Thousands of scientists working at the interface of the mathematical and biological sciences have participated in programs at the Mathematical Biosciences Institute (MBI), where they have found out about the latest advances in their fields. MBI has expanded its program with the MBI Online Colloquium. Now in its second year, this series is available as an online interactive event and as on-demand streaming. The colloquia will cover the many fields of mathematical biology. The goal of this program is twofold: to enable large numbers of researchers to hear about recent advances in the field, and to connect the mathematical biology community worldwide.

The MBI Online Colloquium gives individuals and groups the opportunity to hear from outstanding mathematical biologists and to be an active part of colloquium discussions. You can interact with leading researchers and key opinion leaders from your classroom to the comfort of your own office. If you are unable to make a talk, you can view it on-demand at a later date.

## Current Season

### September 25, 2019

### Rafael Irizarry

Professor and Chair, Department of Data Sciences, Dana-Farber Cancer Institute; Professor of Applied Statistics, Harvard University

#### The Important and Extensive Role of Applied Statistics in the Biological Sciences and Beyond

Statistics has been at the center of many exciting accomplishments of the 21st century, with applied statistics being widely used across several industries and by policy makers. In academia, the number of statisticians becoming leaders in other fields like environmental sciences, human genetics, genomics, and social sciences continues to grow. The unprecedented advances in digital technology during the second half of the 20th century has produced a measurement revolution that is transforming the world. Many areas of science are now being driven by new measurement technologies and many insights are being made by discovery-driven, as opposed to hypothesis-driven, experiments. The current scientific era is defined by its dependence on data and the statistical methods and concepts developed during the 20th century provide an incomparable toolbox to help tackle current challenges. In this talk I will give several specific example including some from own research in genomics and estimating the effects of Hurricane María in Puerto Rico.

### October 16, 2019

### Alex Mogilner

Professor of Mathematics and Biology, Courant Institute and Department of Biology, New York University

#### Feedbacks Between Mechanics, Geometry and Polarity Sorting Ensures Non-Random, Rapid and Precise Mitotic Spindle Assembly

One of the most fundamental cell biological events is assembly of the mitotic spindle - molecular machine that segregates sister chromatids into two daughter cells in the process of cell division. Two existent models of the mitotic spindle assembly are 1) search-and-capture (SAC) and 2) acentrosomal microtubule assembly (AMA). SAC model is pleasingly simple: microtubules (MTs), organized into two asters focused at two centrosomes, undergo dynamic instability: they grow and shrink randomly, rapidly and repeatedly. As soon as a growing MT end bumps into a kinetochore (KT) - molecular complex in the middle of a sister chromatid – the connection between the spindle pole (centrosome) and this chromatid is established. This model predicts that KTs are captured at random times and that slow spindle assembly is plagued by errors.

For decades, the SAC model seemed to work. Recently, 'inconvenient' data ruined the SAC model and suggested that a hybrid between SAC and AMA models (the latter posits that KT-associated MT bundles get integrated with centrosomal asters at random times) could work. I will explain how we used 3D tracking of centrosomes and KTs in animal cells to develop a computational agent-based model, which explains the remarkable speed and precision of the almost deterministic process of the spindle assembly emerging from random and imprecise molecular events.

### November 13, 2019

### Carolyn R. Cho

PPDM-Pharmacometrics, Merck & Co., Kenilworth NJ

#### (Mathematical) Model-Informed Drug Development

Mathematical biosciences research is increasingly used to drive decisions that are made during the drug discovery and development process. Underscoring this impact, the U.S. Food and Drug Administration has a goal of integrating model-informed drug development (MIDD) into more drug applications and advancing the use of MIDD, under the 2017 Prescription Drug User Fee Amendments act. This goal aims to develop information that cannot or would not be generated experimentally.

We will discuss the typical scientific questions encountered during the development of therapeutic agents. To illustrate the breadth of mathematical approaches that can be employed to address these questions, the mathematical models used during the development of a novel glucose-responsive insulin will be presented.

