We will discuss about “Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter”, Bonarius et. al., IEEE Trans. Biomed. Eng., 2021 Abstract Objective: In the near future, real-time estimation of peoples unique, precise circadian clock state has the potential to improve the efficacy of medical treatments and improve human performance …

We will discuss about “Stochastic reaction networks in dynamic compartment populations”, Duso and Zechner, PNAS, 2020 Abstract: Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies …

We will discuss about “Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation”, Li et. al., Cell Systems, 2018 Abstract Gene regulation is a complex non-equilibrium process. Here, we show that quantitating the temporal regulation of key gene states (transcriptionally inactive, active, and refractory) provides a parsimonious framework for analyzing gene regulation. Our …

We will discuss about “TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data”, Ness-Cohn and Braun, Bioinformatics, 2021 Abstract Motivation: The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implicatins of the circadian clock for …

We will discuss about "Cellular signaling beyond the Wiener-Kolmogorov limit", Weisenberger et al., bioRxiv, 2021 Abstract: Accurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory, originally developed for engineering problems, has …

We will discuss about "Machine learning of stochastic gene network phenotypes", Park et al., bioRxiv, 2019 Abstract: A recurrent challenge in biology is the development of predictive quantitative models because most molecular and cellular parameters have unknown values and realistic models are analytically intractable. While the dynamics of the system can be analyzed via computer …