improved mode ls of visual object recognition by maximizing the similarity between inter nal activations of candidate models and neural recordings in rhesus macaqu es. Using simulated and experimentally measured neural responses\, I will demonstrate evidence that compared to performance-guided search methods\, this procedure could lead to discovery of models with significantly lower object categorization error. Together\, these studies offer new ways for neuroscience and machine learning to inform one another\, potentially lea ding to a better understanding of neural computations in the brain and dev elopment of more intelligent machines. I will conclude my talk by describ ing how such computational models are enabling us to study the living brai n in ways that were not possible before.\n\nVeuillez communiquer avec l'or ganisateur pour de l'information sur le séminaire / Please contact the or ganizer for details: anmar.khadra@mcgill.ca\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/4/ END:VEVENT BEGIN:VEVENT SUMMARY:André Longtin (Université d'Ottawa) DTSTART:20200612T161500Z DTEND:20200612T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/5 DESCRIPTION:Title: Gamma and Beta Burst Rhythms and E-I network i nference\nby André Longtin (Université d'Ottawa) as part of CRM-CAMB AM Seminar Series\n\n\nAbstract\nBrain rhythms typically occur in epochs o f higher amplitude known as bursts. Such bursts\, in the gamma or beta ra nge frequency ranges\, are thought to contribute to the efficiency of work ing memory\, communication and movement tasks. Abnormalities in bursts ha ve also been associated with motor and psychiatric disorders. The determi nants of burst generation are not known\, specifically how single cell and connectivity parameters influence burst statistics and the corresponding brain states. We first present a generic mathematical model for burst gen eration in an excitatory-inhibitory (EI) network with self-couplings. The resulting equations for the stochastic phase and envelope of the rhythm's fluctuations are shown to depend on only two meta-parameters that combine all the network parameters. They allow us to identify different regimes of amplitude excursions\, and to highlight the supportive role that networ k finite-size effects and noisy inputs to the EI network can have. We dis cuss how burst attributes\, such as their durations and peak frequency con tent\, depend on the network parameters. We also show how to extend this formalism to the coupling of brain rhythms from different areas\, and the importance of noise for determining the phase difference. In practice\, t he problem above follows the a priori challenge of fitting such E-I spikin g networks to single neuron or population data. Thus\, the second part of the talk will discuss a novel method to fit mesoscale dynamics using sing le neuron data along with a low-dimensional\, and hence statistically trac table\, single neuron model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous 'pools' of neurons\, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and conn ectivity parameters given this activity\, which can then be used to either optimize parameters by gradient ascent on the log-likelihood\, or to perf orm Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using an E-I network of generalized integrate-an d-fire neurons for which mesoscopic dynamics have been previously derived. We show that both single-neuron and connectivity parameters can be adequ ately recovered from simulated data.\n\nREFERENCES:\nArthur Powanwe and An dre Longtin\, Scientific Reports 2019\;\nAlexandre René\, André Longtin and Jakob Macke\, Neural Computation 2020.\nFUNDING: NSERC Canada.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Yoichiro Mori (University of Pennsylvania) DTSTART:20200529T161500Z DTEND:20200529T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/6 DESCRIPTION:Title: Cell volume control and osmosis-driven cell mo vement\nby Yoichiro Mori (University of Pennsylvania) as part of CRM-C AMBAM Seminar Series\n\n\nAbstract\nElectrolyte and cell volume regulation is essential in physiological systems. After a brief introduction to cel l volume control and electrophysiology\, I will discuss the classical pump -leak model of electrolyte and cell volume control. It will be shown that thermodynamic considerations lead to a new perspective of cell volume con trol. This classical model will then be generalized to a model with spati al extent (a system of partial differential equations) modeling cell-level electrodiffusive and osmotic phenomena. A simplified version of this mod el will then be applied to study osmosis-driven cell movement. Osmosis-dr iven and the conventional actin-driven cell movement will be compared theo retically and computationally in terms of its properties\, focusing in par ticular on energy expenditure.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Cambam Zoomposium (Several Universities) DTSTART:20200605T140000Z DTEND:20200605T180000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/7 DESCRIPTION:Title: Cambam Zoomposium: Multiple Timescales in Neur onal and Other Systems\nby Cambam Zoomposium (Several Universities) as part of CRM-CAMBAM Seminar Series\n\nLecture held in En ligne/Web.\n\nAbs tract\nThe generation of neuronal activity – from spikes\, to bursts\, t o multi-phase rhythms –\nfundamentally involves the interaction of proce sses that evolve on widely disparate timescales. As\na result\, advances i n theoretical neuroscience and in methods for the analysis of multiple tim escale\ndynamics have emerged synergistically\, with experimental observat ions driving theoretical\ndevelopments and with theoretical advances yield ing new explanations for data. In this session\,\nspeakers will present wo rk featuring advances on both sides of this partnership\, which highlights \nnew findings about neuronal and other biological systems together with t he modern approaches to\nmultiple timescale analysis that underlie these r esults.