Biomedical Mathematics Group

George Karniadakis, BINNS: Biophysics-Informed Neural Networks
2023-04-07 11:00 - 12:00 (Asia/Seoul) | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: We will present a new approach to develop a data-driven, learning-based framework for predicting outcomes of biophysical systems and for discovering hidden mechanisms and pathways from noisy data. We will introduce a deep learning approach based on neural networks (NNs) and on generative adversarial networks (GANs). Unlike other approaches that rely on big data, here we “learn” from small data by exploiting the information provided by the mathematical physics, e.g.., conservation laws, reaction kinetics, etc,. which are used to obtain informative priors or regularize the neural networks. We will demonstrate how we can train BINNs from multifidelity/multimodality data, and we will present several examples of inverse problems, e.g., in systems biology for diabetes and in biomechanics for non-invasive inference of thrombus material properties. We will also discuss how operator regression in the form of DeepOnet can be used to accelerate inference based on historical data and only a few new data, as well its generalization and transfer learning capacity.

Yun Min Song, The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms
2023-04-07 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms”,Rombouts, Jan, Sarah Verplaetse, and Lendert Gelens., bioRxiv (2023)
Abstract

Many biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on different parts of the biochemical network. Here, we mathematically compare time-delay models where the feedback affects production and degradation. We show a mathematical connection between the linear stability of the two models, and derive how both mechanisms impose different constraints on the production and degradation rates that allow oscillations. We show how oscillations are affected by the inclusion of a distributed delay, of double regulation (acting on production and degradation), and of enzymatic degradation.

Hyun Kim, Comparison of transformations for single-cell RNA-seq data
2023-04-14 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Comparison of transformations for single-cell RNA-seq data”,Ahlmann-Eltze, Constantin, and Wolfgang Huber, Nature Methods (2023): 1-8.
Abstract

The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe four transformation approaches based on the delta method, model residuals, inferred latent expression state and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties; however, in benchmarks using simulated and real-world data, it turns out that a rather simple approach, namely, the logarithm with a pseudo-count followed by principal-component analysis, performs as well or better than the more sophisticated alternatives. This result highlights limitations of current theoretical analysis as assessed by bottom-line performance benchmarks.

Seokjoo Chae, Improving gene regulatory network inference and assessment: The importance of using network structure
2023-04-21 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Improving gene regulatory network inference and assessment: The importance of using network structure”, Escorcia-Rodríguez, Juan M., et al., bioRxiv (2023): 2023-01.
Abstract

Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets.

Hans P.A. Van Dongen, Modeling the temporal dynamics of neurobehavioral performance impairment due to sleep loss and circadian misalignment
2023-04-28 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: The well-known two-process model of sleep regulation makes accurate predictions of sleep timing and duration, as well as neurobehavioral performance, for a variety of acute sleep deprivation and nap sleep scenarios, but it fails to predict the effects of chronic sleep restriction on neurobehavioral performance. The two-process model belongs to a broader class of coupled, non-homogeneous, first-order, ordinary differential equations (ODEs), which can capture the effects of chronic sleep restriction. These equations exhibit a bifurcation, which appears to be an essential feature of performance impairment due to sleep loss. The equations implicate a biological system analogous to two connected compartments containing interacting compounds with time-varying concentrations, such as the adenosinergic neuromodulator/receptor system, as a key mechanism for the regulation of neurobehavioral functioning under conditions of sleep loss. The equations account for dynamic interaction with circadian rhythmicity, and also provide a new approach to dynamically tracking the magnitude of sleep inertia upon awakening from restricted sleep. This presentation will describe the development of the ODE system and its experimental calibration and validation, and will discuss some novel predictions.

Understanding Trade-offs in Biological Information Processing
2023-05-01 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
High accuracy of major biological processes relies on the ability of the participating enzymatic molecules to preferentially select the correct substrate from a pool of chemically similar substrates by activating the so-called proofreading mechanisms. While the importance of such mechanisms is widely accepted, it is still unclear how evolution has optimized biological systems with respect to their characteristic properties. We developed a comprehensive first-passage theoretical framework that allowed us to quantitatively investigate the trade-offs between the three properties of enzymatic systems: error, speed, noise, and energy dissipation. Within this framework, we simultaneously analyzed the speed and accuracy of several fundamental biological processes, including DNA replication, transcription, tRNA charging, and tRNA selection during the translation. The results indicate that the speed-accuracy trade-off is not always observed contrary to typical assumptions. However, when the trade-off is present, the biological systems tend to optimize the speed rather than the accuracy of the processes, as long as the error level is tolerable. When systems function in a regime where no speed-accuracy trade-off is observed, constraints due to energy dissipation in the proofreading play a key role. Our theory demonstrates a universal Pareto front in error-dissipation trade-off and shows how naturally selected kinetic parameters position their system close to this boundary. Our findings, therefore, provide a new system-level picture of how complex biological processes are able to function so fast with high accuracy and low dissipation.

Uncovering the mechanisms of pattern formation and emergent collective behaviors in myxobacteria
2023-05-04 16:15 - 17:15 | KAIST E6-1 1501 Auditorium, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea, Republic of
Collective cell movement is critical to the emergent properties of many multicellular systems including microbial self-organization in biofilms, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Myxococcus xanthus is a model bacteria famous for its coordinated multicellular behavior resulting in dynamic patterns formation. For example, when starving millions of cells coordinate their movement to organize into fruiting bodies – aggregates containing tens of thousands of bacteria. Relating these complex self-organization patterns to the behavior of individual cells is a complex-reverse engineering problem that cannot be solved solely by experimental research. In collaboration with experimental colleagues, we use a combination of quantitative microscopy, image processing, agent-based modeling, and kinetic theory PDEs to uncover the mechanisms of emergent collective behaviors.

Kyongwon Kim, On sufficient graphical models
2023-05-08 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We introduce a sufficient graphical model by applying the recently developed nonlinear sufficient dimension reduction techniques to the evaluation of conditional independence. The graphical model is nonparametric in nature, as it does not make distributional assumptions such as the Gaussian or copula Gaussian assumptions. However, unlike a fully nonparametric graphical model, which relies on the high-dimensional kernel to characterize conditional independence, our graphical model is based on conditional independence given a set of sufficient predictors with a substantially reduced dimension. In this way we avoid the curse of dimensionality that comes with a high-dimensional kernel. We develop the population-level properties, convergence rate, and variable selection consistency of our estimate.
By simulation comparisons and an analysis of the DREAM 4 Challenge data set, we demonstrate that our method outperforms the existing methods when the Gaussian or copula Gaussian assumptions are violated, and its performance remains excellent in the high-dimensional setting.

Mogens Jensen, Droplet formation, DNA repair and chaos in CellsBD
2023-05-10 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: TBD

Hyukpyo Hong, Inference and uncertainty quantification of stochastic gene expression via synthetic models
2023-05-12 11:00 - 13:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Inference and uncertainty quantification of stochastic gene expression via synthetic models”, Öcal et al., J. R. Soc. Interface.
Abstract

Estimating uncertainty in model predictions is a central task in quantitativebiology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.

Dongju Lim, A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease
2023-05-19 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease”, Alexandersen, Christoffer G., et al., Journal of the Royal Society Interface 20.198 (2023): 20220607.
Abstract

Alzheimer’s disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-β and tau affect neurons at the cellular scale. On a larger scale, Alzheimer’s patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein, we model the spreading of both amyloid-β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-β and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-β and tau protein.

Pan Li, Modeling the circadian control of cardiac function
2023-05-22 12:00 - 13:00 | IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract: TBA

Thomas Philipp, Stochastic gene expression in lineage trees
2023-05-24 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Stochasticity in gene expression is an important source of cell-to-cell variability (or noise) in clonal cell populations. So far, this phenomenon has been studied using the Gillespie Algorithm, or the Chemical Master Equation, which implicitly assumes that cells are independent and do neither grow nor divide. This talk will discuss recent developments in modelling populations of growing and dividing cells through agent-based approaches. I will show how the lineage structure affects gene expression noise over time, which leads to a straightforward interpretation of cell-to-cell variability in population snapshots. I will also illustrate how cell cycle variability shapes extrinsic noise across lineage trees. Finally, I outline how to construct effective chemical master equation models based on dilution reactions and extrinsic variability that provide surprisingly accurate approximations of the noise statistics across growing populations. The results highlight that it is crucial to consider cell growth and division when quantifying cellular noise.

Nonparametric predictive model for sparse and irregular longitudinal data
2023-05-25 11:00 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We propose a kernel-based estimator to predict the mean response trajectory for sparse and irregularly measured longitudinal data. The kernel estimator is constructed by imposing weights based on the subject-wise similarity on L2 metric space between predictor trajectories, where we assume that an analogous fashion in predictor trajectories over time would result in a similar trend in the response trajectory among subjects. In order to deal with the curse of dimensionality caused by the multiple predictors, we propose an appealing multiplicative model with multivariate Gaussian kernels. This model is capable of achieving dimension reduction as well as selecting functional covariates with predictive significance. The asymptotic properties of the proposed nonparametric estimator are investigated under mild regularity conditions. We illustrate the robustness and flexibility of our proposed method via the simulation study and an application to Framingham Heart Study

Hyeontae Jo,Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning
2023-05-26 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning”, Zhao, Shuai, et al., IEEE Transactions on Power Electronics 37.10 (2022): 11567-11578.
Abstract
Physics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics, this article proposes a PIML-based parameter estimation method demonstrated by a case study of dc–dc Buck converter. A deep neural network and the dynamic models of the converter are seamlessly coupled. It overcomes the challenges related to training data, accuracy, and robustness which a typical data-driven approach has. This exemplary application envisions to provide a new perspective for tailoring existing machine learning tools for power electronics.

Trivial but not trivial things in data science: From a statistical perspective
2023-05-30 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
TBA

Dongju Lim, Eui Min Jeong, Hyeontae Jo
2023-05-31 12:00 - 13:00 | Tea Room, IBS, Daejeon, Daejeon, 34141, Korea, Republic of
Dongju Lim: Mood Prediction for Bipolar Disorder Patient with Sleep Pattern Information

Eui Min Jeong:Noise attenuation through the multiple repression mechanism in transcription

Hyeontae Jo: Parameter estimation with discontinuously switching system

Eui Min Jung, Uncovering specific mechanisms across cell types in dynamical models
2023-06-02 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Uncovering specific mechanisms across cell types in dynamical models”, Hauber, Adrian Lukas, Marcus Rosenblatt, and Jens Timmer., bioRxiv (2023): 2023-01.
Abstract
Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results.
To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements.

Seonjin Kim, Nonparametric vs Parametric Regression
2023-06-08 11:00 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
To understand nonparametric regression, we should know first what the parametric model is. Simply speaking, the parametric regression model consists of many assumptions and the nonparametric regression model eases the assumptions. I will introduce what assumptions the parametric regression model has and how the nonparametric regression model relieves them. In addition, their pros and cons will be also presented.

Sushmita Roy, Deciphering gene regulatory networks underlying cell-fate specification
2023-06-09 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Cell fate specification is a dynamic process during which gene regulatory networks (GRNs) transition between different states and define cell type-specific patterns of gene expression. Identifying such cell type-specific gene regulatory networks is important for understanding how cells differentiate to diverse lineages from a progenitor state, how differentiated cells can be reprogrammed, and how these networks get disrupted in diseases such as cancer and developmental disorders. The advent of single cell omics has enabled us to perform high-throughput molecular phenotyping of individual cells at different omic levels. These technologies have revolutionized our understanding of cell type composition across diverse normal and disease conditions; however inferring cell type-specific networks and their dynamics from single cell omic datasets is an open challenge. I will present some of our recent efforts for inference and analysis of cell type-specific regulatory networks from single cell omic datasets. Application of our approach to hematopoietic differentiation and mouse cellular reprogramming predicted key regulatory nodes likely important for establishing different cell-type specific expression programs.

Seokjoo Chae, The energy cost and optimal design of networks for biological discrimination
2023-06-09 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “The energy cost and optimal design of networks for biological discrimination”, Yu, Qiwei, Anatoly B. Kolomeisky, and Oleg A. Igoshin., Journal of the Royal Society Interface 19.188 (2022): 20210883.
Abstract

Many biological processes discriminate between correct and incorrect substrates through the kinetic proofreading mechanism that enables lower error at the cost of higher energy dissipation. Elucidating physico-chemical constraints for global minimization of dissipation and error is important for understanding enzyme evolution. Here, we identify theoretically a fundamental error–cost bound that tightly constrains the performance of proofreading networks under any parameter variations preserving the rate discrimination between substrates. The bound is kinetically controlled, i.e. completely determined by the difference between the transition state energies on the underlying free energy landscape. The importance of the bound is analysed for three biological processes. DNA replication by T7 DNA polymerase is shown to be nearly optimized, i.e. its kinetic parameters place it in the immediate proximity of the error–cost bound. The isoleucyl-tRNA synthetase (IleRS) of E. coli also operates close to the bound, but further optimization is prevented by the need for reaction speed. In contrast, E. coli ribosome operates in a high-dissipation regime, potentially in order to speed up protein production. Together, these findings establish a fundamental error–dissipation relation in biological proofreading networks and provide a theoretical framework for studying error–dissipation trade-off in other systems with biological discrimination.

Hyun Kim
2023-06-12 12:00 - 13:00 | Tea Room, IBS, Daejeon, Daejeon, 34141, Korea, Republic of
TBD

Abbas Abbasli and Hyeongjun Jang
2023-06-19 12:00 - 13:00 | Tea Room, IBS, Daejeon, Daejeon, 34141, Korea, Republic of
Abbas Abbasli: Accurate prediction of in-vivo drug interaction mediated by cytochrome P450 inhibition
Hyeongjun Jang: Comparison of the inhibition constant approximation methods

Dae Wook kim, "Wearable data science for personalized digital medicine"
2023-06-22 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Wearable data science for personalized digital medicine”
Abstract
Millions of people currently use wearables such as the Apple Watch to monitor their physical activity, heart rate, and other physiological signals, generating an unprecedented amount of wearable data. This presents an opportunity for digital medicine to advance precision medicine. However, the noisy nature of this wearable data makes it appear unusable without new mathematical techniques to extract key signals from it. In this talk, I will discuss several techniques we have developed for analyzing this noisy time-series data, including the level-set Kalman filter-based data assimilation technique – a new state space estimation method that can estimate the phase of circadian rhythms. Additionally, I will introduce a Kalman filter-assisted autoencoder used for anomaly detection in time-series data, as well as feature engineering based on persistent homology and mathematical modeling. These techniques have practical applications, such as sleep scoring, detection of physiological changes related to COVID-19, and daily mood prediction.

2023 KSIAM-NIMS School on Biomathematics: Statistical Tools for Mathematical Modeling
2023-06-24 13:30 - 2023-06-26 13:00 | Jeju, Korea, Republic of
2023 KSIAM-NIMS School on Biomathematics: Statistical Tools for Mathematical Modeling
2023년 6월 24일 – 6월 26일, 3일간 제주 베스트 웨스턴 호텔에서 2023 KSIAM-NIMS School on Biomathematics: Statistical Tools for Mathematical Modeling이 열립니다.
이 튜토리얼에서는 신시내티 대학교의 장원 교수님과 연세대학교의 박재우 교수님께서 3일간 강의를 해주실 예정입니다. 모든 강의는 한국어로 진행됩니다.
자세한 일정은 아래 홈페이지에서 확인하실 수 있습니다.
https://ksiam.org/Conference/ConferenceView.asp?AC=1&CODE=CS20230401&CpPage=210#CONF
—
We are pleased to announce the 2023 KSIAM-NIMS School on Biomathematics: Statistical Tools for Mathematical Modeling. This three-day tutorial will take place from June 24th to 26th, 2023, at the Jeju Best Western Hotel in Jeju, Korea.
The tutorial will feature serial talks from two distinguished speakers, Won Chang from the University of Cincinnati and Jaewoo Park from Yonsei University. The program will be delivered in Korean.
This tutorial is designed for those interested in learning about statistical tools for mathematical modeling in biomathematics. It will cover a range of topics including parameter estimation, hypothesis testing, and model selection, among others. The program schedule is available on the website: https://ksiam.org/Conference/ConferenceView.asp?AC=1&CODE=CS20230401&CpPage=210#CONF
We hope you’ll join us for this exciting tutorial and look forward to seeing you in Jeju in June 2023!

