Events

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 …

We will discuss about “Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks”, Briat, Corentin, Ankit Gupta, and Mustafa Khammash. Cell systems 2.1 (2016): 15-26. Abstract The ability to adapt to stimuli is a defining feature of many biological systems and critical to maintaining homeostasis. While it is well appreciated that negative feedback …

We will discuss about “Single-sample landscape entropy reveals the imminent phase transition during disease progression”, Liu R, Chen P, Chen L., Bioinformatics. 2020 Mar 1;36(5):1522-1532. Abstract Motivation: The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt, that is, there is a tipping point during …