\n\nCohosted by the University of Waterloo and the Fields Institute . \nFirst Session (10:00 – 11:30 AM - EDT)\nSecond Session (12:30 – 2: 00 PM - EDT)\nFull Programm at: http://www.crm.umontreal.ca/2020/Zoomposiu m20/index_e.php\nURL: https://umontreal.zoom.us/j/99878233014?pwd=bS9xSXc5 c2o2VnVUTkFKOFUyVTc4dz09 - \nMeeting ID: 998 7823 3014 - Password: 609607\ n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/7/ END:VEVENT BEGIN:VEVENT SUMMARY:James Sneyd (University of Auckland) DTSTART:20200619T200000Z DTEND:20200619T210000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/8 DESCRIPTION:Title: Modeling Calcium Signaling in Live Animals \nby James Sneyd (University of Auckland) as part of CRM-CAMBAM Seminar Se ries\n\n\nAbstract\nThe vast majority of previous experimental and theoret ical work on calcium signalling has been in cell lines\, cultured cells\, or\, more recently\, in whole organs. The underlying assumption of these s tudies is that the mechanisms that control calcium signalling in a live an imal are essentially similar\, and one can extrapolate from one to the oth er.\n\nAlthough this assumption is\, to a large extent\, valid and useful\ , recent measurements of cytosolic calcium oscillations in salivary acinar cells from a live mouse have necessitated a major rethink of the mechanis ms underlying whole-cell calcium responses and water transport in salivary cells.\n\nWe shall present these new experimental data\, and show how pre vious models have needed to be significantly modified in order to understa nd and explain these new results.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/8/ END:VEVENT BEGIN:VEVENT SUMMARY:Philip Maini (University of Oxford) DTSTART:20200731T163000Z DTEND:20200731T173000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/9 DESCRIPTION:Title: Modelling collective cell movement in biology and medicine\nby Philip Maini (University of Oxford) as part of CRM-CA MBAM Seminar Series\n\n\nAbstract\nCollective cell movement occurs through out biology and medicine and there are many common features shared across different areas. I will review work we have carried out over the past few years on (i) systematically deriving a PDE model for tumour angiogenesis from a discrete formulation and comparing this model with the classical\, phenomenological snail-trail model\; (ii) agent-based models for cranial n eural crest cell migration in a collaboration with experimental biologists that has revealed a number of new biological insights.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/9/ END:VEVENT BEGIN:VEVENT SUMMARY:Thomas Hillen (University of Alberta) DTSTART:20200626T161500Z DTEND:20200626T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/10 DESCRIPTION:Title: Mathematical Modeling of the Immune-Mediated Theory of Metastasis\nby Thomas Hillen (University of Alberta) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nAccumulating experimental and clinical evidence suggests that the immune response to cancer is not excl usively anti-tumor. In fact\, several pro-tumor effects of the immune sys tem have been identified\, such as production of growth factors\, establis hment of angiogenesis\, inhibition of immune response\, initiation of cell movement and metastasis\, and establishment of metastatic niches. Based on experimental data\, we develop a mathematical model for the immune-medi ated theory of metastasis\, which includes anti- and pro-tumor effects of the immune system. The immune-mediated theory of metastasis can explain d ormancy of metastasis and metastatic blow-up after resection of the primar y tumor. It can explain increased metastasis at sites of injury\, and the relatively poor performance of Immunotherapies\, due to pro-tumor effects of the immune system. Our results suggest that further work is warranted to fully elucidate and control the pro-tumor effects of the immune system in metastatic cancer. (with Adam Rhodes)\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/10/ END:VEVENT BEGIN:VEVENT SUMMARY:José Antonio Carrillo (University of Oxford) DTSTART:20200710T161500Z DTEND:20200710T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/11 DESCRIPTION:Title: Attractive-repulsive models in collective beh avior and applications\nby José Antonio Carrillo (University of Oxfor d) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nWe discuss microsco pic and continuum cell-cell adhesion models and their derivation based on the underlying microscopic assumptions. We analyse the behavior of these models at the microscopic level based on the concept of H-stability of the interaction potential. We will derive these macroscopic limits via mean- field assumptions. We propose an improvement on these models leading to s harp fronts and intermingling invasion fronts between different cell type populations. The model is based on basic principles of localized repulsio n and nonlocal attraction due to adhesion forces at the microscopic level. The new model is able to capture both qualitatively and quantitatively e xperiments by Katsunuma et al. (2016) [J. Cell Biol. 212(5)\, pp. 561- -575]. We also review some of the applications of these models in other a reas of tissue growth in developmental biology. We will analyse the mathe matical properties of the resulting aggregation-diffusion and reaction-dif fusion systems based on variational tools.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/11/ END:VEVENT BEGIN:VEVENT SUMMARY:Carson Chow (National Institute of Diabetes and Digestive and Kidn ey Diseases) DTSTART:20200703T161500Z DTEND:20200703T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/12 DESCRIPTION:Title: Global predictions of unreported SARS-CoV2 in fection from observed COVID-19 cases\nby Carson Chow (National Institu te of Diabetes and Digestive and Kidney Diseases) as part of CRM-CAMBAM Se minar Series\n\n\nAbstract\nIn the absence of full scale serological testi ng\, estimation of infectiousness and fatality of the SARS-CoV-2 virus in the COVID-19 global pandemic is complicated by ascertainment bias resultin g from not all infected individuals being detected and recorded as COVID-1 9 cases. Here\, I will outline a modeling strategy to obtain more plausib le estimates of the true values of key epidemiological variables by fittin g a set of mechanistic Bayesian latent-variable SIR models to confirmed CO VID-19 cases\, deaths\, and recoveries\, for all regions (countries and US states) independently.