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:20260422T212831Z UID:AppliedMathInBiosAndMedecine/1 DESCRIPTION:Title: Modeling axon-myelin relationships: insights on signa l propagation and modulation\nby Claire Guerrier (U. Côte d'Azur) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nThe profound functional s ignificance of myelin is revealed by the severe neurological deficits that are consequent upon multiple inherited or acquired demyelinating conditio ns. Recent observations demonstrate that the dimensions of myelin sheaths relative to axon calibers can be modulated in response to adult experienc e with significant functional consequences. Despite the widely accepted d emonstration that adult myelin is adaptive and the rapidly growing evidenc e that such plasticity plays a key role in both normal and abnormal nervou s system function\, the effects of such myelin and axonal organization cha nges on signal propagation are not clearly understood. In this project\, using data on myelin sheath thickness in relation to axonal diameter comin g from gene edited mice\, we investigate the effects of myelination on the propagation of electrical signals along these axons. We consider an elec trical model based on cable theory and on Hodgkin-Huxley type formalism to represent voltage gated channels at the nodes of Ranvier (NoR). Using th is model\, we investigate the effects of parameter sets corresponding to p athological myelin-axon-NoR organization\, on signal propagation. Using m athematical analysis and simulations\, we show that the different frequenc ies constituting a signal travel at their own speed\, that depends on the fiber properties. Although in normal axons and for a typical signal\, the difference of speed for different frequencies is negligible\, in abnormal demyelinated axons\, there are differences that perturbs signal propagati on\, reducing the reliability of fiber transmission.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Mathieu Desroches (Inria\, Sophia Antipolis - Méditerranée Resea rch Centre) DTSTART:20200522T163000Z DTEND:20200522T173000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/2 DESCRIPTION:Title: Slow-fast analysis of neural bursters: old and new\nby Mathieu Desroches (Inria\, Sophia Antipolis - Méditerranée Researc h Centre) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nIn this talk \, I will present recent work on multiple-timescale dynamical systems disp laying complex oscillations with both slow and fast components. After a b rief review of bursting oscillations and the role of so-called spike-addin g transitions in square-wave bursters\, I will introduce a four-dimensiona l extension of this scenario which creates small-amplitude slow (sub-thres hold) oscillations in between bursts\, mediated by so-called canard soluti ons. In the second half of the talk\, I will revisit another type of four -dimensional bursting scenario with two slow variables\, namely parabolic bursting\, and provide explanations on how the spike-adding mechanism in s uch bursters is also organised by canards but of a different type than bef ore. This will be showcased on several examples of parabolic bursters\, b oth biophysical ones like the Plant model\, and simplified ones like theta models. Finally\, I will show how the burst-excitable structure of netwo rks of theta model may persist across scales up to some mean-field limit. [This is based on joint papers with D Avitabile (Amsterdam)\, GB Ermentro ut (Pittsburgh)\, TJ Kaper (Boston)\, M Krupa (Nice) and S Rodrigues (Bilb ao)]\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Roeland Merks (Mathematical Institute and Institute of Biology\, L eiden University\, The Netherlands) DTSTART:20200508T163000Z DTEND:20200508T173000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/3 DESCRIPTION:Title: Collective cell nehavior and cell migration\nby R oeland Merks (Mathematical Institute and Institute of Biology\, Leiden Uni versity\, The Netherlands) as part of CRM-CAMBAM Seminar Series\n\n\nAbstr act\nDuring embryonic development\, the behavior of individual cells must be coordinated to create the large scale patterns and tissue movements tha t shape the whole embryo. Apart from chemical signals exchanged between c ells\, a prominent role is played by the extracellular matrix (ECM)\; thes e are the hard or jelly materials (e.g. collagens\, fibronectin) that for m the micro-environment of many cells in tissues. To get a better grip on the role of the extracellular matrix in determining the behavior of cells \, we are developing mathematical and computational approaches to analyse the interactions off the mechanics of cells and the extracellular matrix ( ECM) [2\, 3\, 4\, 5]. The cell models are usually based on the Cellular P otts model\, whereas the ECM is model is based on a variety of approaches\ , including the finite-element model and molecular dynamics. I have discu ssed how these mathematical approaches help to elucidate the regulation of cell migration\, collective cell behavior during angiogenesis [2] and oth er mechanisms\, including immune cell migration and the evolution of multi cellularity.\n\nZoom Connection 12:15 (Time America/Montreal/Toronto)\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Pouya Bashivan (Université de Montréal\, MILA) DTSTART:20200515T163000Z DTEND:20200515T173000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/4 DESCRIPTION:Title: Analyze\, Predict & Control: A Pragmatic Approach to Understanding the Visual Brain\nby Pouya Bashivan (Université de Mont réal\, MILA) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nWithin t ens of milliseconds\, our brain processes the patterns of light that strik e the eyes in a series of six interconnected cortical areas called the ven tral visual pathway. These areas form a necessary substrate for our abili ty to recognize objects and their relationships in the world. Currently\, particular deep artificial neural networks constitute our most accurate m odels of the neural processing in the ventral visual pathway. In this tal k\, I will describe two recent works\, emphasizing the symbiotic relations hip between neuroscience and machine learning. First\, I will describe ho w the visual knowledge encapsulated in an artificial neural network model can be utilized to control neural activity at single-neuron resolution in visual area V4 in rhesus macaques. I will demonstrate evidence of success ful control within two settings: (i) neural “stretch”\, in which we sy nthesized images to stretch the maximal firing rate of any single targeted neural site well beyond its naturally occurring maximal rate\, and (ii) n eural population state control\, in which we synthesized images to control a population of neurons into a experimenter-desired pattern of activity. Second\, I will discuss an approach for discovery of improved models of v isual object recognition by maximizing the similarity between internal act ivations of candidate models and neural recordings in rhesus macaques. Us ing simulated and experimentally measured neural responses\, I will demons trate evidence that compared to performance-guided search methods\, this p rocedure could lead to discovery of models with significantly lower object categorization error. Together\, these studies offer new ways for neuros cience and machine learning to inform one another\, potentially leading to a better understanding of neural computations in the brain and developmen t of more intelligent machines. I will conclude my talk by describing how such computational models are enabling us to study the living brain in wa ys that were not possible before.