Seminars and Colloquia by Series

Hierarchical structure and computation of data-driven neuronal networks

Series
Mathematical Biology Seminar
Time
Friday, March 19, 2021 - 15:00 for 1 hour (actually 50 minutes)
Location
ONLINE
Speaker
Hannah ChoiGeorgia Tech

The complex connectivity structure unique to the brain network is believed to underlie its robust and efficient coding capability. Specifically, neuronal networks at multiple scales utilize their structural complexities to achieve different computational goals. I will first introduce functional implications that can be inferred from a weighted and directed “single” network representation of the brain. Then, I will consider a more detailed and realistic network representation of the brain featuring multiple types of connection between a pair of brain regions, which enables us to uncover the hierarchical structure of the brain network using an unsupervised method.  Finally, if time permits, I will discuss computational implications of the hierarchical organization of the brain network, focusing on a specific type of visual computation- predictive coding.

Meeting Link: https://gatech.bluejeans.com/348270750

Dynamics of movement in complex environments

Series
Mathematical Biology Seminar
Time
Friday, March 5, 2021 - 15:00 for 1 hour (actually 50 minutes)
Location
ONLINE
Speaker
Sarah OlsonWorcester Polytechnic Institute

In this talk, we will highlight two different types of movement in viscosity dominated environments: sperm navigation and centrosome clustering in dividing cells.  Sperm often interact with chemicals and other proteins in the fluid, changing force generation and emergent swimming trajectories. Recently developed computational methods and asymptotic analysis allow for insight into swimming efficiency and hydrodynamic interactions of swimmers in different fluid environments. We will also show how parameter estimation techniques can be utilized to infer fluid and/or swimmer properties. For the case of centrosome movement, we explore how cancer cells can cluster additional centrosomes and proceed through either a bipolar or multipolar division. The models focus on understanding centrosome movement during cell division, which is the result of complex interactions between stochastic microtubule dynamics and motor proteins in the viscous cytoplasm of the cell.

Meeting Link: https://gatech.bluejeans.com/348270750

Single Particle Tracking with Applications to Lysosome Transport

Series
Mathematical Biology Seminar
Time
Friday, February 26, 2021 - 15:00 for 1 hour (actually 50 minutes)
Location
ONLINE
Speaker
Keisha CookTulane University

Live cell imaging and single particle tracking techniques have become increasingly popular amongst the mathematical biology community. We study endocytosis, the cellular internalization and transport of bioparticles. This transport is carried out in membrane-bound vesicles through the use of motor proteins. Lysosomes, known for endocytosis, phagocytic destruction, and autophagy, move about the cell along microtubules. Single particle tracking methods utilize stochastic models to simulate intracellular transport and give rise to rigorous analysis of the resulting properties, specifically related to transitioning between inactive to active states. This confidence in the stochastic modeling of particle tracking is useful not only for particle-containing lysosomes, but also broad questions of cellular transport studied with single particle tracking.

Meeting Link: https://gatech.bluejeans.com/348270750

Quantitative modeling of protein RNA interactions

Series
Mathematical Biology Seminar
Time
Friday, February 5, 2021 - 15:00 for 1 hour (actually 50 minutes)
Location
ONLINE
Speaker
Ralf BundschuhThe Ohio State University

The prediction of RNA secondary structures from sequence is a well developed task in computational RNA Biology. However, in a cellular environment RNA molecules are not isolated but rather interact with a multitude of proteins. RNA secondary structure affects those interactions with proteins and vice versa proteins binding the RNA affect its secondary structure.  We have extended the dynamic programming approaches traditionally used to quantify the ensemble of RNA secondary structures in solution to incorporate protein-RNA interactions and thus quantify these effects of protein-RNA interactions and RNA secondary structure on each other. Using this approach we demonstrate that taking into account RNA secondary structure improves predictions of protein affinities from RNA sequence, that RNA secondary structures mediate cooperativity between different proteins binding the same RNA molecule, and that sequence variations (such as Single Nucleotide Polymorphisms) can affect protein affinity at a distance mediated by RNA secondary structures.