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/12/ END:VEVENT BEGIN:VEVENT SUMMARY:Sue Ann Campbell (University of Waterloo) DTSTART:20200717T161500Z DTEND:20200717T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/13 DESCRIPTION:Title: Modulation of synchronization by a slowly var ying M-current\nby Sue Ann Campbell (University of Waterloo) as part o f CRM-CAMBAM Seminar Series\n\n\nAbstract\nThe neurotransmitter acetylchol ine has been shown to modulate the firing properties of several types of n eurons through the down-regulation of voltage dependent potassium currents such as the muscarine-sensitive M-current. In particular\, experimental work has shown that this current can switch the phase resetting curves fro m type I to type II and computational models have studied the resulting ch ange in the synchronization of networks of such neurons. In the brain\, l evels of acetylcholine change with activity. For example\, acetylcholine is higher during waking and REM sleep and low during slow wave sleep. Thu s an accurate model of the effects of acetylcholine should include slow va riation of this neurotransmitter. In the first part of the talk we use no rmal form theory to show how the $M$-current affects the bifurcation struc ture of any conductance-based neuron model. In particular\, we show that the $M$-current induces two co-dimension two bifurcation which cause the m odel to transition from a class I to class II oscillator. In the second p art of the talk\, we use a phase model reduction to study the effect of a slowly varying M-current on the synchronization properties of the neural m odel. We show that as the current is downregulated or upregulated the pha se model passes through two pitchfork bifurcations\, which are associated in the full model with the transition between synchronous and asynchronous behaviour. The criticality of the pitchfork bifurcations depends on the neural model and whether the coupling is inhibitory or excitatory. We sho w that periodic slow passage through these pitchfork bifurcation leads to a hysteresis loop and study how different properties of the model affect t his loop and the transitions between synchronous and asynchronous behaviou r. Numerical simulations confirm the results of the phase model analysis. This is joint work with Victoria Booth\, Xueying Wang and Isam Al-Darbas ah.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/13/ END:VEVENT BEGIN:VEVENT SUMMARY:Frithjof Lutscher (University of Ottawa) DTSTART:20200724T161500Z DTEND:20200724T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/14 DESCRIPTION:Title: A seasonal hybrid model for the evolution of flowering onset in plants\nby Frithjof Lutscher (University of Ottawa) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nIn temperate climates with strong seasonal changes\, plants need to \ndecide how to allocate re sources to vegetative growth or to reproduction \nduring a potentially sho rt favorable season. Many plants switch from \nmostly vegetative growth ea rly in the season to mostly reproduction \nlate in the season. The onset o f flowering marks the transition between \nthe two phases. Later onset of flowering typically implies a larger size \nat maturity and higher reprodu ctive capacity. At the same time\, it limits \nthe remaining time in the f avorable season for pollination and seed \ndevelopment. Hence\, plants fac e a trade-off for some optimal flowering onset. \nIn this talk\, I will pr esent a seasonal hybrid model for the density of a \nplant population\, st ructured by onset of flowering as a trait. I will apply\ntwo complementary approches to analyze the system. Overall\, I find that \nevolution favour s some intermediate flowering times. This is joint work with \nTricia Morr is.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/14/ END:VEVENT BEGIN:VEVENT SUMMARY:Jonathan Rubin (University of Pittsburgh) DTSTART:20200807T161500Z DTEND:20200807T174500Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/15 DESCRIPTION:Title: Multiple roles of synaptic “inhibition” & how they arise in decision-making pathways in the basal ganglia\nby J onathan Rubin (University of Pittsburgh) as part of CRM-CAMBAM Seminar Ser ies\n\n\nAbstract\nThis talk concerns topics in mathematical neuroscience but will not assume any specific knowledge of neuroscience. It should be of interest to anyone who would like to learn more about general ideas of mathematical neuroscience or about certain specific topics: integration of multiple streams of inhibition in neural circuits\, the role of the basal ganglia in decision-making and action selection\, cortico-striatal plasti city\, the impact of time-varying reversal potentials\, and mechanisms of neural synchronization and oscillations. The phrase “inhibition” sug gests a holding back or suppression of activity. It has long been recogni zed that the roles of synaptic inhibition in neuronal circuits can be more diverse\, however\, and include promotion of activity through effects suc h as post-inhibitory rebound and disynaptic disinhibition. The basal gang lia (BG) is a hub for the reward signal dopamine and is believed to be inv olved in decision-making and action selection. Interestingly\, most synap tic pathways within the BG involve neurotransmitters that are traditionall y inhibitory. In the first section of my talk\, I will introduce this cir cuitry and present modeling of how these pathways can collaborate to produ ce reward-driven action. I will also present joint work with Tim Verstyne n\, Cati Vich and our trainees\, which (1) introduces a way to map between biologically detailed models and more abstract decision-making models and (2) suggests how different BG inhibitory neurons serve different roles in terms of evidence accumulation and decision thresholds. In the second se ction of my talk\, I will present work with postdoc Ryan Phillips and our collaborator Aryn Gittis in which we model the integration of two inhibito ry pathways by BG output neurons. Our modeling takes into account chlorid e dynamics and its impact on synaptic reversal potentials and shows how th ese pathways can actually induce excitatory effects\, can contribute to sy nchronization and oscillations\, and can affect action selection\, which m ay be related to Parkinson’s disease.