기초과학연구원 의생명수학그룹
We will discuss about "AI Pontryagin or how artificial neural networks learn to control dynamical systems", Böttcher, L., Antulov-Fantulin, N. & Asikis, T., Nat Commun 13, 333 (2022). Abstract: The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems, both control energy and cost …
Reconstruction of gene regulatory networks (GRNs) is a powerful approach to capture a prioritized gene set controlling cellular processes. In our previous study, we developed TENET a GRN reconstructor from single cell RNA sequencing (scRNAseq). TENET has a superior capability to identify key regulators compared with other algorithms. However, accurate inference of gene regulation is …
We will discuss about "Chemical Organisation Theory ", Dittrich, Peter, and Pietro Speroni Di Fenizio, Bulletin of mathematical biology 69.4 (2007): 1199-1231. Abstract: Complex dynamical reaction networks consisting of many components that interact and produce each other are difficult to understand, especially, when new component types may appear and present component types may vanish completely. …
We will discuss about "Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells", Jia, Chen, and Ramon Grima, bioRxiv (2022). Abstract: The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA, and switching of the gene between …
We will discuss about "Learning stable and predictive structures in kinetic systems", Niklas Pfister , Stefan Bauer, and Jonas Peters. PNAS, 2019 Abstract: Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, …
We will discuss about "Neural Ordinary Differential Equations", Chen, Ricky TQ, et al., Advances in neural information processing systems 31 (2018). Abstract: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output …
We will discuss about "Molecular convolutional neural networks with DNA regulatory circuits", Pei, Hao, et al., Nature Machine Intelligence (2022): 1-11. Abstract: Complex biomolecular circuits enabled cells with intelligent behaviour to survive before neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s, synthetic DNA circuits in liquid phase have been developed as …
This talk will be given online (If you want to join, please send me an email to jaekkim@ibs.re.kr) Abstract Coupling Math with User-Centric Design Shift workers experience profound circadian disruption due to the nature of their work, which often has them on-the-clock at times when their internal clock is sending a strong, sleep-promoting signal. Mathematical …
Abstract: Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference such as estimating the effects of covariates and quantifying the prediction uncertainty is not trivial …
Abstract: A fundamental challenge in biology is the reconstruction of developmental trajectories as they divide and progress through different stages. The recent advance of CRISPR-based molecular tools, such as intMEMOIR and scGESTALT, have produced a new generation of techniques that enable the reconstruction of cell lineages of complex organisms at single-cell resolution. However, there are …
In this short presentation session, six researchers from IBS BIMAG present 10-minute talks. 4:00 -- 4:10, Hyeontae Jo 4:15 -- 4:25, Hyun Kim 4:30 -- 4:40, Aurelio de los Reyes V 4:45 -- 4:55, Yun Min Song 5:00 -- 5:10, Seokmin Ha 5:15 -- 5:25, Hyukpyo Hong
We will discuss about "Inferring Regulatory Networks from Expression Data Using Tree-Based Methods," Huynh-Thu et al., PLoS ONE (2010). Abstract: One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for …