We will discuss about “ Decomposing predictability to identify dominant causal drivers in complex ecosystems ”,Suzuki, Kenta, Shin-ichiro S. Matsuzaki, and Hiroshi Masuya., Proceedings of the National Academy of Sciences 119.42 (2022): e2204405119.   Abstract Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of …

We will discuss about “Generalized Michaelis–Menten rate law with time-varying molecular concentrations”, Lim, Roktaek, et al.,bioRxiv (2022): 2022-01   Abstract The Michaelis–Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, and chemical engineering. The MM rate law and its …

We will discuss about “Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics”, Wang, Yiling, et al., bioRxiv (2023): 2023-08.   Abstract The classical three-stage model of stochastic gene expression predicts the statistics of single cell mRNA and protein number fluctuations as a function of the rates of promoter switching, transcription, translation, …

We will discuss about “Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks ”,Briat, Corentin, Ankit Gupta, and Mustafa Khammash., Journal of The Royal Society Interface 15.143 (2018): 20180079 Abstract The ability of a cell to regulate and adapt its internal state in response to unpredictable environmental changes is called …

We will discuss about “A data-driven approach for timescale decomposition of biochemical reaction networks”, Amir Akbari, Zachary B. Haiman, Bernhard O. Palsson, bioRxiv (2023) Abstract Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for timescale decomposition of biochemical reaction …