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/15/ END:VEVENT BEGIN:VEVENT SUMMARY:Angela Reynolds (Virginia Commonwealth University) DTSTART:20200828T163000Z DTEND:20200828T173000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/16 DESCRIPTION:Title: Modeling the Innate Immune Cells\nby Ange la Reynolds (Virginia Commonwealth University) as part of CRM-CAMBAM Semin ar Series\n\n\nAbstract\nDuring an inflammatory response there is a comple x cascade of reactions\, which may lead to health or sustained inflammatio n during many diseases and processes\, such as wound healing and infection s. In order to understand how the immune cells involved in the inflammator y response contribute to the disease progression\, we have developed vario us models for the immune cell dynamics. In this talk we will start by anal yzing a reduced model and then adapting this model to various diseases by increasing the complexity of the immune cells in the model to gain more bi ological insight. Using bifurcations\, parameter estimation\, and sensitiv ity analysis\, we will explore predictors of outcome and how modulating th e immune response dynamics can alter patient outcome.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/16/ END:VEVENT BEGIN:VEVENT SUMMARY:Thomas Bury (McGill University) DTSTART:20200727T130000Z DTEND:20200727T173000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/17 DESCRIPTION:Title: Interactive data visualisations in Python \nby Thomas Bury (McGill University) as part of CRM-CAMBAM Seminar Series\ n\n\nAbstract\nModern scientific methods give rise to vast quantities of d ata. Creating effective visualisations is essential for both presentation and more importantly exploration of the data. This is no easy task when the data contains dozens of variables and millions of entries. Traditiona l visualisations are static\, that is\, what the user sees is what the use r gets. Using interactive data visualisations allows the user to vary par ameters\, honing in on subsections of the data\, or switching between diff erent plot types - all without touching the code. This allows for rapid e xploration of the data and seamless sharing amongst collaborators\, who on ly require a web browser to open the visualisation. This workshop will eq uip participants with the skills required to begin creating interactive vi sualisations in Python. The format will be highly interactive\, with alte rnation between demonstrations by the instructor and participants working through their own Jupyter notebook (provided in advance). Participants wi ll come away having made several of their own visualisations of either a l arge public dataset\, or their own dataset if they would like to bring one . An example of what can be achieved using these tools can be found at th e following link\, where data output from a model of a cardiac arrhythmia is interactively viewed and analysed. \nhttps://modulated-parasystole.her okuapp.com/\n\nEn ligne/Web - Pour vous inscrire\, veuillez communiquer a vec /For registration\, please contact: thomas.bury@mcgill.ca\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/17/ END:VEVENT BEGIN:VEVENT SUMMARY:Tyler Cassidy\, Francesca Scarabel\, Shaza Alsibaai (See Affiliati ons in the talk comments box) DTSTART:20200806T140000Z DTEND:20200806T183000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/18 DESCRIPTION:Title: CRM-CAMBAM Mini-workshop in Mathematical Biol ogy\nby Tyler Cassidy\, Francesca Scarabel\, Shaza Alsibaai (See Affil iations in the talk comments box) as part of CRM-CAMBAM Seminar Series\n\n \nAbstract\nSee details at : http://www.crm.umontreal.ca/2020/Ateliers20/\ nSummary\nIndividual level behaviour and processes\, such as reproduction\ , movement\, growth\, and death\,\ndrive population level dynamics. Typica lly\, mathematical modellers homogenize these individual level behaviours by considering the “average” behaviour and making arguments that lead\ nto into ordinary differential equation models for population level dynami cs. In this workshop\,\nwe introduce a modelling methodology that begins w ith the biological considerations underlying the individual level behaviou r. Throughout careful book-keeping\, we show how to use\nthese individual level behaviours to derive population level dynamics. By using the classic SIR\nmodel from epidemiology\, we show how considering more realistic inf ection dynamics naturally\nlead to functional equations\, and illustrate t he analytical and numerical techniques that allow\nmodellers to derive bio logical information from the population level dynamics.\nThis workshop wil l provide a “users guide” overview to the use of structured population models\nand is intended as a gentle introduction to the use of delay equa tions in mathematical biology.\nParticipants will learn to build populatio n level models from individual level behaviours\, will\nbe introduced to t he analytical and numerical skills used to derive biological information f rom\npopulation level dynamics\, and will be able to identify the similari ties and differences between\nstructured population models and ordinary di fferential equation models. The workshop will be\na mix of worked analytic al and numerical examples and will include a refresher of the necessary\nm athematical techniques.\n\nExpected Background\nWe expect that the worksho p will be accessible to students with a background in undergraduate\ndiffe rential equations and numerical methods\, and will refresh the relevant ma thematical theory\nas necessary throughout the workshop.