\n\nVeuillez communiquer avec l'organisat eur pour de l'information sur le séminaire / Please contact the organizer for details: anmar.khadra@mcgill.ca\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/4/ END:VEVENT BEGIN:VEVENT SUMMARY:André Longtin (Université d'Ottawa) DTSTART:20200612T161500Z DTEND:20200612T174500Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/5 DESCRIPTION:Title: Gamma and Beta Burst Rhythms and E-I network inferenc e\nby André Longtin (Université d'Ottawa) as part of CRM-CAMBAM Semi nar Series\n\n\nAbstract\nBrain rhythms typically occur in epochs of highe r amplitude known as bursts. Such bursts\, in the gamma or beta range fre quency ranges\, are thought to contribute to the efficiency of working mem ory\, communication and movement tasks. Abnormalities in bursts have also been associated with motor and psychiatric disorders. The determinants o f burst generation are not known\, specifically how single cell and connec tivity parameters influence burst statistics and the corresponding brain s tates. We first present a generic mathematical model for burst generation in an excitatory-inhibitory (EI) network with self-couplings. The result ing equations for the stochastic phase and envelope of the rhythm's fluctu ations are shown to depend on only two meta-parameters that combine all th e network parameters. They allow us to identify different regimes of ampl itude excursions\, and to highlight the supportive role that network finit e-size effects and noisy inputs to the EI network can have. We discuss ho w burst attributes\, such as their durations and peak frequency content\, depend on the network parameters. We also show how to extend this formali sm to the coupling of brain rhythms from different areas\, and the importa nce of noise for determining the phase difference. In practice\, the prob lem above follows the a priori challenge of fitting such E-I spiking netwo rks to single neuron or population data. Thus\, the second part of the ta lk will discuss a novel method to fit mesoscale dynamics using single neur on data along with a low-dimensional\, and hence statistically tractable\, single neuron model. The mesoscopic representation is obtained by approx imating 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 connectivit y parameters given this activity\, which can then be used to either optimi ze parameters by gradient ascent on the log-likelihood\, or to perform Bay esian inference using Markov Chain Monte Carlo (MCMC) sampling. We illust rate this approach using an E-I network of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived. We sh ow that both single-neuron and connectivity parameters can be adequately r ecovered from simulated data.\n\nREFERENCES:\nArthur Powanwe and Andre Lon gtin\, Scientific Reports 2019\;\nAlexandre René\, André Longtin and Jak ob Macke\, Neural Computation 2020.\nFUNDING: NSERC Canada.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Yoichiro Mori (University of Pennsylvania) DTSTART:20200529T161500Z DTEND:20200529T174500Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/6 DESCRIPTION:Title: Cell volume control and osmosis-driven cell movement< /a>\nby Yoichiro Mori (University of Pennsylvania) as part of CRM-CAMBAM S eminar Series\n\n\nAbstract\nElectrolyte and cell volume regulation is ess ential in physiological systems. After a brief introduction to cell volum e control and electrophysiology\, I will discuss the classical pump-leak m odel of electrolyte and cell volume control. It will be shown that thermo dynamic considerations lead to a new perspective of cell volume control. This classical model will then be generalized to a model with spatial exte nt (a system of partial differential equations) modeling cell-level electr odiffusive and osmotic phenomena. A simplified version of this model will then be applied to study osmosis-driven cell movement. Osmosis-driven an d the conventional actin-driven cell movement will be compared theoretical ly and computationally in terms of its properties\, focusing in particular on energy expenditure.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Cambam Zoomposium (Several Universities) DTSTART:20200605T140000Z DTEND:20200605T180000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/7 DESCRIPTION:Title: Cambam Zoomposium: Multiple Timescales in Neuronal an d Other Systems\nby Cambam Zoomposium (Several Universities) as part o f CRM-CAMBAM Seminar Series\n\nLecture held in En ligne/Web.\n\nAbstract\n The generation of neuronal activity – from spikes\, to bursts\, to multi -phase rhythms –\nfundamentally involves the interaction of processes th at evolve on widely disparate timescales. As\na result\, advances in theor etical neuroscience and in methods for the analysis of multiple timescale\ ndynamics have emerged synergistically\, with experimental observations dr iving theoretical\ndevelopments and with theoretical advances yielding new explanations for data. In this session\,\nspeakers will present work feat uring advances on both sides of this partnership\, which highlights\nnew f indings about neuronal and other biological systems together with the mode rn approaches to\nmultiple timescale analysis that underlie these results. \n\nCohosted by the University of Waterloo and the Fields Institute. \nFir st Session (10:00 – 11:30 AM - EDT)\nSecond Session (12:30 – 2:00 PM - EDT)\nFull Programm at: http://www.crm.umontreal.ca/2020/Zoomposium20/ind ex_e.php\nURL: https://umontreal.zoom.us/j/99878233014?pwd=bS9xSXc5c2o2VnV UTkFKOFUyVTc4dz09 - \nMeeting ID: 998 7823 3014 - Password: 609607\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/7/ END:VEVENT BEGIN:VEVENT SUMMARY:James Sneyd (University of Auckland) DTSTART:20200619T200000Z DTEND:20200619T210000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/8 DESCRIPTION:Title: Modeling Calcium Signaling in Live Animals\nby Ja mes Sneyd (University of Auckland) as part of CRM-CAMBAM Seminar Series\n\ n\nAbstract\nThe vast majority of previous experimental and theoretical wo rk on calcium signalling has been in cell lines\, cultured cells\, or\, mo re recently\, in whole organs. The underlying assumption of these studies is that the mechanisms that control calcium signalling in a live animal ar e essentially similar\, and one can extrapolate from one to the other.