### January 22, 2020

### Alison Etheridge

Professor of Probability, Department of Statistics, University of Oxford; Fellow of the Royal Society (FRS), OBE

#### Title to be determined

### February 19, 2020

### L. Mahadevan

Lola England de Valpine Professor of Applied Mathematics, of Organismic and Evolutionary Biology, and of Physics, Harvard University

#### Geometry and the Brain

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Past Seasons

### April 17th, 2019

### Denise Kirschner

Professor, Microbiology, University of Michigan

#### Building, Analyzing and Calibrating Multi-Scale Models in 2D and 3D: Tuberculosis as a Case Study

Multi-scale models (MSM) are increasingly being used to study complex biological processes. Multi-scale models span a range of both spatial and temporal scales and can also encompass multiple physiological compartments. MSMs are growing more complex and cumbersome and it is necessary to coarse grain model aspects when appropriate. A new approach that we call tuneable resolution can provide that flexibility. Additionally, analyses of MSMs can be difficult, and we have fine-tuned a global uncertainty and sensitivity analysis approach that can be applied to all MSM types performing both inter- and intra-scale analyses and to assist with model calibration and validation. Finally, we have been exploring optimization, for example of drug treatment regimens, in the context of MSMs and have identified protocols that are computationally efficient. We will explore all of these topics in the context of our extensive work in the area of the host immune-response to infection with the bacterium Mycobacterium tuberculosis.

### March 20th, 2019

### Franziska Michor

Professor of Computational Biology, Department of Biostatistics, Harvard University

#### Mathematical modeling of cancer evolution

Cancer represents one of the biggest problems for modern societies. In 2017, the cancer incidence was projected to be around 1.7 million cases with an estimated mortality of roughly 601,000. By 2020, cancer deaths worldwide could reach 10 million. Therefore, an important goal of life science research is to improve tumor diagnostics and anti-cancer treatment options to alleviate cancer-related morbidity and mortality. We are interested in using the tools of theoretical evolutionary biology, applied mathematics, statistics, and computational biology to address important questions in cancer research. In this talk I will present recent work in my lab.

### February 20th, 2019

### Michael Mackey

Applied Mathematics in Bioscience and Medicine, Physiology, McGill University

#### Using mathematics to understand, treat and avoid hematological disease

Mathematical modeling of the regulation of blood cell production has reached a level of sophistication where it can be used to help to understand the cellular/molecular basis of some diseases like cyclical neutropenia. Equally, it has been used to suggest ways to treat some of these diseases by suggesting likely targets for drug intervention. And, lastly, the mathematics is now suggesting that there are ways in which we can avoid the hematological side effects of procedures like chemotherapy. In spite of these types of progress, however, we must recognize the very primitive nature of our modeling and that much remains to be done.

### January 23rd, 2019

### Michael I. Jordan

Pehong Chen Distinguished Professor, Department of Statistics, University of California, Berkeley

#### Machine Learning: Dynamical, Economic and Stochastic Perspectives

While there has been significant progress in the theory and practice in machine learning in recent years, many fundamental challenges remain. Some are mathematical in nature, such as the challenges associated with optimization and sampling in high-dimensional spaces. Some are statistical in nature, including the challenges associated with multiple decision-making. Others are economic in nature, including the need to price services and provide incentives in learning-based two-way markets. I will present recent progress on each of these fronts.

### December 12th, 2018

### Mark Lewis

Canada Research Chair in Mathematical Biology, University of Alberta

#### Using Mathematics to Understand Animal Movement Patterns

Animal movement patterns have long been the subject of mathematical and ecological interest. How do individual behavioral decision rules translate into macroscale patterns of space use such as foraging, patrolling or territories? I will show how mechanistic models, using random walks, stochastic processes, first passage time analysis and partial differential equations can be used to connect underlying processes to the observed patterns. Here interactions are complex and may involve memory of past events, as well as a cognitive map. I will make applications to a spectrum of different emerging patterns, ranging from territories in Amazonian birds to patrolling in wolves.

### November 14th, 2018

### Gunnar Carlsson

Department of Mathematics, Stanford University

#### The Shape of Biological Data

In recent years it has become clear that the methods of topology, the mathematical study of shape, can be used to advantage in obtaining understanding of large and complex data sets. Biological data of various kinds are particularly interesting from this point of view. I will discuss the methods from topology, with numerous examples.

### October 17th, 2018

### Avner Friedman

Distinguished University Professor, Department of Mathematics, The Ohio State University

#### Using Mathematical Models to Conduct Cancer Clinical Trials

Most cancer clinical trials with combination therapy in phase II have failed in phase III. One of the reasons for this failure is that no sufficient forethought was given to the interactions between the two (or more) agents. Before embarking on a clinical trial with (say) two agents, one should address the following questions:

- If the two drugs are positively correlated at any dose amounts, how to achieve the same tumor volume reduction with minimal negative side-effects?
- If the two drugs are antagonistic at certain ranges of the doses, how to avoid these zones of antagonism?
- What schedule is most effective in reducing tumor burden? e.g. in which order to give the drugs?
- How to reduce drug resistance by the tumor cells?

We use mathematical models to address these questions, and give several examples with different drugs.