Journal Club

We will discuss about “Improving gene regulatory network inference and assessment: The importance of using network structure”, Escorcia-Rodríguez, Juan M., et al., bioRxiv (2023): 2023-01. Abstract Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous …

We will discuss about “Inference and uncertainty quantification of stochastic gene expression via synthetic models”, Öcal et al., J. R. Soc. Interface. Abstract Estimating uncertainty in model predictions is a central task in quantitativebiology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has …

We will discuss about “A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease”, Alexandersen, Christoffer G., et al., Journal of the Royal Society Interface 20.198 (2023): 20220607. Abstract Alzheimer’s disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies …

We will discuss about “Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning”, Zhao, Shuai, et al., IEEE Transactions on Power Electronics 37.10 (2022): 11567-11578. Abstract Physics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics, this article …

We will discuss about “Uncovering specific mechanisms across cell types in dynamical models”, Hauber, Adrian Lukas, Marcus Rosenblatt, and Jens Timmer., bioRxiv (2023): 2023-01. Abstract Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to …

We will discuss about “The energy cost and optimal design of networks for biological discrimination”, Yu, Qiwei, Anatoly B. Kolomeisky, and Oleg A. Igoshin., Journal of the Royal Society Interface 19.188 (2022): 20210883. Abstract Many biological processes discriminate between correct and incorrect substrates through the kinetic proofreading mechanism that enables lower error at the cost of …

We will discuss about “Wearable data science for personalized digital medicine” Abstract Millions of people currently use wearables such as the Apple Watch to monitor their physical activity, heart rate, and other physiological signals, generating an unprecedented amount of wearable data. This presents an opportunity for digital medicine to advance precision medicine. However, the noisy …

We will discuss about “scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching”, Karin, Jonathan, Yonathan Bornfeld, and Mor Nitzan., Nature Biotechnology (2023): 1-10. Abstract Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we …

We will discuss about “The singularity response reveals entrainment properties of the plant circadian clock”, Masuda, Kosaku, et al., Nature Communications 12.1 (2021): 864. Abstract Circadian clocks allow organisms to synchronize their physiological processes to diurnal variations. A phase response curve allows researchers to understand clock entrainment by revealing how signals adjust clock genes differently …

We will discuss about “ The Internal Model Principle for Biomolecular Control Theory ”, Gupta, Ankit, and Mustafa Khammash., IEEE Open Journal of Control Systems 2 (2023): 63-69.   Abstract The well-known Internal Model Principle (IMP) is a cornerstone of modern control theory. It stipulates the necessary conditions for asymptotic robustness of disturbance-prone dynamical systems …

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 …