기초과학연구원 의생명수학그룹
Abstract: Wearable health trackers have shifted from gadgets for sports enthusiasts to valuable health sentinels over the last few years and that transformation is gathering pace. What do these devices really measure about sleep? What types of devices are there, and which can we trust? Which of the many sleep measures reported, contribute to a …
We will discuss about "Phenotypic switching in gene regulatory networks", PNAS (2014). Abstract Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a …
We will discuss about "An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells", ArXiv (2023). Abstract Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets tend to …
Abstract: Biological phenomena are notorious for crossing several temporal and spatial scales. While often it may be sufficient to focus on a single scale, it is not rare that we have to consider several scales simultaneously. Computational modeling and simulation of biological systems thus frequently requires to include diverse temporal and spatial scales. A popular …
This presentation introduces an approach for estimating parameter distributions in dynamic systems modeled by differential equations. Traditional parameter estimation techniques often struggle with Repeated Cross-Sectional (RCS) data, characteristic of many real-world scenarios where continuous data collection is impractical or impossible. Previous approaches, like employing mean values or leveraging Gaussian Processes for time series generation, fail …
Abstract: In typical single-cell RNA-seq (scRNA-seq) data analysis, a clustering algorithm is applied to find discrete cell clusters as putative cell types, and then a statistical test is employed to identify the differentially expressed (DE) genes between the cell clusters. However, this common procedure suffers the ``double dipping'' issue: the same data are used twice …
We will discuss about “Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming”, Cell (2019). Abstract Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. …
We will discuss about “PenDA, a rank-based method for personalized differential analysis: Application to lung cancer” Plos Computational Biology (2020). Abstract The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require …
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 …
We will discuss about “Data driven governing equations approximation using deep neural networks” Journal of Computational Physics (2019). Abstract We present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be …
In this talk, we discuss the paper "Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe", by Xiaojie Qiu et.al., Cell Syst. 2020. Abstract Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe …
In this talk, we discuss the paper, "MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data" by Siyao Liu et.al. Genome Biology, 2024. Abstract Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) …