\n\n Although this assumption is\, to a large extent\, valid and useful\, recen t measurements of cytosolic calcium oscillations in salivary acinar cells from a live mouse have necessitated a major rethink of the mechanisms unde rlying whole-cell calcium responses and water transport in salivary cells. \n\nWe shall present these new experimental data\, and show how previous m odels have needed to be significantly modified in order to understand and explain these new results.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/8/ END:VEVENT BEGIN:VEVENT SUMMARY:Philip Maini (University of Oxford) DTSTART:20200731T163000Z DTEND:20200731T173000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/9 DESCRIPTION:Title: Modelling collective cell movement in biology and med icine\nby Philip Maini (University of Oxford) as part of CRM-CAMBAM Se minar Series\n\n\nAbstract\nCollective cell movement occurs throughout bio logy and medicine and there are many common features shared across differe nt 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\, phenome nological snail-trail model\; (ii) agent-based models for cranial neural c rest cell migration in a collaboration with experimental biologists that h as revealed a number of new biological insights.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/9/ END:VEVENT BEGIN:VEVENT SUMMARY:Thomas Hillen (University of Alberta) DTSTART:20200626T161500Z DTEND:20200626T174500Z DTSTAMP:20260422T212831Z 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 clinic al evidence suggests that the immune response to cancer is not exclusively anti-tumor. In fact\, several pro-tumor effects of the immune system hav e been identified\, such as production of growth factors\, establishment o f angiogenesis\, inhibition of immune response\, initiation of cell moveme nt and metastasis\, and establishment of metastatic niches. Based on expe rimental data\, we develop a mathematical model for the immune-mediated th eory of metastasis\, which includes anti- and pro-tumor effects of the imm une system. The immune-mediated theory of metastasis can explain dormancy of metastasis and metastatic blow-up after resection of the primary tumor . It can explain increased metastasis at sites of injury\, and the relati vely poor performance of Immunotherapies\, due to pro-tumor effects of the immune system. Our results suggest that further work is warranted to ful ly elucidate and control the pro-tumor effects of the immune system in met astatic cancer. (with Adam Rhodes)\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/10 / END:VEVENT BEGIN:VEVENT SUMMARY:José Antonio Carrillo (University of Oxford) DTSTART:20200710T161500Z DTEND:20200710T174500Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/11 DESCRIPTION:Title: Attractive-repulsive models in collective behavior a nd applications\nby José Antonio Carrillo (University of Oxford) as p art of CRM-CAMBAM Seminar Series\n\n\nAbstract\nWe discuss microscopic and continuum cell-cell adhesion models and their derivation based on the und erlying microscopic assumptions. We analyse the behavior of these models at the microscopic level based on the concept of H-stability of the intera ction potential. We will derive these macroscopic limits via mean-field a ssumptions. We propose an improvement on these models leading to sharp fr onts and intermingling invasion fronts between different cell type populat ions. The model is based on basic principles of localized repulsion and n onlocal attraction due to adhesion forces at the microscopic level. The n ew model is able to capture both qualitatively and quantitatively experime nts 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 areas of tissue growth in developmental biology. We will analyse the mathematical properties of the resulting aggregation-diffusion and reaction-diffusion systems based on variational tools.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/11 / END:VEVENT BEGIN:VEVENT SUMMARY:Carson Chow (National Institute of Diabetes and Digestive and Kidn ey Diseases) DTSTART:20200703T161500Z DTEND:20200703T174500Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/12 DESCRIPTION:Title: Global predictions of unreported SARS-CoV2 infection from observed COVID-19 cases\nby Carson Chow (National Institute of D iabetes and Digestive and Kidney Diseases) as part of CRM-CAMBAM Seminar S eries\n\n\nAbstract\nIn the absence of full scale serological testing\, es timation of infectiousness and fatality of the SARS-CoV-2 virus in the COV ID-19 global pandemic is complicated by ascertainment bias resulting from not all infected individuals being detected and recorded as COVID-19 cases . Here\, I will outline a modeling strategy to obtain more plausible esti mates of the true values of key epidemiological variables by fitting a set of mechanistic Bayesian latent-variable SIR models to confirmed COVID-19 cases\, deaths\, and recoveries\, for all regions (countries and US states ) independently.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/12 / END:VEVENT BEGIN:VEVENT SUMMARY:Sue Ann Campbell (University of Waterloo) DTSTART:20200717T161500Z DTEND:20200717T174500Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/13 DESCRIPTION:Title: Modulation of synchronization by a slowly varying M- current\nby Sue Ann Campbell (University of Waterloo) as part of CRM-C AMBAM Seminar Series\n\n\nAbstract\nThe neurotransmitter acetylcholine has been shown to modulate the firing properties of several types of neurons through the down-regulation of voltage dependent potassium currents such a s the muscarine-sensitive M-current. In particular\, experimental work ha s shown that this current can switch the phase resetting curves from type I to type II and computational models have studied the resulting change in the synchronization of networks of such neurons. In the brain\, levels o f acetylcholine change with activity. For example\, acetylcholine is high er during waking and REM sleep and low during slow wave sleep. Thus an ac curate model of the effects of acetylcholine should include slow variation of this neurotransmitter. In the first part of the talk we use normal fo rm theory to show how the $M$-current affects the bifurcation structure of any conductance-based neuron model. In particular\, we show that the $M$ -current induces two co-dimension two bifurcation which cause the model to transition from a class I to class II oscillator. In the second part 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 model. We show that as the current is downregulated or upregulated the phase mode l passes through two pitchfork bifurcations\, which are associated in the full model with the transition between synchronous and asynchronous behavi our. The criticality of the pitchfork bifurcations depends on the neural model and whether the coupling is inhibitory or excitatory. We show that periodic slow passage through these pitchfork bifurcation leads to a hyste resis loop and study how different properties of the model affect this loo p and the transitions between synchronous and asynchronous behaviour. Num erical simulations confirm the results of the phase model analysis. This is joint work with Victoria Booth\, Xueying Wang and Isam Al-Darbasah.