기초과학연구원 의생명수학그룹
We will discuss about "An Efficient Characterization of Complex-Balanced, Detailed-Balanced, and Weakly Reversible Systems", Craciun et al., SIAM Journal on Applied Mathematics, 2020 Abstract: Very often, models in biology, chemistry, …
This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) Abstract: Outputs from health biological systems display complex fluctuations that are not random but display robust and …
We will discuss about "Toroidal topology of population activity in grid cells", Gardner et al., Nature, 2021. Abstract: The medial entorhinal cortex is part of a neural system for mapping …
We will discuss about "The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes", Katori et al., PNAS, 2022. Abstract: Human sleep phenotypes can be …
This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) Abstract: The idea of balance between excitation and inhibition is central in the theory of biological neural networks. …
This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) Abstract: I will first describe how to extend the theory of balanced networks to account for synaptic …
We will discuss about "Optimizing Oscillators for Specific Tasks Predicts Preferred Biochemical Implementations", Agrahar and Rust., bioRxiv, 2022. Abstract: Oscillatory processes are used throughout cell biology to control time-varying physiology …
This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) Abstract: I will introduce mathematical and computational methods for spatio-temporal modelling in molecular and cell biology, including …
This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) Abstract: I will discuss the development, analysis and applications of multi-resolution methods for spatio-temporal modelling of intracellular …
This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) Abstract: Quantitative characterization of biomolecular networks is important for the analysis and design of network functionality. Reliable …
Abstract. Matrix/tensor factorization models such as principal component analysis , nonnegative matrix factorization, and CANDECOM/PARAFAC tensor decomposition provide powerful framework for dimension reduction and interpretable feature extraction, which are important …
We will discuss about "Approximating Solutions of the Chemical Master Equation using Neural Networks", Sukys et al., bioRxiv, 2022 Abstract: The Chemical Master Equation (CME) provides an accurate description of …