\n\nSpeakers\nTyle r Cassidy\nPostdoctoral Researcher\, Theoretical Biology and Biophysics\, Los Alamos National Laboratory.\n\nFrancesca Scarabel\nPostdoctoral Resear cher\, Laboratory for Industrial and Applied Mathematics\, York University .\n\nShaza Alsibaai\nPhD Student\, Department of Mathematics and Statistic s\, McGill University\n\nRegistration: https://docs.google.com/forms/d/e/1 FAIpQLSdA6QKYjKEGXr60wc7OUkw785Z1BkX9LrPKBhTXQEz5di8yLQ/viewform\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/18/ END:VEVENT BEGIN:VEVENT SUMMARY:Morgan Craig (Université de Montréal/Centre de recherche CHUSJ)\ , Adrianne Jenner (Université de Montréal/Centre de recherche CHUSJ)\, P aul Macklin (Indiana University)\, Randy Heiland (Indiana University)\, an d Pantea Poolavand (Bloomington) DTSTART:20200813T163000Z DTEND:20200813T203000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/19 DESCRIPTION:Title: Computational modelling to study cancer biolo gy and treatments\nby Morgan Craig (Université de Montréal/Centre de recherche CHUSJ)\, Adrianne Jenner (Université de Montréal/Centre de re cherche CHUSJ)\, Paul Macklin (Indiana University)\, Randy Heiland (Indian a University)\, and Pantea Poolavand (Bloomington) as part of CRM-CAMBAM S eminar Series\n\n\nAbstract\nSee details at: http://www.crm.umontreal.ca/2 020/Ateliers20/\nBACKGROUND\nCancer biology and treatment involves complex \, dynamic interactions between cancer cells\, the tumour microenvironment \, and therapeutic molecules. Quantitative approaches combining mechanisti c disease modelling and computational strategies are increasingly leverage d to rationalize pre-clinical and clinical studies\, and to establish effe ctive treatment strategies. In this\nway\, mathematical approaches lay the foundation for computational “virtual laboratories” that offer fully controlled\, and non-invasive conditions in which we can investigate emerg ent clinical behaviours and interrogate new therapeutic strategies.\nAs an introduction to such virtual laboratories\, this workshop will provide an overview of techniques used in computational oncology\, with a focus on i n silico clinical trials and agent-based models (ABMs). Virtual (or in sil ico) clinical trials are useful computational platforms that help distingu ish mechanisms of therapeutic successes and failures\, stratify patient ri sk classes based on an individual’s physiology\, and optimize drug-speci fic parameters. In these platforms\, in silico patients are generated by d rawing from distributions of possible patient characteristics and used to form virtual clinical trials\, in which new treatment strategies can be ev aluated prior to human trials. Data fitting and optimisation techniques ar e cornerstones of this computational platform and are used to generate rea listic virtual patients and evaluate individualised therapies. ABMs are a computational formalism that describes the way individual agents (e.g. can cer cells) interact through probability distributions based on defined cha racteristics that have contributed significant insights into cancer biolog y at the intra-patient tissue level. In oncology\, this technique has been applied to model spatial tumour formation\, tumour cell heterogeneity\, a nd the dynamics of treatment in the tumour microenvironment. Modelling ind ividual cells as agents allows for direct translation of biological\nobser vation into simulation rules and\, like virtual clinical trials\, the inve stigation of new hypotheses and treatment strategies.\n\nIn particular\, t his workshop will address:\n• the optimization of parameter ranges to ge nerate virtual patients or treatment schedules using a variety of techniqu es\,\nincluding simulated annealing\, least-squares nonlinear optimisation \, gradient-based descent\, and genetic algorithms.\n• the translation b etween ABMs and PDEs\n• how to code heterogenous tumour environments int o an ABM using an open-source software known as PhysiCell\nWorkshop partic ipants will have the opportunity to see how each of these techniques are a pplied in computational oncology and learn how to employ them on experimen tal or generated data in Matlab and in C++. By the end of this workshop\, participants will have a comprehensive understanding of computational mode lling in oncology\, the explicit knowledge for how to design\, code\, and simulate an agent-based model\, and an understanding of how to account for within- and betweenpatient heterogeneity by deploying in silico clinical trials.\n\nWORKSHOP ORGANISERS AND SPEAKERS\nMorgan Craig\, Assistant Prof essor\, Université de Montréal/Centre de recherche CHUSJ\, Montréal\, C anada\nAdrianne Jenner\, Postdoctoral Fellow\, Université de Montréal/Ce ntre de recherche CHUSJ\, Montréal\, Canada\nPaul Macklin\, Associate Pro fessor\, Indiana University\, Bloomington\, USA\nRandy Heiland\, Indiana U niversity\, Bloomington\, USA\nPantea Poolavand\, University of Sydney\, S ydney\, Australia\n\nTICKETS\nFree registration for the event can be found at:\nhttps://www.eventbrite.com/e/computational-modelling-to-study-cancer -biology-and-treatments-tickets-113637272140\nMake sure to go through the “pre-flight checklist” available on the Eventbrite page and download t he appropriate programs and software to run PhysiCell. For the Matlab tuto rial\, you will need to have Matlab on your computer.