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/13 / END:VEVENT BEGIN:VEVENT SUMMARY:Frithjof Lutscher (University of Ottawa) DTSTART:20200724T161500Z DTEND:20200724T174500Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/14 DESCRIPTION:Title: A seasonal hybrid model for the evolution of floweri ng onset in plants\nby Frithjof Lutscher (University of Ottawa) as par t of CRM-CAMBAM Seminar Series\n\n\nAbstract\nIn temperate climates with s trong seasonal changes\, plants need to \ndecide how to allocate resources to vegetative growth or to reproduction \nduring a potentially short favo rable season. Many plants switch from \nmostly vegetative growth early in the season to mostly reproduction \nlate in the season. The onset of flowe ring marks the transition between \nthe two phases. Later onset of floweri ng typically implies a larger size \nat maturity and higher reproductive c apacity. At the same time\, it limits \nthe remaining time in the favorabl e season for pollination and seed \ndevelopment. Hence\, plants face a tra de-off for some optimal flowering onset. \nIn this talk\, I will present a seasonal hybrid model for the density of a \nplant population\, structure d by onset of flowering as a trait. I will apply\ntwo complementary approc hes to analyze the system. Overall\, I find that \nevolution favours some intermediate flowering times. This is joint work with \nTricia Morris.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/14 / END:VEVENT BEGIN:VEVENT SUMMARY:Jonathan Rubin (University of Pittsburgh) DTSTART:20200807T161500Z DTEND:20200807T174500Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/15 DESCRIPTION:Title: Multiple roles of synaptic “inhibition” & how th ey arise in decision-making pathways in the basal ganglia\nby Jonathan Rubin (University of Pittsburgh) as part of CRM-CAMBAM Seminar Series\n\n \nAbstract\nThis talk concerns topics in mathematical neuroscience but wil l not assume any specific knowledge of neuroscience. It should be of inte rest to anyone who would like to learn more about general ideas of mathema tical neuroscience or about certain specific topics: integration of multip le streams of inhibition in neural circuits\, the role of the basal gangli a in decision-making and action selection\, cortico-striatal plasticity\, the impact of time-varying reversal potentials\, and mechanisms of neural synchronization and oscillations. The phrase “inhibition” suggests a holding back or suppression of activity. It has long been recognized tha t the roles of synaptic inhibition in neuronal circuits can be more divers e\, however\, and include promotion of activity through effects such as po st-inhibitory rebound and disynaptic disinhibition. The basal ganglia (BG ) is a hub for the reward signal dopamine and is believed to be involved i n decision-making and action selection. Interestingly\, most synaptic pat hways within the BG involve neurotransmitters that are traditionally inhib itory. In the first section of my talk\, I will introduce this circuitry and present modeling of how these pathways can collaborate to produce rewa rd-driven action. I will also present joint work with Tim Verstynen\, Cat i Vich and our trainees\, which (1) introduces a way to map between biolog ically detailed models and more abstract decision-making models and (2) su ggests how different BG inhibitory neurons serve different roles in terms of evidence accumulation and decision thresholds. In the second section o f my talk\, I will present work with postdoc Ryan Phillips and our collabo rator Aryn Gittis in which we model the integration of two inhibitory path ways by BG output neurons. Our modeling takes into account chloride dynam ics and its impact on synaptic reversal potentials and shows how these pat hways can actually induce excitatory effects\, can contribute to synchroni zation and oscillations\, and can affect action selection\, which may be r elated to Parkinson’s disease.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/15 / END:VEVENT BEGIN:VEVENT SUMMARY:Angela Reynolds (Virginia Commonwealth University) DTSTART:20200828T163000Z DTEND:20200828T173000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/16 DESCRIPTION:Title: Modeling the Innate Immune Cells\nby Angela Reyn olds (Virginia Commonwealth University) as part of CRM-CAMBAM Seminar Seri es\n\n\nAbstract\nDuring an inflammatory response there is a complex casca de of reactions\, which may lead to health or sustained inflammation durin g many diseases and processes\, such as wound healing and infections. In o rder to understand how the immune cells involved in the inflammatory respo nse contribute to the disease progression\, we have developed various mode ls for the immune cell dynamics. In this talk we will start by analyzing a reduced model and then adapting this model to various diseases by increas ing the complexity of the immune cells in the model to gain more biologica l insight. Using bifurcations\, parameter estimation\, and sensitivity ana lysis\, we will explore predictors of outcome and how modulating the immun e response dynamics can alter patient outcome.