Seminar

We will discuss about "Anti-Windup Protection Circuits for Biomolecular Integral Controllers", bioRxiv (2023).   Abstract Robust Perfect Adaptation (RPA) is a desired property of biological systems wherein a system’s output perfectly adapts to a steady state, irrespective of a broad class of perturbations. Achieving RPA typically requires the deployment of integral controllers, which continually adjust …

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 …

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 …

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 …