Journal Club

We will discuss about "Inferring Regulatory Networks from Expression Data Using Tree-Based Methods," Huynh-Thu et al., PLoS ONE (2010). Abstract: One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for …

We will discuss about "Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data", Huang, Qi, Journal of The Royal Society Interface 15.139 (2018): 20170885. Abstract: Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a …

We will discuss about "Physics-informed neural networks for PDE-constrained optimization and control", Barry-Straume, Jostein, et al., arXiv preprint arXiv:2205.03377 (2022). Abstract: A fundamental problem of science is designing optimal control policies that manipulate a given environment into producing a desired outcome. Control PhysicsInformed Neural Networks simultaneously solve a given system state, and its respective optimal …

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 …