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/16 / END:VEVENT BEGIN:VEVENT SUMMARY:Thomas Bury (McGill University) DTSTART:20200727T130000Z DTEND:20200727T173000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/17 DESCRIPTION:Title: Interactive data visualisations in Python\nby Th omas Bury (McGill University) as part of CRM-CAMBAM Seminar Series\n\n\nAb stract\nModern scientific methods give rise to vast quantities of data. C reating effective visualisations is essential for both presentation and mo re importantly exploration of the data. This is no easy task when the dat a contains dozens of variables and millions of entries. Traditional visua lisations are static\, that is\, what the user sees is what the user gets. Using interactive data visualisations allows the user to vary parameters \, honing in on subsections of the data\, or switching between different p lot types - all without touching the code. This allows for rapid explorat ion of the data and seamless sharing amongst collaborators\, who only requ ire a web browser to open the visualisation. This workshop will equip par ticipants with the skills required to begin creating interactive visualisa tions in Python. The format will be highly interactive\, with alternation between demonstrations by the instructor and participants working through their own Jupyter notebook (provided in advance). Participants will come away having made several of their own visualisations of either a large pu blic dataset\, or their own dataset if they would like to bring one. An e xample of what can be achieved using these tools can be found at the follo wing link\, where data output from a model of a cardiac arrhythmia is inte ractively viewed and analysed. \nhttps://modulated-parasystole.herokuapp. com/\n\nEn ligne/Web - Pour vous inscrire\, veuillez communiquer avec /Fo r registration\, please contact: thomas.bury@mcgill.ca\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/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:20260422T212831Z UID:AppliedMathInBiosAndMedecine/18 DESCRIPTION:Title: CRM-CAMBAM Mini-workshop in Mathematical Biology \nby Tyler Cassidy\, Francesca Scarabel\, Shaza Alsibaai (See Affiliations in the talk comments box) as part of CRM-CAMBAM Seminar Series\n\n\nAbstr act\nSee details at : http://www.crm.umontreal.ca/2020/Ateliers20/\nSummar y\nIndividual level behaviour and processes\, such as reproduction\, movem ent\, growth\, and death\,\ndrive population level dynamics. Typically\, m athematical modellers homogenize these individual level behaviours by cons idering the “average” behaviour and making arguments that lead\nto int o ordinary differential equation models for population level dynamics. In this workshop\,\nwe introduce a modelling methodology that begins with the biological considerations underlying the individual level behaviour. Thro ughout careful book-keeping\, we show how to use\nthese individual level b ehaviours to derive population level dynamics. By using the classic SIR\nm odel from epidemiology\, we show how considering more realistic infection dynamics naturally\nlead to functional equations\, and illustrate the anal ytical and numerical techniques that allow\nmodellers to derive biological information from the population level dynamics.\nThis workshop will provi de a “users guide” overview to the use of structured population models \nand is intended as a gentle introduction to the use of delay equations i n mathematical biology.\nParticipants will learn to build population level models from individual level behaviours\, will\nbe introduced to the anal ytical and numerical skills used to derive biological information from\npo pulation level dynamics\, and will be able to identify the similarities an d differences between\nstructured population models and ordinary different ial equation models. The workshop will be\na mix of worked analytical and numerical examples and will include a refresher of the necessary\nmathemat ical techniques.\n\nExpected Background\nWe expect that the workshop will be accessible to students with a background in undergraduate\ndifferential equations and numerical methods\, and will refresh the relevant mathemati cal theory\nas necessary throughout the workshop.\n\nSpeakers\nTyler Cassi dy\nPostdoctoral Researcher\, Theoretical Biology and Biophysics\, Los Ala mos National Laboratory.\n\nFrancesca Scarabel\nPostdoctoral Researcher\, Laboratory for Industrial and Applied Mathematics\, York University.\n\nSh aza Alsibaai\nPhD Student\, Department of Mathematics and Statistics\, McG ill University\n\nRegistration: https://docs.google.com/forms/d/e/1FAIpQLS dA6QKYjKEGXr60wc7OUkw785Z1BkX9LrPKBhTXQEz5di8yLQ/viewform\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/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:20260422T212831Z UID:AppliedMathInBiosAndMedecine/19 DESCRIPTION:Title: Computational modelling to study cancer biology and treatments\nby Morgan Craig (Université de Montréal/Centre de recher che CHUSJ)\, Adrianne Jenner (Université de Montréal/Centre de recherche CHUSJ)\, Paul Macklin (Indiana University)\, Randy Heiland (Indiana Unive rsity)\, and Pantea Poolavand (Bloomington) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nSee details at: http://www.crm.umontreal.ca/2020/Ate liers20/\nBACKGROUND\nCancer biology and treatment involves complex\, dyna mic interactions between cancer cells\, the tumour microenvironment\, and therapeutic molecules. Quantitative approaches combining mechanistic disea se modelling and computational strategies are increasingly leveraged to ra tionalize pre-clinical and clinical studies\, and to establish effective t reatment strategies. In this\nway\, mathematical approaches lay the founda tion for computational “virtual laboratories” that offer fully control led\, and non-invasive conditions in which we can investigate emergent cli nical behaviours and interrogate new therapeutic strategies.\nAs an introd uction to such virtual laboratories\, this workshop will provide an overvi ew of techniques used in computational oncology\, with a focus on in silic o clinical trials and agent-based models (ABMs). Virtual (or in silico) cl inical trials are useful computational platforms that help distinguish mec hanisms of therapeutic successes and failures\, stratify patient risk clas ses based on an individual’s physiology\, and optimize drug-specific par ameters. In these platforms\, in silico patients are generated by drawing from distributions of possible patient characteristics and used to form vi rtual clinical trials\, in which new treatment strategies can be evaluated prior to human trials. Data fitting and optimisation techniques are corne rstones of this computational platform and are used to generate realistic virtual patients and evaluate individualised therapies. ABMs are a computa tional formalism that describes the way individual agents (e.g. cancer cel ls) interact through probability distributions based on defined characteri stics that have contributed significant insights into cancer biology at th e intra-patient tissue level. In oncology\, this technique has been applie d to model spatial tumour formation\, tumour cell heterogeneity\, and the dynamics of treatment in the tumour microenvironment. Modelling individual cells as agents allows for direct translation of biological\nobservation into simulation rules and\, like virtual clinical trials\, the investigati on of new hypotheses and treatment strategies.