CAIMS 2018 Annual Meeting

Size: px

Start display at page:

Download "CAIMS 2018 Annual Meeting"

Raymond Martin
5 years ago
Views:

CAIMS 2018 Annual Meeting June 4-7, 2018,

Xanthos Department of Mathematics, Ryerson

1 CAIMS 2018 Annual Meeting June 4-7, 2018, Ryerson University, Toronto, Ontario, Canada Local Organizers: D. Delic, K. Georgiou, J.P. Pascal, K. Rohlf, K. Wilkie, F. Xanthos Department of Mathematics, Ryerson University, Toronto, Canada Plenary and Award Talk Abstracts

2 Plenary Talks: Bringing Modelling and Sequence Data Together in the Context of Infectious Disease Caroline Colijn, Imperial College London Monday June 4, 8h45-9h45, Rehearsal Hall 2 Abstract: New molecular sequencing technologies can offer an unprecedented view of biological diversity and evolution. In principle, this should give us the opportunity to understand population dynamics, ecology and even individual events like disease transmission in much more depth than is possible with conventional data. However, sequence data do not directly reveal population dynamics, ecology or individual-level events. Accordingly, there are exciting opportunities for mathematical research to play a key role in many applications. I will describe two topics in this broad area, both in the context of infectious disease. First, DNA or RNA sequence data contain information about person-to-person transmission events: if two individuals have similar viral genomes, it is possible that one infected the other. But the relationship between sequence similarity and who infected whom is complicated by in-host diversity, timing and the shared ancestry patterns in a set of sequences. I will describe an approach based on colouring phylogenetic trees; it clarifies the constraints on possible transmission events given a set of sequences, and estimates who infected whom (the transmission tree) along with uncertainty. I will also describe a metric for comparing transmission trees. Next, moving to the population level, understanding the dynamics of diverse infections poses significant modelling challenges, as models describing diversity are high-dimensional, hard to specify, and difficult to fit to data. While population dynamic models are widely used to predict the effects of interventions like vaccines or antibiotic treatment, these models can almost never accommodate the rich data on a pathogen s diversity that we can obtain through sequencing. And in contrast, genomic studies tend to be descriptive, and do not seek to model underlying dynamics or make predictions over time. I will describe bridging this gap with a population dynamic model describing strain dynamics in Streptococcus pneumonaie. The model is directly derived from thousands of whole-genome sequences, and a key observation in the data defines the inter-strain interactions. We use the model to design new candidate vaccine strategies that the model predicts could perform better than current vaccine formulations. 2

3 Flows Induced by Quasi-Monochromatic Waves Bruce Sutherland, University of Alberta Monday June 4, 2h30-3h30, Rehearsal Hall 2 Abstract: Driven by buoyancy forces, internal gravity waves move vertically through a fluid whose effective density decreases with height. Unlike most waves, they have the property that periodic internal gravity waves exactly solve the fully nonlinear equations of motion. This has led to some confusion about the role of momentum transport by waves, despite the early work of Bretherton (J. Fluid Mech., 1969) who first showed that the structure of flows induced by waves differs qualitatively depending upon their dimensionality. This talk will revisit the problem of flows induced by internal waves starting with the ansatz that the waves are quasi-monochromatic. This mathematically rigorous, but physically intuitive approach, clearly demonstrates how one-dimensional wavepackets induce uniform flows, two-dimensional wavepackets induce long waves and three-dimensional wavepackets induce a dipole recirculating structure. In the case of wide wavepackets, the induced dipole can co-exist with induced long waves with the transition boundary being well-predicted by theory. In all cases the induced flows significantly modify the evolution of the wavepacket if the waves are of moderately large amplitude, as is well described by the nonlinear Schroedinger equation. Recent extensions examining internal modes in non-uniform stratification will also be discussed. Extended McKean-Vlasov Stochastic Control Problems Beatrice Acciaio, London School of Economics Tuesday June 5, 8h30-9h30, Rehearsal Hall 2 Abstract: I will consider McKean-Vlasov stochastic control problems where the cost functions and the state dynamics depend upon the joint distribution of the controlled state and the control process. First, I will provide a suitable version of the Pontryagin stochastic maximum principle, showing that, in the present general framework, pointwise minimization of the Hamiltonian with respect to the control is not a necessary optimality condition. Then I will take a different perspective, and present a variational approach to study a weak formulation of such control problems, thereby establishing a new connection between those and optimal transport problems on path space. The talk is based on a joint project with J. Backhoff-Veraguas and R. Carmona. 3

