Dongju Lim, Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors

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

We will discuss about “Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors”, Magal, Noa, et al., Chronic Stress 6 (2022): 24705470221100987. Abstract Background: Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior, potentially leading to impaired mental and

Hyeontae Jo, Characterizing possible failure modes in physics-informed neural networks

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

We will discuss about “Characterizing possible failure modes in physics-informed neural networks”, Krishnapriyan, Aditi, et al., Advances in Neural Information Processing Systems 34 (2021): 26548-26560. Abstract Recent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge as soft constraints on an empirical loss

Shinya Kuroda, Systems Biology of Insulin Action

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: 1. The "temporal information code" of insulin action: a bottom-up approach One of the essential elements of signaling networks is to encode information from a wide variety of inputs into a limited set of molecules. We have proposed a "temporal information code" that regulates a variety of physiological functions by encoding input information in

Seho Park, Dynamical information enables inference of gene regulation at single-cell scale

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

We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”, Zhang, Stephen Y., and Michael PH Stumpf., bioRxiv (2023): 2023-01. Abstract Cellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem

Martin Nowak, Evolution of cooperation

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Cooperation means that one individual pays a cost for another to receive a benefit. Cooperation can be at variance with natural selection. Why should you help competitors? Yet cooperation is abundant in nature and is important component of evolutionary innovation. Cooperation can be seen as the master architect of evolution and as the third

Eui Min Jung, Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks

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

We will discuss about “Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks”, Briat, Corentin, Ankit Gupta, and Mustafa Khammash. Cell systems 2.1 (2016): 15-26. Abstract The ability to adapt to stimuli is a defining feature of many biological systems and critical to maintaining homeostasis. While it is well appreciated that negative feedback

Marko Ćosić, The morphological analysis of the collagen straightness in the colon mucosa away from the cancer

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

Abstract: The morphological method – based on the topology and singularity theory and originally developed for the analysis of the scattering experiments – was extended to be applicable for the analysis of biological data. The usefulness of the topological viewpoint was demonstrated by quantification of the changes of collagen fiber straightness in the human colon mucosa

Julio Saez-Rodriguez, Dynamic logic models complement machine learning for personalized medicine

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Multi-omics technologies, and in particular those with single-cell and spatial resolution, provide unique opportunities to study the deregulation of intra- and inter-cellular signaling processes in disease. I will present recent methods and applications from our group toward this aim, focusing on computational approaches that combine data with biological knowledge within statistical and machine learning

Olive Cawiding, Single-sample landscape entropy reveals the imminent phase transition during disease progression

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

We will discuss about “Single-sample landscape entropy reveals the imminent phase transition during disease progression”, Liu R, Chen P, Chen L., Bioinformatics. 2020 Mar 1;36(5):1522-1532. Abstract Motivation: The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt, that is, there is a tipping point during

Marko Ćosić, Stewart’s Catastrophic Swing

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

Abstract The standard approach to problem-solving in physics consists of identifying state variables of the system, setting differential equations governing the state evolution, and solving the obtained. The behavior of the system for different values of parameters can be examined only as a fourth step. On the contrary, the modern approach to studying dynamical systems

Candan Celik, The effect of microRNA on protein variability and gene expression fidelity

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

We will discuss about “The effect of microRNA on protein variability and gene expression fidelity”, Hilfinger, Andreas, and Raymond Fan., Biophysical journal 122.3 (2023): 537a. Abstract Small regulatory RNA molecules such as microRNA modulate gene expression through inhibiting the translation of messenger RNA (mRNA). Such post-transcriptional regulation has been recently hypothesized to reduce the stochastic variability

(Rescheduled: 3/22 -> 3/24) Stefan Bauer, Neural Causal Models for Experimental Design

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Many questions in everyday life as well as in research are causal in nature: How would the climate change if we lower train prices or will my headache go away if I take an aspirin? Inherently, such questions need to specify the causal variables relevant to the question and their interactions. However, existing algorithms

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