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/19/ END:VEVENT BEGIN:VEVENT SUMMARY:Mark Chaplain (University of St. Andrews) DTSTART:20200814T163000Z DTEND:20200814T173000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/20 DESCRIPTION:Title: A Mathematical Framework for Modelling the Me tastatic Spread of Cancer\nby Mark Chaplain (University of St. Andrews ) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nInvasion and metasta sis are two of the hallmarks of cancer and are intimately connected proces ses. Invasion\, as the name suggests\, involves cancer cells spreading ou t from the main cancerous mass into the surrounding tissue\, through produ ction and secretion of matrix degrading enzymes. Metastatic spread is the process whereby invasive cancer cells enter nearby blood vessels (or lymp h vessels)\, are carried around the body in the main circulatory system an d then succeed in escaping from the circulatory system at distant secondar y sites where the growth of the cancer starts again. It is this metastat ic spread that is responsible for around 90% of deaths from cancer. To sh ed light on the metastatic process\, we present a mathematical modelling f ramework that captures for the first time the interconnected processes of invasion and metastatic spread of individual cancer cells in a spatially e xplicit manner—a multigrid\, hybrid\, individual-based approach. This f ramework accounts for the spatiotemporal evolution of mesenchymal- and epi thelial-like cancer cells\, membrane-type-1 matrix metalloproteinase (MT1- MMP) and the diffusible matrix metalloproteinase-2 (MMP-2)\, and for their interactions with the extracellular matrix. Using computational simulati ons\, we demonstrate that our model captures all the key steps of the inva sion-metastasis cascade\, i.e. invasion by both heterogeneous cancer cell clusters and by single mesenchymal-like cancer cells\; intravasation of t hese clusters and single cells both via active mechanisms mediated by matr ix-degrading enzymes (MDEs) and via passive shedding\; circulation of canc er cell clusters and single cancer cells in the vasculature with the assoc iated risk of cell death and disaggregation of clusters\; extravasation of clusters and single cells\; and metastatic growth at distant secondary si tes in the body.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/20/ END:VEVENT BEGIN:VEVENT SUMMARY:Jonathan Michaels (University of Western Ontario) DTSTART:20210406T160000Z DTEND:20210406T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/21 DESCRIPTION:Title: Combining deep learning and primate electroph ysiology to understand reach and grasp control\nby Jonathan Michaels ( University of Western Ontario) as part of CRM-CAMBAM Seminar Series\n\n\nA bstract\nWhether it’s picking up a cup or deftly slotting a key into a l ock\, we appear to move our arms and hands with ease. While humans do the se tasks easily – we have not developed artificial systems with the same level of skill. Nonetheless\, in recent years we have progressed greatly in our understanding of AI/robotics as well as the nervous system. The g oal of my research is to understand how the nervous system controls our ar ms and hands with this incredible level of flexibility\, how sensory info rmation supports this control\, and what computational principles guide th e acquisition of these skills. In this talk\, I will present my recent wo rk developing neural network models of the primate grasping system to pro vide insight into how the brain coordinates grasping and lay out how we ca n use tools from AI to help understand the brain.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/21/ END:VEVENT BEGIN:VEVENT SUMMARY:Stephen Coombes (University of Nottingham) DTSTART:20210427T160000Z DTEND:20210427T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/22 DESCRIPTION:Title: Next generation neural field modelling\nb y Stephen Coombes (University of Nottingham) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nNeural mass models have been actively used since th e 1970s to model the coarse-grained activity of large populations of neuro ns and synapses.  They have proven especially fruitful for understanding brain rhythms.  However\, although motivated by neurobiological considera tions they are phenomenological in nature\, and cannot hope to recreate so me of the rich repertoire of responses seen in real neuronal tissue.  In this talk I will discuss a simple spiking neuron network model that has re cently been shown to admit to an exact mean-field description for synaptic interactions.  This has many of the features of a neural mass model coup led to an additional dynamical equation that describes the evolution of po pulation synchrony.  I will show that this next generation neural mass mo del is ideally suited to understanding beta-rebound. This is readily obse rved in MEG recordings whereby motor action causes a drop in the beta powe r band attributed to a loss of network synchrony.  Existing neural mass m odels are unable to capture this phenomenon (event related de-synchrony) s ince they do not track any notion of network coherence (only firing rate).   I will spend the latter part of my talk discussing patterns and waves i n a spatially continuous non-local extension of this model\, highlighting its usefulness for large scale cortical modelling.   \n\nIf you would li ke a not too technical heads-up about this and related work please see Á Byrne\, R O’ Dea\, M Forrester\, J Ross and S Coombes 2020 Next generati on neural mass and field modelling\, Journal of Neurophysiology\, Vol 123\ , 726–742 https://doi.org/10.1152/jn.00406.2019\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/22/ END:VEVENT BEGIN:VEVENT SUMMARY:Arjun Krishnaswamy (McGill University) DTSTART:20210420T160000Z DTEND:20210420T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/23 DESCRIPTION:Title: Molecular cues for the assembly and function of retinal circuits\nby Arjun Krishnaswamy (McGill University) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nIntricate patterns of connect ivity among neurons are critical for our abilities\, and all too often\, d isabilities. Our goal is to understand how such patterns\, called circuits \, arise by studying the assembly of neural circuits in the mouse retina. Here\, the specific connections among ~120 retinal interneurons and ~45 re tinal ganglion cells create circuits that detect visual features such as m otion direction. Today\, I will present our recent work showing that membe rs of the cadherin and immunoglobulin superfamilies play a critical role i n establishing such specific connections and creating circuits that detect features in the visual scene.