Journal Club

We will discuss about "Learning stable and predictive structures in kinetic systems", Niklas Pfister , Stefan Bauer, and Jonas Peters. PNAS, 2019 Abstract: Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, …

We will discuss about "Neural Ordinary Differential Equations", Chen, Ricky TQ, et al., Advances in neural information processing systems 31 (2018). Abstract: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output …

We will discuss about "Molecular convolutional neural networks with DNA regulatory circuits", Pei, Hao, et al., Nature Machine Intelligence (2022): 1-11. Abstract: Complex biomolecular circuits enabled cells with intelligent behaviour to survive before neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s, synthetic DNA circuits in liquid phase have been developed as …

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 …