https://gatech.bluejeans.com/348270750

Combinatorial aspects of RNA design

Series
Mathematical Biology Seminar
Time
Friday, January 22, 2021 - 15:00 for 1 hour (actually 50 minutes)
Location
ONLINE
Speaker
Yann PontyEcole Polytechnique France

Please Note: BlueJeans Link: https://bluejeans.com/348270750

RiboNucleic Acids (RNAs) are ubiquitous, versatile, and overall fascinating, biomolecules which play central roles in modern molecular biology. They also represent a largely untapped potential for biotechnology and health, substantiated by recent disruptive developments (mRNA vaccines, RNA silencing therapies, guide-RNAs of CRISPR-Cas9 systems...). To address those challenges, one must effectively  perform RNA design, generally defined as the determination of an RNA sequence achieving a predefined biological function.

I will focus in this talk on algorithmic results and enumerative properties stemming from the inverse folding, the problem of designing a sequence of nucleotides that fold preferentially and uniquely (with respect to base-pair maximization) into a target secondary structure. Despite the NP-hardness of the problem (+ absence of a Fixed Parameter-Tractable algorithm) we showed that it can be solved in polynomial time for restricted families of structures. Such families are dense in the space of designable 2D structures, so that any structure that admits a solution for the inverse folding can be efficiently designed in an approximated sense.

We show that any 2D structure avoiding two forbidden motifs can be modified into a designable structure  by adding at most one extra base-pair per helix. Moreover, both the modification and the design of a sequence for the modified structure can be computed in linear time. Finally, if time allows, I will discuss combinatorial consequences of the existence of undesignable motifs. In particular, it implies an exponentially decreasing density of designable structures amongst secondary structures. Those results extend to virtually any design objectives and energy models.

This is joint work with Cédric Chauve, Jozef Hales, Jan Manuch, Ladislav Stacho (SFU, Canada), Alice Héliou, Mireille Régnier, and Hua-Ting Yao (Ecole Polytechnique, France).

Automated Feature Extraction from Large Cardiac Electrophysiological Data Sets

Series
Mathematical Biology Seminar
Time
Friday, November 6, 2020 - 15:00 for 1 hour (actually 50 minutes)
Location
ONLINE
Speaker
Peter HinowUniversity of Wisconsin-Milwaukee

Please Note: https://bluejeans.com/819527897/5512

A multi-electrode array-based application for the long-term recording of action potentials from electrogenic cells makes possible exciting cardiac electrophysiology studies in health and disease. With hundreds of simultaneous electrode recordings being acquired over a period of days, the main challenge becomes achieving reliable signal identification and quantification. We set out to develop an algorithm capable of automatically extracting regions of high-quality action potentials from terabyte size experimental results and to map the trains of action potentials into a low-dimensional feature space for analysis. Our automatic segmentation algorithm finds regions of acceptable action potentials in large data sets of electrophysiological readings. We use spectral methods and support vector machines to classify our readings and to extract relevant features. We show that action potentials from the same cell site can be recorded over days without detrimental effects to the cell membrane. The variability between measurements 24 h apart is comparable to the natural variability of the features at a single time point. Our work contributes towards a non-invasive approach for cardiomyocyte functional maturation, as well as developmental, pathological, and pharmacological studies.

This is joint work with Dr. Viviana Zlochiver (Advocate Aurora Research Institute) and John Jurkiewicz (graduate student at UWM).

Meeting room: https://bluejeans.com/819527897/5512

CANCELLED - - Tiny Giants - Mathematics Looks at Zooplankton

Series
Mathematical Biology Seminar
Time
Wednesday, April 8, 2020 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Peter HinowUniversity of Wisconsin-Milwaukee

Zooplankton is an immensely numerous and diverse group of organisms occupying every corner of the oceans, seas and freshwater bodies on the planet. They form a crucial link between autotrophic phytoplankton and higher trophic levels such as crustaceans, molluscs, fish, and marine mammals. Changing environmental conditions such as rising water temperatures, salinities, and decreasing pH values currently create monumental challenges to their well-being.

