Dongju Lim, The timing of cellular events: a stochastic vs deterministic perspective
2024-01-19 14:00 - 16:00 (Asia/Seoul) | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “The timing of cellular events: a stochastic vs deterministic perspective”, bioRxiv (2023): 2023-07.

Abstract
Changes in cell state are driven by key molecular events whose timing can often be measured experimentally. Of particular interest is the time taken for the levels of RNA or protein molecules to reach a critical threshold defining the triggering of a cellular event. While this mean trigger time can be estimated by numerical integration of deterministic models, these ignore intrinsic noise and hence their predictions may be inaccurate. Here we study the differences between deterministic and stochastic model predictions for the mean trigger times using simple models of gene expression, post-transcriptional feedback control, and enzyme-mediated catalysis. By comparison of the two predictions, we show that when promoter switching is present there exists a transition from a parameter regime where deterministic models predict a longer trigger time than stochastic models to a regime where the opposite occurs. Furthermore, the ratio of the trigger times of the two models can be large, particularly for auto-regulatory genetic feedback loops. Our theory provides intuitive insight into the origin of these effects and shows that deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells.

Eui Min Jeong, "Linear mapping approximation of gene regulatory networks with stochastic dynamics"
2024-01-26 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Linear mapping approximation of gene regulatory networks with stochastic dynamics”, Nature communications 9.1 (2018): 3305.

Abstract
The presence of protein–DNA binding reactions often leads to analytically intractable models of stochastic gene expression. Here we present the linear-mapping approximation that maps systems with protein–promoter interactions onto approximately equivalent systems with no binding reactions. This is achieved by the marriage of conditional mean-field approximation and the Magnus expansion, leading to analytic or semi-analytic expressions for the approximate time-dependent and steady-state protein number distributions. Stochastic simulations verify the method’s accuracy in capturing the changes in the protein number distributions with time for a wide variety of networks displaying auto- and mutual-regulation of gene expression and independently of the ratios of the timescales governing the dynamics. The method is also used to study the first-passage time distribution of promoter switching, the sensitivity of the size of protein number fluctuations to parameter perturbation and the stochastic bifurcation diagram characterizing the onset of multimodality in protein number distributions.

Yun Min Song, A trade-off in controlling upstream and downstream noise in signaling networks
2024-02-02 14:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “A trade-off in controlling upstream and downstream noise in signaling networks”, bioRxiv (2023): 2023-08.

Abstract
Signal transduction, underpinning the function of a variety of biological systems, is inevitably affected by fluctuations. It remains intriguing how the timescale of a signaling network relates to its capability of noise control, specifically, whether long timescale can average out fluctuation or accumulate fluctuation. Here, we consider two noise components of the signaling system: the upstream noise from the fluctuation of the input signal and the downstream noise from the stochastic fluctuations of the network. We discover a fundamental trade-off in controlling the upstream and downstream noise: a longer timescale of the signaling network can buffer upstream noise, while accumulate downstream noise. Moreover, we confirm that this trade-off relation exists in real biological signaling networks such as a fold-change detection circuit and the p53 activation signaling system.

Jong Kyoung Kim, Dissecting cellular heterogeneity and plasticity in adipose tissue
2024-02-05 14:00 - 15:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract: Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. Single-cell sequencing is expanding to combine genomic, epigenomic, and transcriptomic features with environmental cues from the same single cell. In this talk, I demonstrate how scRNA-seq can be applied to dissect cellular heterogeneity and plasticity of adipose tissue, and discuss related computational challenges.

IBS BIMAG 2024 Winter Internship Presentation
2024-02-08 09:00 - 11:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Program table:

Topic
Talk+Q&A
Presenter
Title

Model reduction
9:00-9:10
Hyeong Jun Jang
Accurate and precise estimation in enzyme inihibition

9:10-9:20
Seolah Shin
Beyond Homogeneity: Assessing the Validity of the Michaelis–Menten Rate Law in Spatially Heterogeneous Environments

Circadian rhythms & Sleep
9:20-9:35
Ahn Jong Seok, Kim Ju Hyeon
Estimating missed initial sleep data to guess accurate circadian phase marker in sleep circadian rhythm

9:35-9:45
Jihahm Yoo
Alertness Model Personalization via Physics-informed Neural Network

9:45-9:55
Abbas Abbasli
Temperature Compensation in Circadian Clocks: Challenging the Robustness-Plasticity Relationship in PER2 Phosphoswitch

