Seminar

We will discuss about “Detecting and quantifying causal associations in large nonlinear time series datasets”, Runge et al., Science Advances, 2019 Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference …

We will discuss about “Solving Singular Control Problems in Mathematical Biology, Using PASA”, Atkins et al., arXiv, 2020 In this paper, we will demonstrate how to use a nonlinear polyhedral constrained optimization solver called the Polyhedral Active Set Algorithm (PASA) for solving a general singular control problem. We present methods of discretizing a general optimal …

We will discuss about “A Random Matrix Theory Approach to Denoise Single-Cell Data”, Aparicio et al., Patterns, 2020 Single-cell technologies provide the opportunity to identify new cellular states. However, a major obstacle to the identification of biological signals is noise in single-cell data. In addition, single-cell data are very sparse. We propose a new method …

We will discuss about “Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics”, Ji et al., The Journal of Physical Chemistry A, 2020 The recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network such that the network not …

We will discuss about "Information Integration and Energy Expenditure in Gene Regulation", Estrada et al., Cell, 2016 Abstract: The quantitative concepts used to reason about gene regulation largely derive from bacterial studies. We show that this bacterial paradigm cannot explain the sharp expression of a canonical developmental gene in response to a regulating transcription factor …