Journal Club

We will discuss about "Cell clustering for spatial transcriptomics data with graph neural networks", Li, J., Chen, S., Pan, X. et al., Nat Comput Sci 2, 399–408 (2022) Abstract: Spatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but …

We will discuss about "Absolute concentration robustness in networks with low-dimensional stoichiometric subspace", Meshkat, Nicolette, Anne Shiu, and Angelica Torres., Vietnam Journal of Mathematics 50.3 (2022): 623-651. Abstract: A reaction system exhibits “absolute concentration robustness” (ACR) in some species if the positive steady-state value of that species does not depend on initial conditions. Mathematically, this …

We will discuss about "Rhythmicity is linked to expression cost at the protein level but to expression precision at the mRNA level", David Laloum, and Marc Robinson-Rechavi, PLoS computational biology 18.9 (2022): e1010399. Abstract: Many genes have nycthemeral rhythms of expression, i.e. a 24-hours periodic variation, at either mRNA or protein level or both, and …

We will discuss about "Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19", Cheng, Jinyu, et al., Briefings in bioinformatics 22.2 (2021): 988-1005. Abstract: Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we …

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 …