A signi cant subgroup of zooplankton are crustaceans of sizes between 1 and 10 mm. Despite their small size, they have extremely acute senses that allow them to navigate their surroundings, escape predators, find food and mate. In a series of joint works with Rudi Strickler (Department of Biological Sciences, University of Wisconsin - Milwaukee) we have investigated various behaviors of crustacean zooplankton. These include the visualization of the feeding current of the copepod Leptodiaptomus sicilis, the introduction of the "ecological temperature" as a descriptor of the swimming behavior of the water flea Daphnia pulicaria and the communication by sex pheromones in the copepod Temora longicornis. The tools required for the studies stem from optics, ecology, dynamical systems, statistical physics, computational fluid dynamics, and computational neuroscience.

Canceled -- Human Sensitive Analytics for Personalized Weight Loss Interventions

Series
Mathematical Biology Seminar
Time
Wednesday, March 25, 2020 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Yonatan Mintz Department of Industrial and Systems Engineering, Georgia Institute of Technology

One of the most challenging aspects of designing human sensitive systems is in designing systems that assist decision makers in applying an effective intervention to a large group of individuals. This design challenge becomes especially difficult when the decision maker must operate under scarce resources and only partial knowledge of how each individual will react to the intervention.

In this talk, I will consider this problem from the perspective of a clinician that is designing a personalized weight loss program. Despite this focus, the precision analytics framework I propose for designing these interventions is quite general and can apply to many settings where a single coordinator must influence agents who make decisions by maximizing utility functions that depend on prior system states, inputs, and other parameters that are initially unknown. This precision analytics framework involves three steps: first, a predictive model that effectively captures the decision-making process of an agent; second, an optimization algorithm to estimate this model’s parameters for each agent and predict their future decisions; and third, an optimization model that uses these predictions to optimize a set of incentives that will be provided to each agent. A key advantage of this framework is that the calculated incentives are adapted as new information is collected. In the case of personalized weight loss interventions, this means that the framework can leverage patient level data from mobile and wearable sensors over the course of the intervention to personalize the recommended treatment for each individual.

  I will present theoretical results that show that the incentives computed by this approach are asymptotically optimal with respect to a loss function that describes the coordinator's objective.  I will also present an effective decomposition scheme to optimize the agent incentives, where each sub-problem solves the coordinator's problem for a single agent, and the master problem is a pure integer program. To validate this method I will present a numerical study that shows this proposed framework is more cost efficient and clinically effective than simple heuristics in a simulated environment. I will conclude by discussing the results of a randomized control trial (RCT) and pilot study where this precision analytics framework was applied for personalizing exercise goals for UC Berkeley staff and students. The results of these trials show that using personalized step goals calculated by the precision analytics algorithm result in a significant improvement over existing state of the art approaches in a real world setting.

Modeling Phytoplankton Blooms with a Reaction-Diffusion Predator-Prey Model

Series
Mathematical Biology Seminar
Time
Wednesday, March 4, 2020 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Seth CowallMercer University

Phytoplankton are the base of the marine food web. They are also responsible for much of the oxygen we breathe, and they remove carbon dioxide from the atmosphere. The mechanisms that govern the timing of seasonal phytoplankton blooms is one of the most debated topics in oceanography. Here, we present a macroscale plankton ecology model consisting of coupled, nonlinear reaction-diffusion equations with spatially and temporally changing coefficients to offer insight into the causes of phytoplankton blooms. This model simulates biological interactions between nutrients, phytoplankton and zooplankton. It also incorporates seasonally varying solar radiation, diffusion and depth of the ocean’s upper mixed layer because of their impact on phytoplankton growth. The model is analyzed using seasonal oceanic data with the goals of understanding the model’s dependence on its parameters and of understanding seasonal changes in plankton biomass. A study of varying parameter values and the resulting effects on the solutions, the stability of the steady-states, and the timing of phytoplankton blooms is carried out. The model’s simulated blooms result from a temporary attraction to one of the model’s steady-states.

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