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/23/ END:VEVENT BEGIN:VEVENT SUMMARY:Maryam M. Shanechi (University of Southern California) DTSTART:20210511T160000Z DTEND:20210511T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/24 DESCRIPTION:Title: Dynamical modeling\, decoding\, and control o f multiscale brain networks: from motor to mood\nby Maryam M. Shanechi (University of Southern California) as part of CRM-CAMBAM Seminar Series\ n\n\nAbstract\nIn this talk\, I will discuss our work on dynamical modelin g\, decoding\, and control of large-scale brain network activity underlyin g naturalistic motor and mood states. I present a multiscale dynamical mo deling framework that allows us to decode human mood variations and identi fy brain regions that are most predictive of mood. I then develop a syste m identification approach that can predict multiregional brain network dyn amics (output) in response to time-varying electrical stimulation (input) toward enabling closed-loop control of brain activity. Further\, I extend our modeling framework to enable dissociating and uncovering behaviorally relevant neural dynamics that can otherwise be missed\, such as those dur ing naturalistic movements. Finally\, I show how our framework can model brain network activity across multiple spatiotemporal scales simultaneousl y\, thus uncovering multiscale neural dynamics that explain naturalistic r each-and-grasp behavior. These dynamical models\, decoders\, and controll ers can provide new neuroscientific insight and enable brain-machine inter faces for personalized therapy in neurological and neuropsychiatric disord ers.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/24/ END:VEVENT BEGIN:VEVENT SUMMARY:Marc Timme (Dresden) DTSTART:20211005T160000Z DTEND:20211005T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/25 DESCRIPTION:Title: Model-free inference of network structural fe atures from observed dynamics\nby Marc Timme (Dresden) as part of CRM- CAMBAM Seminar Series\n\n\nAbstract\nThe dynamics of biological networks e nables the function of a variety of systems we rely on every day\, from ge ne and protein regulation to metabolic circuits and neural networks in the brain. Understanding and predicting network function relies on suitable models\, yet it remains unclear how to extract key features of networks if only time series data from (some) units are available. Here we report on recent progress on model-free inference of network structural features fr om observed dynamics. First\, we demonstrate how to identify the number N of dynamical variables making up a network -- arguably its most fundament al property -- from recorded time series of only a small subset of nThe MeshCODE theory – does our brain store memories in a binary format?\nby Ben Goult (University of Kent) as par t of CRM-CAMBAM Seminar Series\n\n\nAbstract\nCell adhesion to the extrace llular matrix (ECM)\, mediated by integrins\, is exquisitely sensitive to biochemical\, structural\, and mechanical features of the ECM. Talin\, the primary link between integrins and the actin cytoskeleton\, coordinates t he binding of a wide range of cytoskeletal and signaling adaptors in a for ce-dependent manner. Our work has defined talin as a major mechanosensitiv e signalling hub. More recently we have discovered that talin has “molec ular memory” and so provides organisms with a way to store data\, throug h persistent alterations in protein conformation. In this talk I discuss t he implications of these findings and describe a novel role for integrin a dhesions in data-storage leading to a novel theory of how memories are sto red in our brain. The MeshCODE theory presented here provides an original concept for the molecular basis of memory storage. I propose that memory i s biochemical in nature\, written in the form of different protein conform ations in each of the trillions of synapses. Based on established biophysi cal principles\, a mechanical basis for memory would provide a physical lo cation for data storage in the brain. Furthermore\, the conversion and sto rage of sensory and temporal inputs into a binary format identifies an add ressable read/write memory system supporting the view of the mind as an or ganic supercomputer.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/26/ END:VEVENT BEGIN:VEVENT SUMMARY:Guillaume Lajoie (University of Montreal) DTSTART:20211214T170000Z DTEND:20211214T180000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/27 DESCRIPTION:Title: Top-down optimization recovers biological cod ing principles of single-neuron adaptation in RNNs\nby Guillaume Lajoi e (University of Montreal) as part of CRM-CAMBAM Seminar Series\n\n\nAbstr act\nSpike frequency adaptation (SFA) is a well studied physiological mech anism with established computational properties at the single neuron level \, including noise mitigating effects based on efficient coding principles . Network models with adaptive neurons have revealed advantages including modulation of total activity\, supporting Bayesian inference\, and allowin g computations over distributed timescales. Such efforts are bottom-up\, m odeling adaptive mechanisms from physiology and analysing their effects. H ow top-down environmental and functional pressures influence the specifici ty of adaptation remains largely unexplored.\n\nIn this talk\, I will disc uss work where we use deep learning to uncover optimal adaptation strategi es from scratch\, in recurrent neural networks (RNNs) performing perceptua l tasks. In our RNN model\, each neuron's activation function (AF) is take n from a parametrized family to allow modulation mimicking SFA\, and an ad aptation controller is trained end-to-end to control an AF in real time\, based on pre-activation inputs to a neuron. Remarkably\, we find emergent adaptation strategies that implement SFA mechanisms from biological neuron s\, including fractional input differentiation. This suggests that even in simplified models\, environmental pressures and objective-based optimizat ion are enough for sophisticated biological mechanisms to emerge.