Medical survey reduction
10:00-10:10
Sungmun Kim
SymScore: Bridging Machine Learning and Real-World Healthcare

10:10-10:17
Sieun Lee
Predicting the Risk of PTSD using Simplified Questionnaire via SymScore

Data analysis
10:17-10:27
Kyeong Tae Ko
Estimation of compartment E with delay in SEIR model

10:27-10:37
Hyun Suk Choo
Inferring the network structure of a small ecosystem using time series data

10:37-10:47
Faeyza R. Ardi
Development of a Python-Based scLENS and Its Integration with Multiple-Clustering Packages in a Python Environment

Kang MIn Lee, Oscillation in brain and its potential role in inter-areal communication
2024-02-14 11:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract: Through the past decades, electrophysiological experiments have revealed that extracellular electrical potential of brain show diverse rhythmic activity. Called ‘Local Field Potential(LFP)’, those rhythmic activities are thought to reflect populational activity of neurons. In this talk, I will introduce basic concepts on LFP and its generation mechanisms. Then, roles of LFP in brain inter-areal communication will be presented. Particularly, hypothesis on frequency specific communication and their experimental evidences will be main topics.

Olive Cawiding, Anticipating the occurrence and type of critical transitions
2024-02-16 14:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Anticipating the occurrence and type of critical transitions”, Science Advances 9.1 (2023): eabq4558.

Abstract
Critical transition can occur in many real-world systems. The ability to forecast the occurrence of transition is of major interest in a range of contexts. Various early warning signals (EWSs) have been developed to anticipate the coming critical transition or distinguish types of transition. However, no effective method allows to establish practical threshold indicating the condition when the critical transition is most likely to occur. Here, we introduce a powerful EWS, named dynamical eigenvalue (DEV), that is rooted in bifurcation theory of dynamical systems to estimate the dominant eigenvalue of the system. Theoretically, the absolute value of DEV approaches 1 when the system approaches bifurcation, while its position in the complex plane indicates the type of transition. We demonstrate the efficacy of the DEV approach in model systems with known bifurcation types and also test the DEV approach on various critical transitions in real-world systems.

Hyun Kim, A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples
2024-02-23 14:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples
”, Nature communications 14.1 (2023): 7286.

Abstract

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

Mark Alber, Combined multiscale mathematical modeling and experimental study of regulation mechanisms of shape formation during tissue development
2024-03-08 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: The regulation and maintenance of an organ’s shape and structure is a major outstanding question in developmental biology. The Drosophila wing imaginal disc serves as a powerful system for elucidating design principles of the shape formation in epithelial morphogenesis.

Brenda Lyn Gavina, Reduced model for female endocrine dynamics: Validation and functional variations
2024-03-12 16:30 - 18:30 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Reduced model for female endocrine dynamics: Validation and functional variations.” Mathematical Biosciences 358 (2023): 108979.
Abstract

A normally functioning menstrual cycle requires significant crosstalk between hormones originating in ovarian and brain tissues. Reproductive hormone dysregulation may cause abnormal function and sometimes infertility. The inherent complexity in this endocrine system is a challenge to identifying mechanisms of cycle disruption, particularly given the large number of unknown parameters in existing mathematical models. We develop a new endocrine model to limit model complexity and use simulated distributions of unknown parameters for model analysis. By employing a comprehensive model evaluation, we identify a collection of mechanisms that differentiate normal and abnormal phenotypes. We also discover an intermediate phenotype—displaying relatively normal hormone levels and cycle dynamics—that is grouped statistically with the irregular phenotype. Results provide insight into how clinical symptoms associated with ovulatory disruption may not be detected through hormone measurements alone.

Seokjoo Chae, Transcriptome-wide analysis of cell cycle-dependent bursty gene expression from single-cell RNA-seq data using mechanistic model-based inference
2024-03-22 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Transcriptome-wide analysis of cell cycle-dependent bursty gene expression from single-cell RNA-seq data using mechanistic model-based inference”, bioRxiv (2024)
Abstract
Bursty gene expression is quantified by two intuitive parameters: the burst frequency and the burst size. While these parameters are known to be cell-cycle dependent for some genes, a transcriptome-wide picture remains missing. Here we address this question by fitting a suite of mechanistic models of gene expression to mRNA count data for thousands of mouse genes, obtained by sequencing of single cells for which the cell-cycle position has been inferred using a deep-learning approach. This leads to the estimation of the burst frequency and size per allele in the G1 and G2/M cell-cycle phases, hence providing insight into the global patterns of transcriptional regulation. In particular, we identify an interesting balancing mechanism: on average, upon DNA replication, the burst frequency decreases by ≈ 50%, while the burst size increases by the same amount. We also show that for accurate estimation of the ratio of burst parameters in the G1 and G2/M phases, mechanistic models must explicitly account for gene copy number differences between cells but, surprisingly, additional corrections for extrinsic noise due to the coupling of transcription to cell age within the cell cycle or technical noise due to imperfect capture of RNA molecules in sequencing experiments are unnecessary.

Dongju Lim, Anti-Windup Protection Circuits for Biomolecular Integral Controllers
2024-03-29 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Anti-Windup Protection Circuits for Biomolecular Integral Controllers”, bioRxiv (2023).

Abstract
Robust Perfect Adaptation (RPA) is a desired property of biological systems wherein a system’s output perfectly adapts to a steady state, irrespective of a broad class of perturbations. Achieving RPA typically requires the deployment of integral controllers, which continually adjust the system’s output based on the cumulative error over time. However, the action of these integral controllers can lead to a phenomenon known as “windup”. Windup occurs when an actuator in the system is unable to respond to the controller’s commands, often due to physical constraints, causing the integral error to accumulate significantly. In biomolecular control systems, this phenomenon is especially pronounced due to the positivity of molecular concentrations, inevitable promoter saturation and resource limitations. To protect against such performance deterioration or even instability, we present three biomolecular anti-windup topologies. The underlying architectures of these topologies are then linked to classical control-theoretic anti-windup strategies. This link is made possible due the development of a general model reduction result for chemical reaction networks with fast sequestration reactions that is valid in both the deterministic and stochastic settings. The topologies are realized as chemical reaction networks for which genetic designs, harnessing the flexibility of inteins, are proposed. To validate the efficacy of our designs in mitigating windup effects, we perform simulations across a range of biological systems, including a complex model of Type I diabetic patients and advanced biomolecular proportional-integral-derivative (PID) controllers. This work lays a foundation for developing robust and reliable biomolecular control systems, providing necessary safety and protection against windup-induced instability.

Brian P. Delisle, Circadian Regulation of Cardiac Electrophysiology
2024-04-05 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Circadian rhythms in physiology and behavior are regulated by circadian clocks, ubiquitous molecular transcriptional-translational feedback loops that cycle with a periodicity of ~24 hours. Circadian clocks serve as cellular timekeepers regulating important cell-type specific functions. The phase of circadian rhythms and circadian clocks throughout the body are entrained to the light cycle by signals originating in the suprachiasmatic nucleus of the hypothalamus. The functional importance of circadian clocks in cardiomyocytes is underscored by the observation that genetic disruption of the circadian clock mechanism in mouse hearts alters the electrocardiogram (ECG), cardiac action potential, and size of individual ionic currents. This presentation discusses recent basic science studies showing how daily environmental, behavioral, and circadian rhythms impact cardiac electrophysiology and cardiac arrhythmogenesis at the systems, tissue, and molecular levels. These studies provide new insights into how daily environmental, behavioral, and circadian rhythms affect the timing of cardiovascular events, and they are starting to identify chronotherapeutic strategies that may mitigate the risk for cardiac arrhythmias.

Michael Chee, How Data from Sleep Trackers Can Transform Our Understanding of Sleep
2024-04-12 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Wearable health trackers have shifted from gadgets for sports enthusiasts to valuable health sentinels over the last few years and that transformation is gathering pace. What do these devices really measure about sleep? What types of devices are there, and which can we trust? Which of the many sleep measures reported, contribute to a better understanding of sleep, sleep habits and sleep health? How can sleep data improve personal and public health? What new uses of sensor data can we look forward to in coming years? I seek to shed light on these issues in a presentation that will focus on distinguishing scientific and health-oriented perspectives from consumer-facing ones.

Eui Min Jeong, Phenotypic switching in gene regulatory networks
2024-04-19 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Phenotypic switching in gene regulatory networks”, PNAS (2014).

Abstract
Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory.

Yun Min Song, An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells
2024-04-26 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells”, ArXiv (2023).

Abstract
Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets. Here we introduce a new method, ODeGP (Oscillation Detection using Gaussian Processes), which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene, thus highlighting our method’s ability to discover unexpected patterns. In its current implementation, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.

