Timothy L. Downing, Biophysical Regulation of Cell Fate, from ECM to Nuclear Chromatin
2024-05-27 16:00 - 17:00 (Asia/Seoul) | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract: The Downing lab investigates the intricate biophysical interactions between cells and their environment, elucidating their role in modulating adult cell behavior and phenotypic transitions via epigenetic regulation of gene expression. Leveraging diverse genome-scale sequencing techniques, we decipher mechanisms underlying cell fate transitions mediated through dynamic regulation of nuclear chromatin and heterogeneous gene activity. Our research endeavors aim to engineer molecular tools and biomaterials to synthetically modulate the epigenome, enhancing control over cell fate and behavior. In this seminar presentation, I will focus on how signaling pathways governing cell-cell and cell-ECM communication contribute to observed fate transitions during the acquisition of stemness phenotypes and lineage plasticity, particularly in iPSC reprogramming and cancer contexts.

Lucas MacQuarrie, Data driven governing equations approximation using deep neural networks
2024-05-31 14:00 - 16:00 | Daejeon
We will discuss about “Data driven governing equations approximation using deep neural networks” Journal of Computational Physics (2019).
Abstract

We present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be considered as a one-step method that is exact in temporal integration. We then present two multi-step methods, recurrent ResNet (RT-ResNet) method and recursive ReNet (RS-ResNet) method. The RT-ResNet is a multi-step method on uniform time steps, whereas the RS-ResNet is an adaptive multi-step method using variable time steps. All three methods presented here are based on integral form of the underlying dynamical system. As a result, they do not require time derivative data for equation recovery and can cope with relatively coarsely distributed trajectory data. Several numerical examples are presented to demonstrate the performance of the methods.

Olive Cawiding, Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe
2024-06-07 14:00 - 16:00 | Daejeon
In this talk, we discuss the paper “Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe”, by Xiaojie Qiu et.al., Cell Syst. 2020.
Abstract
Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for “pseudotime”-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as “RNA velocity” restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.

Hyun Kim, MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing datamics data with TDEseq
2024-06-14 14:00 - 16:00 | Daejeon
In this talk, we discuss the paper, “MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data” by Siyao Liu et.al. Genome Biology, 2024.
Abstract
Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.

Brenda Gavina, A modified shuffled frog leaping algorithm with inertia weight
2024-06-21 14:00 - 16:00 | Daejeon
In this talk, we will discuss the paper, “A modified shuffled frog leaping algorithm with inertia weight”, by Zhuanzhe Zhao et.al. , Scientific Reports, 2024.
Abstract
The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

Hyungsuk Tak, Statistical Challenges in Astronomical Time Delay Estimation (Cancelled)
2024-06-25 14:00 - 15:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
I present time delay estimation problems in astronomy as a part of time delay cosmography to infer the Hubble constant, the current expansion rate of the Universe. Time delay cosmography is based on strong gravitational lensing, an effect that multiple images of the same astronomical object appear in the sky because paths of the light (from the object to the Earth) are bent by the strong gravitational field of an intervening galaxy. By measuring brightness of multiply-lensed images, we obtain several time series data of brightness, and time delays can be inferred by modeling these data. I focus on challenges in modeling these time series data and computational issues in fitting the models. In particular, I explain continuous-time auto-regressive models to account for stochastic variability of the time series data, and several Monte Carlo samplers to sample from the target posterior distributions with multiple modes. At the end of the talk, I show how these time delays estimates contribute to the Hubble constant estimation. Two main references of this talk are arXiv2207.09327 and arXiv2308.13018.

Recent Advances in Methods for Biomedical Mathematics
2024-06-27 08:00 - 2024-06-29 17:00 | IBS Science Culture Center, Expo-ro 55, Daejeon, Korea, Republic of
This satellite workshop for 2024 SMB-KSMB Annual Meeting will be held at IBS in Daejeon, conveniently located about an hour from Seoul by train or approximately three hours from Incheon airport by bus. We’re planning a comprehensive program, featuring six instructive tutorials, each lasting two hours. These sessions will cover various methodologies pivotal to our research, including but not limited to data analysis and data simulation. Additionally, there will be a poster session open to all attendees.
The workshop website can be accessed here: https://sites.google.com/view/advancesinmethodsforbiomedmath/home