\n\nIn particular\, this wor kshop will address:\n• the optimization of parameter ranges to generate virtual patients or treatment schedules using a variety of techniques\,\ni ncluding simulated annealing\, least-squares nonlinear optimisation\, grad ient-based descent\, and genetic algorithms.\n• the translation between ABMs and PDEs\n• how to code heterogenous tumour environments into an AB M using an open-source software known as PhysiCell\nWorkshop participants will have the opportunity to see how each of these techniques are applied in computational oncology and learn how to employ them on experimental or generated data in Matlab and in C++. By the end of this workshop\, partici pants will have a comprehensive understanding of computational modelling i n oncology\, the explicit knowledge for how to design\, code\, and simulat e 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 Professor\, Université de Montréal/Centre de recherche CHUSJ\, Montréal\, Canada\n Adrianne Jenner\, Postdoctoral Fellow\, Université de Montréal/Centre de recherche CHUSJ\, Montréal\, Canada\nPaul Macklin\, Associate Professor\ , Indiana University\, Bloomington\, USA\nRandy Heiland\, Indiana Universi ty\, Bloomington\, USA\nPantea Poolavand\, University of Sydney\, Sydney\, Australia\n\nTICKETS\nFree registration for the event can be found at:\nh ttps://www.eventbrite.com/e/computational-modelling-to-study-cancer-biolog y-and-treatments-tickets-113637272140\nMake sure to go through the “pre- flight checklist” available on the Eventbrite page and download the appr opriate programs and software to run PhysiCell. For the Matlab tutorial\, you will need to have Matlab on your computer.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/19 / END:VEVENT BEGIN:VEVENT SUMMARY:Mark Chaplain (University of St. Andrews) DTSTART:20200814T163000Z DTEND:20200814T173000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/20 DESCRIPTION:Title: A Mathematical Framework for Modelling the Metastati c Spread of Cancer\nby Mark Chaplain (University of St. Andrews) as pa rt of CRM-CAMBAM Seminar Series\n\n\nAbstract\nInvasion and metastasis are two of the hallmarks of cancer and are intimately connected processes. I nvasion\, as the name suggests\, involves cancer cells spreading out from the main cancerous mass into the surrounding tissue\, through production a nd secretion of matrix degrading enzymes. Metastatic spread is the proces s whereby invasive cancer cells enter nearby blood vessels (or lymph vesse ls)\, are carried around the body in the main circulatory system and then succeed in escaping from the circulatory system at distant secondary sites where the growth of the cancer starts again. It is this metastatic spre ad that is responsible for around 90% of deaths from cancer. To shed ligh t on the metastatic process\, we present a mathematical modelling framewor k that captures for the first time the interconnected processes of invasio n and metastatic spread of individual cancer cells in a spatially explicit manner—a multigrid\, hybrid\, individual-based approach. This framewor k accounts for the spatiotemporal evolution of mesenchymal- and epithelial -like cancer cells\, membrane-type-1 matrix metalloproteinase (MT1-MMP) an d the diffusible matrix metalloproteinase-2 (MMP-2)\, and for their intera ctions with the extracellular matrix. Using computational simulations\, w e demonstrate that our model captures all the key steps of the invasion-me tastasis cascade\, i.e. invasion by both heterogeneous cancer cell cluste rs and by single mesenchymal-like cancer cells\; intravasation of these cl usters and single cells both via active mechanisms mediated by matrix-degr ading enzymes (MDEs) and via passive shedding\; circulation of cancer cell clusters and single cancer cells in the vasculature with the associated r isk of cell death and disaggregation of clusters\; extravasation of cluste rs and single cells\; and metastatic growth at distant secondary sites in the body.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/20 / END:VEVENT BEGIN:VEVENT SUMMARY:Jonathan Michaels (University of Western Ontario) DTSTART:20210406T160000Z DTEND:20210406T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/21 DESCRIPTION:Title: Combining deep learning and primate electrophysiolog y to understand reach and grasp control\nby Jonathan Michaels (Univers ity of Western Ontario) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract \nWhether it’s picking up a cup or deftly slotting a key into a lock\, w e appear to move our arms and hands with ease. While humans do these task s 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 goal of my research is to understand how the nervous system controls our arms and hands with this incredible level of flexibility\, how sensory information supports this control\, and what computational principles guide the acqui sition of these skills. In this talk\, I will present my recent work deve loping neural network models of the primate grasping system to provide in sight into how the brain coordinates grasping and lay out how we can use t ools from AI to help understand the brain.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/21 / END:VEVENT BEGIN:VEVENT SUMMARY:Stephen Coombes (University of Nottingham) DTSTART:20210427T160000Z DTEND:20210427T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/22 DESCRIPTION:Title: Next generation neural field modelling\nby Steph en Coombes (University of Nottingham) as part of CRM-CAMBAM Seminar Series \n\n\nAbstract\nNeural mass models have been actively used since the 1970s to model the coarse-grained activity of large populations of neurons and synapses.  They have proven especially fruitful for understanding brain r hythms.  However\, although motivated by neurobiological considerations t hey are phenomenological in nature\, and cannot hope to recreate some of t he rich repertoire of responses seen in real neuronal tissue.  In this ta lk I will discuss a simple spiking neuron network model that has recently been shown to admit to an exact mean-field description for synaptic intera ctions.  This has many of the features of a neural mass model coupled to an additional dynamical equation that describes the evolution of populatio n synchrony.  I will show that this next generation neural mass model is ideally suited to understanding beta-rebound. This is readily observed in MEG recordings whereby motor action causes a drop in the beta power band attributed to a loss of network synchrony.  Existing neural mass models a re unable to capture this phenomenon (event related de-synchrony) since th ey do not track any notion of network coherence (only firing rate).  I wi ll spend the latter part of my talk discussing patterns and waves in a spa tially continuous non-local extension of this model\, highlighting its use fulness for large scale cortical modelling.   \n\nIf you would like a no t 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 generation neur al mass and field modelling\, Journal of Neurophysiology\, Vol 123\, 726 –742 https://doi.org/10.1152/jn.00406.2019\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/22 / END:VEVENT BEGIN:VEVENT SUMMARY:Arjun Krishnaswamy (McGill University) DTSTART:20210420T160000Z DTEND:20210420T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/23 DESCRIPTION:Title: Molecular cues for the assembly and function of reti nal circuits\nby Arjun Krishnaswamy (McGill University) as part of CRM -CAMBAM Seminar Series\n\n\nAbstract\nIntricate patterns of connectivity a mong neurons are critical for our abilities\, and all too often\, disabili ties. Our goal is to understand how such patterns\, called circuits\, aris e by studying the assembly of neural circuits in the mouse retina. Here\, the specific connections among ~120 retinal interneurons and ~45 retinal g anglion cells create circuits that detect visual features such as motion d irection. Today\, I will present our recent work showing that members of t he cadherin and immunoglobulin superfamilies play a critical role in estab lishing such specific connections and creating circuits that detect featur es in the visual scene.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/23 / END:VEVENT BEGIN:VEVENT SUMMARY:Maryam M. Shanechi (University of Southern California) DTSTART:20210511T160000Z DTEND:20210511T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/24 DESCRIPTION:Title: Dynamical modeling\, decoding\, and control of multi scale brain networks: from motor to mood\nby Maryam M. Shanechi (Unive rsity of Southern California) as part of CRM-CAMBAM Seminar Series\n\n\nAb stract\nIn this talk\, I will discuss our work on dynamical modeling\, dec oding\, and control of large-scale brain network activity underlying natur alistic motor and mood states. I present a multiscale dynamical modeling framework that allows us to decode human mood variations and identify brai n regions that are most predictive of mood. I then develop a system ident ification approach that can predict multiregional brain network dynamics ( output) in response to time-varying electrical stimulation (input) toward enabling closed-loop control of brain activity. Further\, I extend our mo deling framework to enable dissociating and uncovering behaviorally releva nt neural dynamics that can otherwise be missed\, such as those during nat uralistic movements. Finally\, I show how our framework can model brain n etwork activity across multiple spatiotemporal scales simultaneously\, thu s uncovering multiscale neural dynamics that explain naturalistic reach-an d-grasp behavior. These dynamical models\, decoders\, and controllers can provide new neuroscientific insight and enable brain-machine interfaces f or personalized therapy in neurological and neuropsychiatric disorders.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/24 / END:VEVENT BEGIN:VEVENT SUMMARY:Marc Timme (Dresden) DTSTART:20211005T160000Z DTEND:20211005T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/25 DESCRIPTION:Title: Model-free inference of network structural features from observed dynamics\nby Marc Timme (Dresden) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nThe dynamics of biological networks enables the function of a variety of systems we rely on every day\, from gene 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 t ime series data from (some) units are available. Here we report on recent progress on model-free inference of network structural features from obse rved dynamics. First\, we demonstrate how to identify the number N of dyn amical variables making up a network -- arguably its most fundamental prop erty -- from recorded time series of only a small subset of nThe MeshCODE theory – does our brain store memorie s in a binary format?\nby Ben Goult (University of Kent) as part of CR M-CAMBAM Seminar Series\n\n\nAbstract\nCell adhesion to the extracellular matrix (ECM)\, mediated by integrins\, is exquisitely sensitive to biochem ical\, structural\, and mechanical features of the ECM. Talin\, the primar y link between integrins and the actin cytoskeleton\, coordinates the bind ing of a wide range of cytoskeletal and signaling adaptors in a force-depe ndent manner. Our work has defined talin as a major mechanosensitive signa lling hub. More recently we have discovered that talin has “molecular me mory” and so provides organisms with a way to store data\, through persi stent alterations in protein conformation. In this talk I discuss the impl ications of these findings and describe a novel role for integrin adhesion s in data-storage leading to a novel theory of how memories are stored in our brain. The MeshCODE theory presented here provides an original concept for the molecular basis of memory storage. I propose that memory is bioch emical in nature\, written in the form of different protein conformations in each of the trillions of synapses. Based on established biophysical pri nciples\, a mechanical basis for memory would provide a physical location for data storage in the brain. Furthermore\, the conversion and storage of sensory and temporal inputs into a binary format identifies an addressabl e read/write memory system supporting the view of the mind as an organic s upercomputer.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/26 / END:VEVENT BEGIN:VEVENT SUMMARY:Guillaume Lajoie (University of Montreal) DTSTART:20211214T170000Z DTEND:20211214T180000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/27 DESCRIPTION:Title: Top-down optimization recovers biological coding pri nciples of single-neuron adaptation in RNNs\nby Guillaume Lajoie (Univ ersity of Montreal) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nSp ike frequency adaptation (SFA) is a well studied physiological mechanism w ith established computational properties at the single neuron level\, incl uding noise mitigating effects based on efficient coding principles. Netwo rk models with adaptive neurons have revealed advantages including modulat ion of total activity\, supporting Bayesian inference\, and allowing compu tations over distributed timescales. Such efforts are bottom-up\, modeling adaptive mechanisms from physiology and analysing their effects. How top- down environmental and functional pressures influence the specificity of a daptation remains largely unexplored.