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 …