Journal Club

We will discuss about "Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach", Öcal, Kaan, Guido Sanguinetti, and Ramon Grima., arXiv preprint arXiv:2210.05329 (2022). Abstract: The complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist's toolkit. For stochastic reaction networks described using the Chemical Master Equation, commonly …

We will discuss about “PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations”, Zhong, Weiheng, and Hadi Meidani, Computer Methods in Applied Mechanics and Engineering 403 (2023): 115664. Abstract We propose a new class of physics-informed neural networks, called the Physics-Informed Variational Auto-Encoder (PI-VAE), to solve stochastic differential equations (SDEs) or inverse problems involving SDEs. In …

We will discuss about “Detecting critical state before phase transition of complex biological systems by hidden Markov model”, Chen, Pei, et al. Bioinformatics 32.14 (2016): 2143-2150.   Abstract Motivation: Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may …

We will discuss about “Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors”, Vipond, Oliver, et al, Proceedings of the National Academy of Sciences 118.41 (2021): e2102166118. Abstract Highly resolved spatial data of complex systems encode rich and nonlinear information. Quantification of heterogeneous and noisy data—often with outliers, artifacts, and mislabeled points—such as …

We will discuss about "Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators”, Karapetyan, Sargis, and Nicolas E. Buchler,Physical Review E 92.6 (2015): 062712. Abstract Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest …

We will discuss about “Optimal control of aging in complex networks”, Sun, Eric D., Thomas CT Michaels, and L. Mahadevan, Proceedings of the National Academy of Sciences 117.34 (2020): 20404-20410. Abstract Many complex systems experience damage accumulation, which leads to aging, manifest as an increasing probability of system collapse with time. This naturally raises the question …

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 …