Seokjoo Chae, Holimap: an accurate and efficient method for solving stochastic gene network dynamics
2024-07-12 14:00 - 16:00 | Daejeon
In this talk, we discuss the paper “Holimap: an accurate and efficient method for solving stochastic gene network dynamics” by Chen Jia and Ramon Grima, bioRxiv, 2024.
Abstract
Gene-gene interactions are crucial to the control of sub-cellular processes but our understanding of their stochastic dynamics is hindered by the lack of simulation methods that can accurately and efficiently predict how the distributions of protein numbers for each gene vary across parameter space. To overcome these difficulties, here we present Holimap (high-order linear-mapping approximation), an approach that approximates the protein number distributions of a complex gene network by the distributions of a much simpler reaction system. We demonstrate Holimap’s computational advantages over conventional methods by applying it to predict the stochastic time-dependent protein dynamics of several gene regulatory networks, ranging from simple autoregulatory loops to complex randomly connected networks. Holimap is ideally suited to study how the intricate network of gene-gene interactions results in precise coordination and control of gene expression.

Dongju Lim, Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics.
2024-07-19 14:00 - 16:00 | Daejeon
In this talk, we discuss the paper “Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics” by D. F. Anderson, B. Ermentrout and P. J. Thomas, Journal of Computational Neuroscience, 2015.
Abstract
In this paper we provide two representations for stochastic ion channel kinetics, and compare the perfor- mance of exact simulation with a commonly used numer- ical approximation strategy. The first representation we present is a random time change representation, popular- ized by Thomas Kurtz, with the second being analogous to a “Gillespie” representation. Exact stochastic algorithms are provided for the different representations, which are prefer- able to either (a) fixed time step or (b) piecewise constant propensity algorithms, which still appear in the literature. As examples, we provide versions of the exact algorithms for the Morris-Lecar conductance based model, and detail the error induced, both in a weak and a strong sense, by the use of approximate algorithms on this model. We include ready-to-use implementations of the random time change algorithm in both XPP and Matlab. Finally, through the consideration of parametric sensitivity analysis, we show how the representations presented here are useful in the development of further computational methods. The gen- eral representations and simulation strategies provided here are known in other parts of the sciences, but less so in the present setting.

Eui Min Jeong, Temperature compensation through kinetic regulation in biochemical oscillators.
2024-07-26 14:00 - 16:00 | Daejeon
In this talk, we discuss the paper “Temperature compensation through kinetic regulation in biochemical oscillators” by HaochenFu, Chenyi Fei, Qi Ouyang, and Yuhai Tu, to appear in PNAS.
Abstract
Although individual kinetic rates in biochemical reactions are sensitive to temperature, most circadian clocks exhibit a relatively constant period across a wide range of temperatures, a phenomenon called temperature compensation (TC). However, it remains unclear how different biochemical oscillators achieve TC. In this study, using representative biochemical oscillator models with different underlying reaction networks, we demonstrate a general kinetic regulation mechanism for TC regardless of the network structure. We find that by driving the system into a regime far from onset where the period increases strongly with at least one of the kinetic rates in the system to balance its inverse dependence on other rates, robust TC can be achieved for a wide range of parameters in different networks.

Summer Intern workshop 2024
2024-07-31 10:30 - 12:00 | Daejeon
Presentor(s)
Mentor
Talk title

Jaehun Jeong
Gyuyoung Hwang
Analyzing coupled SCN cell frequencies of mammals for multi-step transcriptional model

Hyunsuk Choo, Yonghee Lee
Seok Joo Chae
Development of a data-driven causality detection method using Taken’s Theorem

Juhyeon Kim
Dongju Lim
Accurate initial condition for circadian pacemaker model estimating the circadian phase

Kyeongtae Ko
Dongju Lim
Accurate initial condition estimation of exposed individuals in SEIR model

Ashley L Lawas
Olive R Cawiding
Advancing causal inference in complex systems through ODE-based methods

Sieun Lee
Olive R Cawiding
Improving efficiency of sleep disorder diagnosis via SymScore

Daniel Shin, Anar Rzayev
Olive R Cawiding
Improving Sleep Disorder Diagnosis Questionnaire (SLEEPS) by integrating Lifestyle Factors into Machine-learning algorithms

Shubhangi Kumar
Pan Li
Modelling cardiac pacemaking dysfunction in heart failure progression

Yejin Lee
Pan Li
Modelling beta-adrenergic regulation of calcium dynamics in human ventricular myocytes

Hyungu Lee
Yun Min Song
PSG sleep pattern prediction from actigraphy data

Yujin Park
Yun Min Song
Validating the usefulness of anchor sleep from sleep-wake patterns using ESS

Yoon Kim
Yun Min Song
Predicting sleep onset latency

Hyukpyo Hong, Koopman representation: Linear representation – not an approximation – of nonlinear dynamics
2024-07-31 16:00 - 17:00 | Daejeon
Abstract: A system of ordinary differential equations (ODEs) is one of the most widely used tools to describe a deterministic dynamical system. In general, such ODEs involve nonlinear equations, which make analysis of dynamical systems difficult. In this talk, we introduce Koopman theory, which offers a linear representation – not an approximation – of nonlinear dynamics. In particular, we present a data-driven algorithm to find such a linear representation

Yun Min Song, RNA velocity of single cells
2024-08-02 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “RNA velocity of single sells” by Gioele La Manno et.al., Nature, 2018.
Abstract
RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling
2024-08-07 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract:
Wearable biosensors measure physiological variables with high temporal resolution over multiple days and are increasingly employed in clinical settings, such as continuous glucose monitoring in diabetes care. Such datasets bring new opportunities and challenges, and patients, clinicians, and researchers are today faced with a common challenge: how to best summarize and capture relevant information from multimodal wearable time series? Here, we aim to provide insights into individual glucose dynamics and their relationships with food and drink ingestion, time of day, and coupling with other physiological states such as physical and heart activity. To this end, we generate and analyze multiple wearable device data through the lens of a parsimonious mathematical model with interpretable components and parameters. A key innovation of our method is that the models are learned on a personalized level for each participant within a Bayesian framework, which enables the characterization of interindividual heterogeneity in features such as the glucose response time following meals or underlying circadian baseline rhythm. I will also describe how we are currently applying this framework in the context of gestational diabetes.

Gyuyoung Hwang, A universal description of stochastic oscillators
2024-08-09 14:00 - 16:00 | Daejeon
In this talk, we discuss the paper “A universal description of stochastic oscillators”, by Alberto Perez-Cervera et. al., PNAS, 2023.
Abstract
Many systems in physics, chemistry, and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms, for example, linear dynamics of a stable focus with fluctuations, limit-cycle systems perturbed by noise, or excitable systems in which random inputs lead to a train of pulses. Despite their diverse origins, the phenomenology of random oscillations can be strikingly similar. Here, we introduce a nonlinear transformation of stochastic oscillators to a complex-valued function Q1*(x) that greatly simplifies and unifies the mathematical description of the oscillator’s spontaneous activity, its response to an external time-dependent perturbation, and the correlation statistics of different oscillators that are weakly coupled. The function Q1* (x) is the eigenfunction of the Kolmogorov backward operator with the least negative (but nonvanishing) eigenvalue λ1 = μ1 + iω1. The resulting power spectrum of the complex-valued function is exactly given by a Lorentz spectrum with peak frequency ω1 and half-width μ1; its susceptibility with respect to a weak external forcing is given by a simple one-pole filter, centered around ω1; and the cross-spectrum between two coupled oscillators can be easily expressed by a combination of the spontaneous power spectra of the uncoupled systems and their susceptibilities. Our approach makes qualitatively different stochastic oscillators comparable, provides simple characteristics for the coherence of the random oscillation, and gives a framework for the description of weakly coupled oscillators.

Kevin Spinicci, SMSSVD : Submatrix selection singular value decomposition
2024-08-16 14:00 - 16:00 | Daejeon
In this talk, we discuss the paper, “SMSSVD : Submatrix selection singular value decomposition”, by Rasmus Henningsson and Magnus Fontes, Bioinformatics, 2019.
Abstract

Motivation
High throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data analysis. Existing methods, aimed at signal reconstruction and deconvolution, in general, are either supervised, contain parameters that need to be estimated or present other types of ad hoc features. We here introduce SubMatrix Selection Singular Value Decomposition (SMSSVD), a parameter-free unsupervised signal decomposition and dimension reduction method, designed to reduce noise, adaptively for each low-rank-signal in a given data matrix, and represent the signals in the data in a way that enable unbiased exploratory analysis and reconstruction of multiple overlaid signals, including identifying groups of variables that drive different signals.

Results
The SMSSVD method produces a denoised signal decomposition from a given data matrix. It also guarantees orthogonality between signal components in a straightforward manner and it is designed to make automation possible. We illustrate SMSSVD by applying it to several real and synthetic datasets and compare its performance to golden standard methods like PCA (Principal Component Analysis) and SPC (Sparse Principal Components, using Lasso constraints). The SMSSVD is computationally efficient and despite being a parameter-free method, in general, outperforms existing statistical learning methods.

Quantitative Ecology of Host-associated Microbiomes - Lei Dai
2024-09-04 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: The realization that microbiomes, associated with virtually all multicellular organisms, have tremendous impact on their host health is considered as one of the most important scientific discoveries in the last decade. The host associated microbiomes, composed of tens to hundreds of co-existing microbial species, are highly heterogenous at multiple scales (e.g. between different hosts and within a host). In this talk, I will share our recent works on understanding the heterogeneity of complex microbial communities, and how these conceptual and technological advances in microbial ecology pave the way for precision microbiome engineering to prevent and treat diseases.

Make Your Science Friendly: A Guide to Engaging Visuals - Sunghwan Bae
2024-09-05 10:00 - 11:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
The talk will be hybrid, participants may join via Zoom with the following link: https://us06web.zoom.us/j/99567630778?pwd=[REDACTED]

Olive Cawiding, Predicting multiple observations in complex systems through low-dimensional embeddings
2024-09-06 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, 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=[REDACTED]
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 and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.

Hyun Kim, Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage
2024-09-13 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, 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=[REDACTED]
Abstract
Cells must adopt flexible regulatory strategies to make decisions regarding their fate, including differentiation, apoptosis, or survival in the face of various external stimuli. One key cellular strategy that enables these functions is stochastic gene expression programs. However, understanding how transcriptional bursting, and consequently, cell fate, responds to DNA damage on a genome-wide scale poses a challenge. In this study, we propose an interpretable and scalable inference framework, DeepTX, that leverages deep learning methods to connect mechanistic models and scRNA-seq data, thereby revealing genome-wide transcriptional burst kinetics. This framework enables rapid and accurate solutions to transcription models and the inference of transcriptional burst kinetics from scRNA-seq data. Applying this framework to several scRNA-seq datasets of DNA-damaging drug treatments, we observed that fluctuations in transcriptional bursting induced by different drugs could lead to distinct fate decisions: IdU treatment induces differentiation in mouse embryonic stem cells by increasing the burst size of gene expression, while 5FU treatment with low and high dose increases the burst frequency of gene expression to induce cell apoptosis and survival in human colon cancer cells. Together, these results show that DeepTX can be used to analyze single-cell transcriptomics data and can provide mechanistic insights into cell fate decisions.

Brenda Gavina, Achieving Occam’s razor: Deep learning for optimal model reduction
2024-09-20 14:00 - 16:00 | Daejeon
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 is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to apply Occam’s razor to model parameters. Our method, FixFit, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model’s degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In three use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model and a dynamic model of blood glucose regulation. In the latter, we demonstrate the ability to fit the latent parameters to real data. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.

Novel approaches and technologies for the study of sleep and circadian rhythms in health and disease - Derk-Jan Dijk
2024-10-02 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
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 and combinations of data with mathematical models.

Dongju Lim, Mathematical model for the distribution of DNA replication origins
2024-10-04 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, 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 important in ensuring that replication is completed quickly. Cells are more vulnerable to DNA damage and other forms of stress while they are replicating their genome. This raises the possibility that the spatial distribution of origins is under selection pressure. In this paper we investigate the hypothesis that natural selection favors origin distributions leading to shorter replication times. Using a simple mathematical model, we show that this hypothesis leads to two main predictions about the origin distributions: that neighboring origins that are inefficient (less likely to fire) are more likely to be close to each other than efficient origins; and that neighboring origins with larger differences in firing times are more likely to be close to each other than origins with similar firing times. We test these predictions using next-generation sequencing data, and show that they are both supported by the data.

Eui Min Jeong, A frequency-amplitude coordinator and its optimal energy consumption for biological oscillators
2024-10-11 14:00 - 16:00 | Daejeon
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=[REDACTED]
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 frequency and amplitude of biorhythm prevents physiological dysfunctions or diseases. However, many works were devoted to modulate frequency exclusively whereas amplitude is usually ignored, although both quantities are equally significant for coordinating biological functions and outputs. Especially, a feasible method coordinating the two quantities concurrently and precisely is still lacking. Here, for the first time, we propose a universal approach to design a frequency-amplitude coordinator rigorously via dynamical systems tools. We consider both spatial and temporal information. With a single well-designed coordinator, they can be calibrated to desired levels simultaneously and precisely. The practical usefulness and efficacy of our method are demonstrated in representative neuronal and gene regulatory models. We further reveal its fundamental mechanism and optimal energy consumption providing inspiration for biorhythm regulation in future.

Interpretable Machine Learning-Based Scoring System for Clinical Decision Making - Nan Liu
2024-10-18 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
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 acceptability. This talk aims to introduce and demonstrate how interpretable machine learning can be used to create scoring systems for clinical decision making.

Yun Min Song - Noise robustness and metabolic load determine the principles of central dogma regulation
2024-10-25 14:00 - 15:00 | Daejeon
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=[REDACTED]
Abstract
The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Latent space dynamics identification - Youngsoo Choi
2024-10-30 15:00 - 16:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
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 that fit the reduced latent space data. Then, in the prediction phase, the identified differential equations are solved in the reduced space for a new parameter point and its solution is projected back to the full space. The efficiency of the LaSDI framework comes from the fact that the solution process in the prediction phase does not involve any full order model size. For the identification, various approaches are possible, e.g., a fixed form as in dynamic mode decomposition and thermodynamics-based LaSDI, a regression form as in sparse identification of nonlinear dynamics (SINDy) and weak SINDy, and a physics-driven form as projection-based reduced order model. Various physics prob- lems were accurately accelerated by the family of LaSDIs, achieving a speed-up of 1000x, e.g., kinetic plasma simulations, pore collapse, and computational fluid problems.

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
2024-11-01 14:00 - 15:00 | Daejeon
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=[REDACTED]
Abstract
Collective alignment of cell populations is a commonly observed phenomena in biology. An important example are aligning fibroblasts in healthy or scar tissue. In this work we derive and simulate a mechanistic agent-based model of the collective behaviour of actively moving and interacting cells, with a focus on understanding collective alignment. The derivation strategy is based on energy minimisation. The model ingredients are motivated by data on the behaviour of different populations of aligning fibroblasts and include: Self-propulsion, overlap avoidance, deformability, cell-cell junctions and cytoskeletal forces. We find that there is an optimal ratio of self-propulsion speed and overlap avoidance that maximises collective alignment. Further we find that deformability aids alignment, and that cell-cell junctions by themselves hinder alignment. However, if cytoskeletal forces are transmitted via cell-cell junctions we observe strong collective alignment over large spatial scales.

Cluster-based network modeling—From snapshots to complex dynamical systems - Olive R. Cawiding
2024-11-08 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, 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) bridging machine learning, network science, and statistical physics. CNM describes short- and long-term behavior and is fully automatable, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor, ECG heartbeat signals, Kolmogorov flow, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict, and control complex systems in all scientific fields.

Mathematical models for malaria - Jennifer Flegg
2024-11-13 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: The effect of malaria on the developing world is devastating. Each year there are more than 200 million cases and over 400,000 deaths, with children under the age of five the most vulnerable. Ambitious malaria elimination targets have been set by the World Health Organization for 2030. These involve the elimination of the disease in at least 35 countries. However, these malaria elimination targets rest precariously on being able to treat the disease appropriately; a difficult feat with the emergence and spread of antimalarial drug resistance, along with many other challenges. In this talk, I will introduce several statistical and mathematical models that can be used to monitor malaria transmission and to support malaria elimination. For example, I’ll present mechanistic models of disease transmission, statistical models that allow the emergence and spread of antimalarial drug resistance to be monitored, mechanistic models that capture the role of bioclimatic factors on the risk of malaria and optimal geospatial sampling schemes for future malaria surveillance. I will discuss how the results of these models have been used to inform public health policy and support ongoing malaria elimination efforts.
