Olive Cawiding, Nonlinear delay differential equations and their application to modeling biological network motifs

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

We will discuss about “Nonlinear delay differential equations and their application to modeling biological network motifs” Nature communications 12.1 (2021): 1788. Abstract Biological regulatory systems, such as cell signaling networks, nervous systems and ecological webs, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight into

Hyeontae Jo, Denoising Diffusion Probabilistic Models

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

We will discuss about “Denoising Diffusion Probabilistic Models” Advances in neural information processing systems 33 (2020): 6840-6851. Abstract We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according

Hyun Kim, A comparative study of algorithms detecting differential rhythmicity in transcriptomic data

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

We will discuss about “A comparative study of algorithms detecting differential rhythmicity in transcriptomic data” bioRxiv (2023): 2023-10. Abstract Rhythmic transcripts play pivotal roles in driving the daily oscillations of various biological processes. Genetic or environmental disruptions can lead to alterations in the rhythmicity of transcripts, ultimately impacting downstream circadian outputs, including metabolic processes and

Yun Min Song, Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

We will discuss about “Hard limits and performance tradeoffs in a class of antithetic integral feedback networks.” bioRxiv (2023): 2023-09. Abstract The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the