\n\nIn this talk\, I will discuss wor k where we use deep learning to uncover optimal adaptation strategies from scratch\, in recurrent neural networks (RNNs) performing perceptual tasks . In our RNN model\, each neuron's activation function (AF) is taken from a parametrized family to allow modulation mimicking SFA\, and an adaptatio n controller is trained end-to-end to control an AF in real time\, based o n pre-activation inputs to a neuron. Remarkably\, we find emergent adaptat ion strategies that implement SFA mechanisms from biological neurons\, inc luding fractional input differentiation. This suggests that even in simpli fied models\, environmental pressures and objective-based optimization are enough for sophisticated biological mechanisms to emerge.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/27 / END:VEVENT BEGIN:VEVENT SUMMARY:Gary Bader (University of Toronto) DTSTART:20220125T170000Z DTEND:20220125T180000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/28 DESCRIPTION:by Gary Bader (University of Toronto) as part of CRM-CAMBAM Se minar Series\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/28 / END:VEVENT BEGIN:VEVENT SUMMARY:Becca Asquith (Imperial College London) DTSTART:20220201T170000Z DTEND:20220201T180000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/29 DESCRIPTION:by Becca Asquith (Imperial College London) as part of CRM-CAMB AM Seminar Series\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/29 / END:VEVENT BEGIN:VEVENT SUMMARY:Josh McDermott (MIT) DTSTART:20220222T170000Z DTEND:20220222T180000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/30 DESCRIPTION:by Josh McDermott (MIT) as part of CRM-CAMBAM Seminar Series\n \nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/30 / END:VEVENT BEGIN:VEVENT SUMMARY:Antoine Allard (University of Laval) DTSTART:20220322T160000Z DTEND:20220322T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/31 DESCRIPTION:by Antoine Allard (University of Laval) as part of CRM-CAMBAM Seminar Series\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/31 / END:VEVENT BEGIN:VEVENT SUMMARY:Allison Shaw (University of Minnesota) DTSTART:20220405T160000Z DTEND:20220405T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/32 DESCRIPTION:Title: Parasites\, animal migration\, and how perspective s hapes science\nby Allison Shaw (University of Minnesota) as part of CR M-CAMBAM Seminar Series\n\n\nAbstract\nAnimal migration (round-trip\, pred ictable movements) takes individuals across space and time\, bringing them into contact with new communities of organisms. In particular\, migrator y movements can be shaped by the costs and risk of parasite transmission. Here\, I will present some of our work using mathematical models to under stand how parasites shape the evolution of animal migration. We use adapt ive dynamics to determine the evolutionarily stable strategy of migratory tendency\, given different infection scenarios.  Finally\, I’ll use thi s example to argue that research is shaped by the identities\, perspective s\, and experiences of the scientists who conduct it. Perspective shapes the questions we ask\, the systems we work in\, and the processes we choos e to explore (as well as the ones we choose to ignore).\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/32 / END:VEVENT BEGIN:VEVENT SUMMARY:Sam Gershman (Harvard University) DTSTART:20220412T160000Z DTEND:20220412T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/33 DESCRIPTION:by Sam Gershman (Harvard University) as part of CRM-CAMBAM Sem inar Series\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/33 / END:VEVENT BEGIN:VEVENT SUMMARY:Michael Frank (Stanford) DTSTART:20220920T160000Z DTEND:20220920T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/34 DESCRIPTION:Title: Bigger data about smaller people: Studying language learning at scale\nby Michael Frank (Stanford) as part of CRM-CAMBAM S eminar Series\n\n\nAbstract\nEvery typically developing child learns to ta lk\, but children vary tremendously in how and when they do so. What predi cts this variability\, and what is consistent across children and across l earners of different languages? In this talk\, I’ll describe our efforts to create predictive models of early language learning as a way of formal izing hypotheses in this space. This goal has led us to create open data r esources like Wordbank\, childes-db\, and Peekbank that capture data from tens of thousands of children learning dozens of different languages.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/34 / END:VEVENT BEGIN:VEVENT SUMMARY:Amy Goldberg (Duke University) DTSTART:20220927T160000Z DTEND:20220927T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/35 DESCRIPTION:Title: Evolutionary perspectives on malaria: humans\, prima tes\, and the parasites we share\nby Amy Goldberg (Duke University) as part of CRM-CAMBAM Seminar Series\n\n\nAbstract\nClassically called one o f the strongest selective pressures in human evolution\, I will discuss ne w computational methods we are developing to understand the ongoing host a nd pathogen pressures shaping malaria. First\, leveraging the added infor mation that distributions of genetic ancestry provide\, we infer rapid ada ptation to P. vivax malaria in humans from the islands of Cabo Verde. We describe a suite of tools that are broadly applicable to study post-admix ture adaptation. Then\, we consider the broader spectrum of malaria paras ites across primates to begin to ask why some impact human evolution and d isease burden more than others. To do this\, we will first need new popul ation-genetic simulation methods to interpret patterns of variation in mal aria parasites given their complex lifecycles.\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/35 / END:VEVENT BEGIN:VEVENT SUMMARY:Peter Harrison (Cambridge) DTSTART:20221101T160000Z DTEND:20221101T170000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/36 DESCRIPTION:by Peter Harrison (Cambridge) as part of CRM-CAMBAM Seminar Se ries\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/36 / END:VEVENT BEGIN:VEVENT SUMMARY:James Faeder (University of Pittsburgh) DTSTART:20221108T170000Z DTEND:20221108T180000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/37 DESCRIPTION:by James Faeder (University of Pittsburgh) as part of CRM-CAMB AM Seminar Series\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/37 / END:VEVENT BEGIN:VEVENT SUMMARY:John Murray (Yale University) DTSTART:20221129T170000Z DTEND:20221129T180000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/38 DESCRIPTION:by John Murray (Yale University) as part of CRM-CAMBAM Seminar Series\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/38 / END:VEVENT BEGIN:VEVENT SUMMARY:Amber Smith (Duke University) DTSTART:20221206T170000Z DTEND:20221206T180000Z DTSTAMP:20260422T212831Z UID:AppliedMathInBiosAndMedecine/39 DESCRIPTION:by Amber Smith (Duke University) as part of CRM-CAMBAM Seminar Series\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AppliedMathInBiosAndMedecine/39 / END:VEVENT END:VCALENDAR