We will discuss about “Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms”,Jia, Chen, and Youming Li, BioRxiv (2022). Abstract Classical gene expression models assume exponential switching time distributions between the active and inactive promoter states. However, recent experiments have shown that many genes in mammalian cells may produce non-exponential switching time …

We will discuss about “Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems”, Linka, Kevin, et al., Computer Methods in Applied Mechanics and Engineering Volume 402, 1 December 2022, 115346 Abstract Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical …

We will discuss about “Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics" , Lin, Baihan., arXiv preprint arXiv:2204.14048 (2022). Abstract The absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate, differentiate, and compete, i.e. …

We will discuss about “A scalable approach for solving chemical master equations based on modularization and filtering ”, Fang, Zhou, Ankit Gupta, and Mustafa Khammash., bioRxiv (2022). Abstract Solving the chemical master equation (CME) that characterizes the probability evolution of stochastically reacting processes is greatly important for analyzing intracellular reaction systems. Conventional methods for solving CMEs …

We will discuss about “Optimal information networks: Application for data-driven integrated health in populations”, Servadio, Joseph L., and Matteo Convertino, Science Advances 4.2 (2018): e1701088. Abstract Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and …

We will discuss about “Estimating and Assessing Differential Equation Models with Time-Course Data”, Wong, Samuel WK, Shihao Yang, and S. C. Kou, arXiv preprint arXiv:2212.10653 (2022). Abstract Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course …

We will discuss about “Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors”, Magal, Noa, et al., Chronic Stress 6 (2022): 24705470221100987. Abstract Background: Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior, potentially leading to impaired mental and …

We will discuss about “Characterizing possible failure modes in physics-informed neural networks”, Krishnapriyan, Aditi, et al., Advances in Neural Information Processing Systems 34 (2021): 26548-26560. Abstract Recent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge as soft constraints on an empirical loss …

We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”, Zhang, Stephen Y., and Michael PH Stumpf., bioRxiv (2023): 2023-01. Abstract Cellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem …