Alfio Quarteroni, Physics-based and data-driven numerical models for computational medicine
2023-11-22 16:00 - 17:00 (Asia/Seoul) | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: I will report on some recent results on modelling the heart, the external circulation, and their application to problems of clinical relevance. I will show that a proper integration between PDE-based and machine-learning algorithms can improve the computational efficiency and enhance the generality of our iHEART simulator.

Dongju Lim, An accurate probabilistic step finder for time-series analysis
2023-11-24 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “An accurate probabilistic step finder for time-series analysis.” bioRxiv (2023): 2023-09.
Abstract

Noisy time-series data is commonly collected from sources including Förster Resonance Energy Transfer experiments, patch clamp and force spectroscopy setups, among many others. Two of the most common paradigms for the detection of discrete transitions in such time-series data include: hidden Markov models (HMMs) and step-finding algorithms. HMMs, including their extensions to infinite state-spaces, inherently assume in analysis that holding times in discrete states visited are geometrically–or, loosely speaking in common language, exponentially–distributed. Thus the determination of step locations, especially in sparse and noisy data, is biased by HMMs toward identifying steps resulting in geometric holding times. In contrast, existing step-finding algorithms, while free of this restraint, often rely on ad hoc metrics to penalize steps recovered in time traces (by using various information criteria) and otherwise rely on approximate greedy algorithms to identify putative global optima. Here, instead, we devise a robust and general probabilistic (Bayesian) step-finding tool that neither relies on ad hoc metrics to penalize step numbers nor assumes geometric holding times in each state. As the number of steps themselves in a time-series are, a priori unknown, we treat these within a Bayesian nonparametric (BNP) paradigm. We find that the method developed, Bayesian Nonparametric Step (BNP-Step), accurately determines the number and location of transitions between discrete states without any assumed kinetic model and learns the emission distribution characteristic of each state. In doing so, we verify that BNP-Step can analyze sparser data sets containing higher noise and more closely-spaced states than otherwise resolved by current state-of-the-art methods. What is more, BNP-Step rigorously propagates measurement uncertainty into uncertainty over state transition locations, numbers, and emission levels as characterized by the posterior. We demonstrate the performance of BNP-Step on both synthetic data as well as data drawn from force spectroscopy experiments.

Eui Min Jung, Hard Limits and Performance Tradeoffs in a Class of Antithetic Integral Feedback Networks
2023-12-01 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Hard limits and performance tradeoffs in a class of antithetic integral feedback networks.” Cell systems 9.1 (2019): 49-63.
Abstract

Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism called antithetic integral feedback, which can be implemented using the binding of two molecules. Our work develops an analytic framework for understanding the hard limits, performance tradeoffs, and architectural properties of this simple model of biological feedback control. Using tools from control theory, we show that there are simple parametric relationships that determine both the stability and the performance of these systems in terms of speed, robustness, steady-state error, and leakiness. These findings yield a holistic understanding of the behavior of antithetic integral feedback and contribute to a more general theory of biological control systems.

Robyn P. Araujo, Cellular cognition and the simple complexity of the networks of life
2023-12-08 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: TBD

Olive Cawiding, Time delays modulate the stability of complex ecosystems
2023-12-08 15:30 - 17:30 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Time delays modulate the stability of complex ecosystems” Nature Ecology & Evolution 7.10 (2023): 1610-1619.
Abstract
What drives the stability, or instability, of complex ecosystems? This question sits at the heart of community ecology and has motivated a large body of theoretical work exploring how community properties shape ecosystem dynamics. However, the overwhelming majority of current theory assumes that species interactions are instantaneous, meaning that changes in the abundance of one species will lead to immediate changes in the abundances of its partners. In practice, time delays in how species respond to one another are widespread across ecological contexts, yet the impact of these delays on ecosystems remains unclear. Here we derive a new body of theory to comprehensively study the impact of time delays on ecological stability. We find that time delays are important for ecosystem stability. Large delays are typically destabilizing but, surprisingly, short delays can substantially increase community stability. Moreover, in stark contrast to delay-free systems, delays dictate that communities with more abundant species can be less stable than ones with less abundant species. Finally, we show that delays fundamentally shift how species interactions impact ecosystem stability, with communities of mixed interaction types becoming the most stable class of ecosystem. Our work demonstrates that time delays can be critical for the stability of complex ecosystems.

