Biomedical Mathematics Seminar

Abstract: Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing …

Abstract: Through the past decades, electrophysiological experiments have revealed that extracellular electrical potential of brain show diverse rhythmic activity. Called ‘Local Field Potential(LFP)’, those rhythmic activities are thought to reflect populational activity of neurons. In this talk, I will introduce basic concepts on LFP and its generation mechanisms. Then, roles of LFP in brain inter-areal …

Abstract: The Downing lab investigates the intricate biophysical interactions between cells and their environment, elucidating their role in modulating adult cell behavior and phenotypic transitions via epigenetic regulation of gene expression. Leveraging diverse genome-scale sequencing techniques, we decipher mechanisms underlying cell fate transitions mediated through dynamic regulation of nuclear chromatin and heterogeneous gene activity. Our …

I present time delay estimation problems in astronomy as a part of time delay cosmography to infer the Hubble constant, the current expansion rate of the Universe. Time delay cosmography is based on strong gravitational lensing, an effect that multiple images of the same astronomical object appear in the sky because paths of the light …

Abstract: A system of ordinary differential equations (ODEs) is one of the most widely used tools to describe a deterministic dynamical system. In general, such ODEs involve nonlinear equations, which make analysis of dynamical systems difficult. In this talk, we introduce Koopman theory, which offers a linear representation – not an approximation – of nonlinear dynamics. In particular, we present a data-driven algorithm to find such a linear representation

The talk will be hybrid, participants may join via Zoom with the following link: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09

Title: Theoretical studies on biological oscillations by using waveform data and mathematical models Abstract: Temporal waveforms of biological oscillations are of various shapes. In our research, we have explored the functional implications of these waveform shapes. In particular, we theoretically showed that the period of circadian clocks is proportional to the waveform distortion from sinusoidal wave. It suggests that the circadian period can be stable against temperature changes only if the waveform becomes more distorted at higher temperatures. In this talk, I will explain my past research and discuss my future plans. Reference: Shingo Gibo, Gen Kurosawa, Non-sinusoidal Waveform in Temperature Compensated Circadian Oscillations, Biophysical Journal 116 (4) 741-751 (2019). doi: 10.1016/j.bpj.2018.12.022 Shingo Gibo, Gen Kurosawa, Theoretical study on the regulation of circadian rhythms by RNA methylation, Journal of Theoretical Biology 490, 110140 (2020). doi; 10.1016/j.jtbi.2019.110140 Shingo Gibo, Teiji Kunihiro, Tetsuo Hatsuda, Gen Kurosawa, Waveform …

In this talk, we discuss the paper : “Solving Inverse Problems in Medical Imaging with Score-Based Generative Models” by Y Song et al. Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based on machine learning typically train a model to directly …

In this talk, we discuss the paper : “Diffusion Posterior Sampling for Linear Inverse Problem Solving- A Filtering Perspective” by Z. Dou& Y. Song Diffusion models have achieved tremendous success in generating high-dimensional data like images, videos and audio. These models provide powerful data priors that can solve linear inverse problems in zero shot through …

Abstract: In this talk, we propose novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. …