Pedro Mendes, Multiscale hybrid differential equation and agent-based models
2024-05-03 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Biological phenomena are notorious for crossing several temporal and spatial scales. While often it may be sufficient to focus on a single scale, it is not rare that we have to consider several scales simultaneously. Computational modeling and simulation of biological systems thus frequently requires to include diverse temporal and spatial scales. A popular approach in systems biology is to combine differential equations and agent-based models, where usually small sets of differential equations are used to represent the internal state of each cell, with the cells being represented as interacting autonomous agents on a lattice. This type of hybrid models allows for parallel solution of smaller sets of differential equations rather than the solution of a single but very large set of differential equations. At certain discrete times, the agents are allowed to communicate, and only then are the different sets of differential equations able to influence each other. This time discretization of the cell-cell interactions carries an inherent approximation error compared to the continuous interaction of these cells in the single model of a large set of coupled differential equations. Here we study this approximation error and investigate the conditions in which it becomes negligible, thus defining the domain where the multiscale approach is valid. The approach is illustrated with a classic model of Drosophila segment polarity network, where a model based on a full set of differential equations (the original version of that model) is compared with a hybrid model combining differential equations and agent-based approach (implemented with the open source software simulators Vivarium and COPASI). This study is also relevant to other hybrid simulations, such as those representing “whole-cell models”, where partitions may be done at other organizational scales.

(Cancelled) Sung Woong Cho - Estimating the distribution of parameters in differential equations with repeated cross-sectional data
2024-05-03 15:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
This presentation introduces an approach for estimating parameter distributions in dynamic systems modeled by differential equations. Traditional parameter estimation techniques often struggle with Repeated Cross-Sectional (RCS) data, characteristic of many real-world scenarios where continuous data collection is impractical or impossible. Previous approaches, like employing mean values or leveraging Gaussian Processes for time series generation, fail to capture system parameters’ true heterogeneity and distributions. We introduce a novel approach to infer accurate parameter distributions from RCS data. By constructing artificial trajectories from randomly selected observations at each time point and iteratively refining parameter estimates to minimize discrepancies between observed and modeled dynamics, our method enables the derivation of true parameter distributions even for RCS data. We demonstrate the efficacy of our method through its application to models including exponential growth, logistic population dynamics, and target cell-limited models with delayed virus production. Our findings offer a robust framework for understanding the full complexity of dynamic systems, paving the way for more precise and insightful analyses across various fields of study.

Jingyi Jessica Li, ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping
2024-05-10 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: In typical single-cell RNA-seq (scRNA-seq) data analysis, a clustering algorithm is applied to find discrete cell clusters as putative cell types, and then a statistical test is employed to identify the differentially expressed (DE) genes between the cell clusters. However, this common procedure suffers the “double dipping” issue: the same data are used twice to find discrete cell clusters as putative cell types and DE genes as potential cell-type marker genes, leading to false-positive cell-type marker genes even when the cell clusters are spurious. To overcome this challenge, we propose ClusterDE, a post-clustering DE method for controlling the false discovery rate (FDR) of identified DE genes regardless of clustering quality, which can work as an add-on to popular pipelines such as Seurat. The core idea of ClusterDE is to generate real-data-based synthetic null data containing only one cell type, in contrast to the real data, for evaluating the whole procedure of clustering followed by a DE test. Using comprehensive simulation and real data analysis, we show that ClusterDE has solid FDR control and the ability to identify canonical cell-type marker genes as top DE genes, distinguishing them from common housekeeping genes. Notably, the DE genes identified by ClusterDE are informative markers for discrete cell types and can guide the merging of spurious clusters. ClusterDE is fast, transparent, and adaptive to a wide range of clustering algorithms and DE tests.

Gyuyoung Hwang, Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming
2024-05-17 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming”, Cell (2019).

Abstract
Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.

Kévin SPINICCI, PenDA, a rank-based method for personalized differential analysis: Application to lung cancer
2024-05-24 14:00 - 16:00 | Daejeon
We will discuss about “PenDA, a rank-based method for personalized differential analysis: Application to lung cancer” Plos Computational Biology (2020).
Abstract

The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here, we propose a novel method, PenDA, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access, user-friendly R package is available at https://github.com/bcm-uga/penda.

Timothy L. Downing, Biophysical Regulation of Cell Fate, from ECM to Nuclear Chromatin
2024-05-27 16:00 - 17:00 | 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.