Yun Min Song, Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination
2023-12-15 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination” bioRxiv (2023): 2023-10.
Abstract

Entrainment to an external stimulus enables a synchronized oscillatory response across a population of cells, increasing coherent responses by reducing cell-to-cell heterogeneity. It is unclear whether the property of entrainability extends to systems where responses are intrinsic to the individual cell, rather than dependent on coherence across a population of cells. Using a combination of mathematical modeling, time-lapse fluorescence microscopy, and single-cell tracking, we demonstrated that p53 oscillations triggered by DNA double-strand breaks (DSBs) can be entrained with a periodic damage stimulus, despite such synchrony not known to function in effective DNA damage responses. Surprisingly, p53 oscillations were experimentally entrained over a wider range of DSB frequencies than predicted by an established computational model for the system. We determined that recapitulating the increased range of entrainment frequencies required, non-intuitively, a less robust oscillator and wider steady-state valley on the energy landscape. Further, we show that p53 entrainment can lead to altered expression dynamics of downstream targets responsible for cell fate in a manner dependent on target mRNA stability. Overall, this study demonstrates that entrainment can occur in a biological oscillator despite the apparent lack of an evolutionary advantage conferred through synchronized responses and highlights the potential of externally entraining p53 dynamics to reduce cellular variability and synchronize cell-fate responses for therapeutic outcomes.

Hyun Kim, MultiVI: deep generative model for the integration of multimodal data
2023-12-29 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “MultiVI: deep generative model for the integration of multimodal data” Nature Methods 20.8 (2023): 1222-1231.
Abstract

Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data, even for cells for which one or more modalities are missing. It is available at scvi-tools.org.

Hyeontae Jo, Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
2024-01-05 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery” IEEE Transactions on neural networks and learning systems 32.9 (2020): 4166-4177.
Abstract

Symbolic regression is a powerful technique to discover analytic equations that describe data, which can lead to explainable models and the ability to predict unseen data. In contrast, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing tasks, but they are often seen as black-box models that are difficult to interpret and typically extrapolate poorly. In this article, we use a neural network-based architecture for symbolic regression called the equation learner (EQL) network and integrate it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation. To demonstrate the power of such systems, we study their performance on several substantially different tasks. First, we show that the neural network can perform symbolic regression and learn the form of several functions. Next, we present an MNIST arithmetic task where a convolutional network extracts the digits. Finally, we demonstrate the prediction of dynamical systems where an unknown parameter is extracted through an encoder. We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared with a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery

Hyung Jin Choi, A Normative Framework Dissociates Need and Motivation in Hypothalamic Neurons
2024-01-09 10:00 - 11:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract: Physiological needs evoke motivational drives to produce natural behaviours for survival. However, the temporally intertwined dynamics of need and motivation have made it challenging to differentiate these two components in previous experimental paradigms. Based on classic homeostatic theories, we established a normative framework to derive computational models of neural activity and behaviours for need-encoding and motivation-encoding neurons during events that induce predicted gain or loss. We further developed simple and intuitive experimental paradigms that enabled us to distinguish the distinct roles of subpopulations of neurons in the hypothalamus. Our results show that AgRP neurons and LHLepR neurons are consistent with need and motivation, respectively. Our study provides a parsimonious understanding of how distinct hypothalamic neurons separately encode need and motivation to produce adaptive behaviours for maintaining homeostasis.

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]

Seokjoo Chae, AI Feynman: A physics-inspired method for symbolic regression
2024-01-12 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “AI Feynman: A physics-inspired method for symbolic regression”,Science Advances 6.16 (2020): eaay2631.
Abstract
A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90%.

Junil Kim, TENET+: a tool for reconstructing gene networks by integrating single cell expression and chromatin accessibility data
2024-01-17 11:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract: Reconstruction of gene regulatory networks (GRNs) is a powerful approach to capture a prioritized gene set controlling cellular processes. In our previous study, we developed TENET a GRN reconstructor from single cell RNA sequencing (scRNAseq). TENET has a superior capability to identify key regulators compared with other algorithms. However, accurate inference of gene regulation is still challenging. Here, we suggest an integrative strategy called TENET+ by combining single cell transcriptome and chromatin accessibility data. TENET+ predicts target genes and open chromatin regions associated with transcription factors (TFs) and links the target regions to their corresponding target gene. As a result, TENET+ can infer regulatory triplets of TF, target gene, and enhancer. By applying TENET+ to a paired scRNAseq and scATACseq dataset of human peripheral blood mononuclear cells, we found critical regulators and their epigenetic regulations for the differentiations of CD4 T cells, CD8 T cells, B cells and monocytes. Interestingly, not only did TENET+ predict several top regulators of each cell type which were not predicted by the motif-based tool SCENIC, but we also found that TENET+ outperformed SCENIC in prioritizing critical regulators by using a cell type associated gene list. Furthermore, utilizing and modeling regulatory triplets, we can infer a comprehensive epigenetic GRN. In sum, TENET+ is a tool predicting epigenetic gene regulatory programs for various types of datasets in an unbiased way, suggesting that novel epigenetic regulations can be identified by TENET+.
Github page: https://github.com/hg0426/TENETPLUS.

Dongju Lim, The timing of cellular events: a stochastic vs deterministic perspective
2024-01-19 14:00 - 16:00 | 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.