4 Cell Polarization and Growth in Yeast Mating Linda Petzold, UC Santa Barbara Tuesday June 5, 2h00-3h00, Rehearsal Hall 2 Abstract: Polarization is an essential behavior of living cells, yet the dynamics of this symmetry-breaking process are not fully understood. We have developed a spatial stochastic model of cellular polarization during mating of Saccharomyces cerevisiae. Specifically we investigated the ability of yeast cells to sense a spatial gradient of mating pheromone and respond by forming a projection in the direction of the mating partner. Our results demonstrated that a spatial stochastic model of polarisome formation can more robustly reproduce two fundamental characteristics observed in wild-type cells: a tightly polarized phenotype and the ability to track moving pheromone input, in comparison with the corresponding deterministic model. Existing models of cell polarization have focused solely on the biochemical signaling system. However, there exists a well-known interplay between the growth of the mating projection and the mechanical forces of the cell wall in determining the shape of the cell. The cell wall of S. cerevisiae both defines its shape and provides the mechanical integrity necessary to sustain the large internal turgor pressure. Under the isotropic push of turgor pressure, polarized expansion occurs via localized assembly of new cell wall material in combination with a simultaneous softening of the cell wall, inducing it to yield and locally expand. Intracellular signaling directs enzymes with the ability to modify cross-linking of polymers in the cell wall to the region of polarization. The resulting mechanical feedback from the wall expansion initiates the delivery of raw material via vesicular transport. To accurately model this complex biological phenomena, we have developed a multiscale computational framework for simulating the coupling of the stochastic dynamics of biochemical reactions involved in shaping walled cells to the mechanical processes of cell wall expansion and growth. Our computational method exploits the time-scale separation between the relatively slow dynamics of the cell wall and the rapid interactions of the intercellular signaling network. Attacking NP-hard Problems William J. Cook, University of Waterloo Wednesday June 6, 2h00-3h00, Rehearsal Hall 2 Abstract: The past several decades have seen an intense study of computational tools for attacking NP-hard models in discrete optimization. We give an overview of this work, discussing current techniques, results, and research directions. The talk will highlight successful approaches adopted in the exact solution of large-scale mixed-integer programming models and the traveling salesman problem. 4

5 CAIMS Cecil-Graham Doctoral Dissertation Award: Social Evolution Under Demographic Stochasticity David McLeod, ETH Zürich Wednesday June 6, 8h30-9h30, Rehearsal Hall 2 Abstract: How social traits such as altruism and spite evolve remains an open question in evolutionary biology. One factor thought to be potentially important is demographic stochasticity. In this talk, I will present a general theoretical analysis of the role of demographic stochasticity in social evolution. I will show that the evolutionary impact of stochasticity depends upon how the social action alters the recipient s life cycle. If the action alters the recipient s death rate, then demographic stochasticity always favours altruism and disfavours spite. On the other hand, if the action alters the recipient s birth rate, then stochasticity can either favour or disfavour both altruism and spite. I will show that which outcome occurs is determined by the underlying population demographics. Finally, I will discuss when demographic stochasticity can reverse the direction of selection upon social traits. These findings are based upon ongoing work with Troy Day. CAIMS-PIMS Early Career Award: Geometry-Preserving Modeling in Geophysical Fluid Dynamics Alex Bihlo, Memorial University Thursday June 7, 11h30-12h30, Rehearsal Hall 2 Abstract: Symmetries and conservation laws are among the many important geometric properties of the governing equations of geophysical fluid dynamics. Unfortunately, they are also among the first properties to be lost once these equations are discretized using conventional discretization techniques. In this talk I will discuss tailored numerical integrators for some of the important models of geophysical fluid dynamics that numerically preserve their symmetries and/or conservation laws. This entails the development of geometric numerical integrators for the resolved grid-scale dynamics as well as geometric parameterization schemes for unresolved subgrid-scale processes. Applications include the modeling of geostrophic turbulence, barotropic eddies in the ocean, and shallow-water dynamics on the plane and on the sphere. 5

AP Biology Essential Knowledge Cards BIG IDEA 1

AP Biology Essential Knowledge Cards BIG IDEA 1 Essential knowledge 1.A.1: Natural selection is a major mechanism of evolution. Essential knowledge 1.A.4: Biological evolution is supported by scientific