Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach – Gyuyoung Hwang

In this talk, we discuss the paper "Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach" by R.C. Vendrell et.al., Sci. Adv. 2024 at the Journal Club. Abstract De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no

Dynamics and Decision Making in Single Cells – Galit Lahav

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract Individual human cancer cells often show different responses to the same treatment. In this talk I will share the quantitative experimental approaches my lab has developed for studying the fate and behavior of human cells at the single-cell level. I will focus on the tumor suppressor protein p53, a transcription factor controlling genomic integrity

Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain – Hyun Kim

In this talk, we discuss the paper "Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain" by Rubén Calvo et al., Physical Review Letters 2024, at the Journal Club. Abstract Recent analyses, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain, compellingly

A lognormal Poisson model for single cell transcriptomic normalization – Fred Wright

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract The advent of single-cell transcriptomics has brought a greatly improved understanding of the heterogeneity of gene expression across cell types, with important applications in developmental biology and cancer research. Single-cell RNA sequencing datasets, which are based on tags called universal molecular identifiers, typically include a large number of zeroes. For such datasets, genes with

Accurate predictions on small data with a tabular foundation model – Dongju Lim

In this talk, we discuss the paper "Accurate predictions on small data with a tabular foundation model" by Noah Hollmann et al., Nature (2025). Abstract Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science1,2. The fundamental prediction task of filling in

Entrainment and multi-stability of the p53 oscillator in human cells – Eui Min Jeong

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper, "Entrainment and multi-stability of the p53 oscillator in human cells" by Alba Jiménez et al., Cell Systems, 2024. Abstract  The tumor suppressor p53 responds to cellular stress and activates transcription programs critical for regulating cell fate. DNA damage triggers oscillations in p53 levels with a robust period. Guided by

Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series – Yun Min Song

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series" by Julian Lee, Physical Review E, 2025. Abstract  Transfer entropy (TE) is a widely used tool for quantifying causal relationships in stochastic dynamical systems. Traditionally, TE and its conditional

Dimensionality Reduction and Summary-Statistical Modeling in Genetic Studies – Fatemeh Yavartanoo

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

Abstract: This presentation introduces DRLPC and a refined summary-statistics method to improve genetic association analysis. Applications to cognition, neurodegenerative diseases, and high cholesterol are discussed, with future directions in single-cell analysis and drug target discovery.

FoodSeq: Using Genomics to Track and Study Diet – Lawrence David

Conference room, (B109) Daejeon, Daejeon, Korea, Republic of

Abstract Dietary assessment is crucial for understanding the relationship between diet and health. Yet traditional recall-based methods for tracking diet often face challenges like participant compliance and accurate recall. To address these issues, our lab at Duke University has developed FoodSeq, a genomic approach to track food intake through DNA sequencing of stool samples. In

Boolean modelling as a logic-based dynamic approach in systems medicine – Kevin Spinicci

In this talk, we discuss the paper "Boolean modelling as a logic-based dynamic approach in systems medicine" by Ahmed Abdelmonem Hemedan et al., Computational and Structural biotechnology journal (2022). Abstract  Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can

Chaos is not rare in natural ecosystems – Olive Cawiding

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Chaos is not rare in natural ecosystems" by Tanya L. Rogers, Bethany J. Johnson, and Stephan B. Munch, nature ecology & evolution, 2022. Abstract Chaotic dynamics are thought to be rare in natural populations but this may be due to methodological and data limitations, rather than the inherent

Quantifying and correcting bias in transcriptional parameter inference from single-cell data – Kangmin Lee

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Quantifying and correcting bias in transcriptional parameter inference from single-cell data" by Ramon Grima and Pierre-Marie Esmenjaud, Biophysical journal, 2024. Abstract The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state

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