Hyukpyo Hong and Seokmin Ha
2023-07-05 12:00 - 13:00 | Tea Room, IBS, Daejeon, Daejeon, 34141, Korea, Republic of
Hyukpyo Hong: Advancing Infectious Disease Modeling: Estimating Reproduction Number with Realistic Latent and Infectious Periods
Seokmin Ha: Systematic inference-driven experiments reveal a fundamental mechanism governing clock protein interactions in plants

Hyun Kim, scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching
2023-07-07 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching”, Karin, Jonathan, Yonathan Bornfeld, and Mor Nitzan., Nature Biotechnology (2023): 1-10.
Abstract

Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell–cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrisma’s flexibility in incorporating prior knowledge, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis.

Yun Min Song, The singularity response reveals entrainment properties of the plant circadian clock
2023-07-28 10:00 - 11:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “The singularity response reveals entrainment properties of the plant circadian clock”, Masuda, Kosaku, et al., Nature Communications 12.1 (2021): 864.
Abstract

Circadian clocks allow organisms to synchronize their physiological processes to diurnal variations. A phase response curve allows researchers to understand clock entrainment by revealing how signals adjust clock genes differently according to the phase in which they are applied. Comprehensively investigating these curves is difficult, however, because of the cost of measuring them experimentally. Here we demonstrate that fundamental properties of the curve are recoverable from the singularity response, which is easily measured by applying a single stimulus to a cellular network in a desynchronized state (i.e. singularity). We show that the singularity response of Arabidopsis to light/dark and temperature stimuli depends on the properties of the phase response curve for these stimuli. The measured singularity responses not only allow the curves to be precisely reconstructed but also reveal organ-specific properties of the plant circadian clock. The method is not only simple and accurate, but also general and applicable to other coupled oscillator systems as long as the oscillators can be desynchronized. This simplified method may allow the entrainment properties of the circadian clock of both plants and other species in nature.

Yun Min Song and Seokjoo Chae
2023-07-31 12:00 - 13:00 | Tea Room, IBS, Daejeon, Daejeon, 34141, Korea, Republic of
Yun Min Song: Noisy delay denoises biochemical oscillators
Seokjoo Chae: Reduction of spatiotemporal model and its validity condition

Seokhwan Moon, The Internal Model Principle for Biomolecular Control Theory
2023-08-04 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “ The Internal Model Principle for Biomolecular Control Theory ”, Gupta, Ankit, and Mustafa Khammash., IEEE Open Journal of Control Systems 2 (2023): 63-69.

Abstract
The well-known Internal Model Principle (IMP) is a cornerstone of modern control theory. It stipulates the necessary conditions for asymptotic robustness of disturbance-prone dynamical systems by asserting that such a system must embed a subsystem in a feedback loop, and this subsystem must be able to reduplicate the dynamic disturbance using only the regulated variable as the input. The insights provided by IMP can help in both designing suitable controllers and also in analysing the regulatory mechanisms in complex systems. So far the application of IMP in biology has been case-specific and ad hoc, primarily due to the lack of generic versions of the IMP for biomolecular reaction networks that model biological processes. In this short article we highlight the need for an IMP in biology and discuss a recently developed version of it for biomolecular networks that exhibit maximal Robust Perfect Adaptation (maxRPA) by being robust to the maximum number of disturbance sources.
