Olive Cawiding, Predicting multiple observations in complex systems through low-dimensional embeddings

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper, "Predicting multiple observations in complex systems through low-dimensional embeddings", by Tao Wu et. al., Nature Communications, 2024. Zoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven

Hyun Kim, Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage" by Zhiwei Huang, et. al., bioRxiv, 2024. Zoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract Cells must adopt flexible regulatory strategies to make decisions regarding their fate, including differentiation, apoptosis, or survival in the face of

Brenda Gavina, Achieving Occam’s razor: Deep learning for optimal model reduction

In this talk, we discuss the paper "Achieving Occam’s razor: Deep learning for optimal model reduction" by Botond B. Antal et.al., PLOS Computational Biology, 2024. Abstract  All fields of science depend on mathematical models. Occam’s razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This

Novel approaches and technologies for the study of sleep and circadian rhythms in health and disease – Derk-Jan Dijk

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

Abstract: The study of sleep and circadian rhythms at scale requires novel technologies and approaches that are valid, cost effective and do not pose much of a burden to the participant. Here we will present our recent studies in which we have evaluated several classes of technologies and approaches including wearables, nearables, blood based biomarkers

Dongju Lim, Mathematical model for the distribution of DNA replication origins

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk we discuss the paper "Mathematical model for the distribution of DNA replication origins" by Alessandro de Moura and Jens Karschau, Physical Review E, 2024. Abstract  DNAreplication in yeast and in many other organisms starts from well-defined locations on the DNA known as replication origins. The spatial distribution of these origins in the genome is particularly

Eui Min Jeong, A frequency-amplitude coordinator and its optimal energy consumption for biological oscillators

In this talk, we discuss the paper, “A frequency-amplitude coordinator and its optimal energy consumption for biological oscillators”, by Bo-Wei Qin et. al., Nature Communications, 2021. Zoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract  Biorhythm including neuron firing and protein-mRNA interaction are fundamental activities with diffusive effect. Their well-balanced spatiotemporal dynamics are beneficial for healthy sustainability. Therefore, calibrating both anomalous

Interpretable Machine Learning-Based Scoring System for Clinical Decision Making – Nan Liu

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

Abstract: There has been an increased use of scoring systems in clinical settings for the purpose of assessing risks in a convenient manner that provides important evidence for decision making. Machine learning-based methods may be useful for identifying important predictors and building models; however, their 'black box' nature limits their interpretability as well as clinical

Yun Min Song – Noise robustness and metabolic load determine the principles of central dogma regulation

In this talk, we discuss the paper : "Noise robustness and metabolic load determine the principles of central dogma regulation" by Teresa W. Lo et al, Sci. Adv, https://doi.org/10.1126/sciadv.ado3095. Zoom: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light

Latent space dynamics identification – Youngsoo Choi

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

Abstravt: Latent space dynamics identification (LaSDI) is an interpretable data-driven framework that follows three distinct steps, i.e., compression, dynamics identification, and prediction. Compression allows high-dimensional data to be reduced so that they can be easily fit into an interpretable model. Dynamics identification lets you derive the interpretable model, usually some form of parameterized differential equations

Derivation and simulation of a computational model of active cell populations: How overlap avoidance, deformability, cell-cell junctions and cytoskeletal forces affect alignment – Kevin SPINICCI

In this talk, we discuss the paper : "Derivation and simulation of a computational model of active cell populations: How overlap avoidance, deformability, cell-cell junctions and cytoskeletal forces affect alignment" by Leech et al, nature biotechnology, https://doi.org/10.1371/journal.pcbi.1011879. Zoom: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract Collective alignment of cell populations is a commonly observed phenomena in biology. An important example

Cluster-based network modeling—From snapshots to complex dynamical systems – Olive R. Cawiding

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

Abstract: We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM)

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IBS Biomedical Mathematics Group (BIMAG)
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