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/27/ END:VEVENT BEGIN:VEVENT SUMMARY:Gary Bader (University of Toronto) DTSTART:20220125T170000Z DTEND:20220125T180000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/28 DESCRIPTION:by Gary Bader (University of Toronto) as part of CRM-CAMBAM Se minar Series\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/28/ END:VEVENT BEGIN:VEVENT SUMMARY:Becca Asquith (Imperial College London) DTSTART:20220201T170000Z DTEND:20220201T180000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/29 DESCRIPTION:by Becca Asquith (Imperial College London) as part of CRM-CAMB AM Seminar Series\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/29/ END:VEVENT BEGIN:VEVENT SUMMARY:Josh McDermott (MIT) DTSTART:20220222T170000Z DTEND:20220222T180000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/30 DESCRIPTION:by Josh McDermott (MIT) as part of CRM-CAMBAM Seminar Series\n \nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/30/ END:VEVENT BEGIN:VEVENT SUMMARY:Antoine Allard (University of Laval) DTSTART:20220322T160000Z DTEND:20220322T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/31 DESCRIPTION:by Antoine Allard (University of Laval) as part of CRM-CAMBAM Seminar Series\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/31/ END:VEVENT BEGIN:VEVENT SUMMARY:Allison Shaw (University of Minnesota) DTSTART:20220405T160000Z DTEND:20220405T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/32 DESCRIPTION:Title: Parasites\, animal migration\, and how perspe ctive shapes science\nby Allison Shaw (University of Minnesota) as par t of CRM-CAMBAM Seminar Series\n\n\nAbstract\nAnimal migration (round-trip \, predictable movements) takes individuals across space and time\, bringi ng them into contact with new communities of organisms. In particular\, m igratory movements can be shaped by the costs and risk of parasite transmi ssion. Here\, I will present some of our work using mathematical models t o understand how parasites shape the evolution of animal migration. We us e adaptive dynamics to determine the evolutionarily stable strategy of mig ratory tendency\, given different infection scenarios.  Finally\, I’ll use this example to argue that research is shaped by the identities\, pers pectives\, and experiences of the scientists who conduct it. Perspective shapes the questions we ask\, the systems we work in\, and the processes w e choose to explore (as well as the ones we choose to ignore).\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/32/ END:VEVENT BEGIN:VEVENT SUMMARY:Sam Gershman (Harvard University) DTSTART:20220412T160000Z DTEND:20220412T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/33 DESCRIPTION:by Sam Gershman (Harvard University) as part of CRM-CAMBAM Sem inar Series\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/33/ END:VEVENT BEGIN:VEVENT SUMMARY:Michael Frank (Stanford) DTSTART:20220920T160000Z DTEND:20220920T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/34 DESCRIPTION:Title: Bigger data about smaller people: Studying la nguage learning at scale\nby Michael Frank (Stanford) as part of CRM-C AMBAM Seminar Series\n\n\nAbstract\nEvery typically developing child learn s to talk\, but children vary tremendously in how and when they do so. Wha t predicts this variability\, and what is consistent across children and a cross learners of different languages? In this talk\, I’ll describe our efforts to create predictive models of early language learning as a way of formalizing hypotheses in this space. This goal has led us to create open data resources like Wordbank\, childes-db\, and Peekbank that capture dat a from tens of thousands of children learning dozens of different language s.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/34/ END:VEVENT BEGIN:VEVENT SUMMARY:Amy Goldberg (Duke University) DTSTART:20220927T160000Z DTEND:20220927T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/35 DESCRIPTION:Title: Evolutionary perspectives on malaria: humans\ , primates\, and the parasites we share\nby Amy Goldberg (Duke Univers ity) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nClassically calle d one of the strongest selective pressures in human evolution\, I will dis cuss new computational methods we are developing to understand the ongoing host and pathogen pressures shaping malaria. First\, leveraging the adde d information that distributions of genetic ancestry provide\, we infer ra pid adaptation to P. vivax malaria in humans from the islands of Cabo Ver de. We describe a suite of tools that are broadly applicable to study pos t-admixture adaptation. Then\, we consider the broader spectrum of malari a parasites across primates to begin to ask why some impact human evolutio n and disease burden more than others. To do this\, we will first need ne w population-genetic simulation methods to interpret patterns of variation in malaria parasites given their complex lifecycles.\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/35/ END:VEVENT BEGIN:VEVENT SUMMARY:Peter Harrison (Cambridge) DTSTART:20221101T160000Z DTEND:20221101T170000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/36 DESCRIPTION:by Peter Harrison (Cambridge) as part of CRM-CAMBAM Seminar Se ries\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/36/ END:VEVENT BEGIN:VEVENT SUMMARY:James Faeder (University of Pittsburgh) DTSTART:20221108T170000Z DTEND:20221108T180000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/37 DESCRIPTION:by James Faeder (University of Pittsburgh) as part of CRM-CAMB AM Seminar Series\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/37/ END:VEVENT BEGIN:VEVENT SUMMARY:John Murray (Yale University) DTSTART:20221129T170000Z DTEND:20221129T180000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/38 DESCRIPTION:by John Murray (Yale University) as part of CRM-CAMBAM Seminar Series\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/38/ END:VEVENT BEGIN:VEVENT SUMMARY:Amber Smith (Duke University) DTSTART:20221206T170000Z DTEND:20221206T180000Z DTSTAMP:20260404T110745Z UID:AppliedMathInBiosAndMedecine/39 DESCRIPTION:by Amber Smith (Duke University) as part of CRM-CAMBAM Seminar Series\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AppliedMathInBiosAndMede cine/39/ END:VEVENT END:VCALENDAR