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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 …

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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 …

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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 …

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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 …

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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 …

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(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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Sungwoong Cho, HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork

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

Fast and accurate predictions for complex physical dynamics are a big challenge across various applications. Real-time prediction on resource-constrained hardware is even more crucial in the real-world problems. The deep operator network (DeepONet) has recently been proposed as a framework for learning nonlinear mappings between function spaces. However, the DeepONet requires many parameters and has …

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George Karniadakis, BINNS: Biophysics-Informed Neural Networks

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

Abstract: We will present a new approach to develop a data-driven, learning-based framework for predicting outcomes of biophysical systems and for discovering hidden mechanisms and pathways from noisy data. We will introduce a deep learning approach based on neural networks (NNs) and on generative adversarial networks (GANs). Unlike other approaches that rely on big data, …

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Yun Min Song, The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms

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

We will discuss about “The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms”,Rombouts, Jan, Sarah Verplaetse, and Lendert Gelens., bioRxiv (2023) Abstract Many biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on …

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Hyun Kim, Comparison of transformations for single-cell RNA-seq data

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

We will discuss about “Comparison of transformations for single-cell RNA-seq data”,Ahlmann-Eltze, Constantin, and Wolfgang Huber, Nature Methods (2023): 1-8. Abstract The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and …

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Seokjoo Chae, Improving gene regulatory network inference and assessment: The importance of using network structure

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

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 …

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Hans P.A. Van Dongen, Modeling the temporal dynamics of neurobehavioral performance impairment due to sleep loss and circadian misalignment

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

Abstract: The well-known two-process model of sleep regulation makes accurate predictions of sleep timing and duration, as well as neurobehavioral performance, for a variety of acute sleep deprivation and nap sleep scenarios, but it fails to predict the effects of chronic sleep restriction on neurobehavioral performance. The two-process model belongs to a broader class of …

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IBS 의생명수학그룹 Biomedical Mathematics Group
기초과학연구원 수리및계산과학연구단 의생명수학그룹
대전 유성구 엑스포로 55 (우) 34126
IBS Biomedical Mathematics Group (BIMAG)
Institute for Basic Science (IBS)
55 Expo-ro Yuseong-gu Daejeon 34126 South Korea
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