Biomedical Mathematics Group

Hyukpyo Hong, Estimating and Assessing Differential Equation Models with Time-Course Data
2023-02-03 14:00 - 16:00 (Asia/Seoul) | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Estimating and Assessing Differential Equation Models with Time-Course Data”, Wong, Samuel WK, Shihao Yang, and S. C. Kou, arXiv preprint arXiv:2212.10653 (2022).
Abstract

Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertainty quantification. Second, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI’s efficient computation of model predictions. Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.

Dongju Lim, Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors
2023-02-10 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors”, Magal, Noa, et al., Chronic Stress 6 (2022): 24705470221100987.
Abstract

Background: Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior, potentially leading to impaired mental and physical health. Multiple physiological and behavioral lifestyle features can now be recorded unobtrusively in daily-life using wearable sensors. The aim of the current study was to identify a distinct set of physiological and behavioral lifestyle features that are associated with elevated levels of chronic stress across different stress sources.
Methods: For that, 140 healthy female participants completed the Trier inventory for chronic stress (TICS) before wearing the Fitbit Charge3 sensor for seven consecutive days while maintaining their daily routine. Physiological and lifestyle features that were extracted from sensor data, alongside demographic features, were used to predict high versus low chronic stress with support vector machine classifiers, applying out-of-sample model testing.
Results: The model achieved 79% classification accuracy for chronic stress from a social tension source. A mixture of physiological (resting heart-rate, heart-rate circadian characteristics), lifestyle (steps count, sleep onset and sleep regularity) and non-sensor demographic features (smoking status) contributed to this classification.
Conclusion: As wearable technologies continue to rapidly evolve, integration of daily-life indicators could improve our understanding of chronic stress and its impact of physiology and behavior.

Hyeontae Jo, Characterizing possible failure modes in physics-informed neural networks
2023-02-17 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Characterizing possible failure modes in physics-informed neural networks”, Krishnapriyan, Aditi, et al., Advances in Neural Information Processing Systems 34 (2021): 26548-26560.
Abstract

Recent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge as soft constraints on an empirical loss function and use existing machine learning methodologies to train the model. We demonstrate that, while existing PINN methodologies can learn good models for relatively trivial problems, they can easily fail to learn relevant physical phenomena for even slightly more complex problems. In particular, we analyze several distinct situations of widespread physical interest, including learning differential equations with convection, reaction, and diffusion operators. We provide evidence that the soft regularization in PINNs, which involves PDE-based differential operators, can introduce a number of subtle problems, including making the problem more ill-conditioned. Importantly, we show that these possible failure modes are not due to the lack of expressivity in the NN architecture, but that the PINN’s setup makes the loss landscape very hard to optimize. We then describe two promising solutions to address these failure modes. The first approach is to use curriculum regularization, where the PINN’s loss term starts from a simple PDE regularization, and becomes progressively more complex as the NN gets trained. The second approach is to pose the problem as a sequence-to-sequence learning task, rather than learning to predict the entire space-time at once. Extensive testing shows that we can achieve up to 1-2 orders of magnitude lower error with these methods as compared to regular PINN training.

Shinya Kuroda, Systems Biology of Insulin Action
2023-03-03 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract:
1. The “temporal information code” of insulin action: a bottom-up approach One of the essential elements of signaling networks is to encode information from a wide variety of inputs into a limited set of molecules. We have proposed a “temporal information code” that regulates a variety of physiological functions by encoding input information in temporal patterns of molecular activity, and based on this concept, we are analyzing biological homeostasis by insulin signaling. Taking blood insulin as an example, we will explain how the temporal information of blood insulin is selectively decoded by downstream networks.
2. Transomics of insulin action: a top-down approach In order to obtain a complete picture of insulin action, we performed transomics measurements integrating metabolomics and transcriptomics, and found that metabolism is regulated by allosteric regulation in the liver of normal mice and by compensatory gene expression in the liver of obese mice. (Top-down approach). I will talk about approach the principle of homeostasis of living organisms by temporal patterns, using the analysis of systems biology of insulin action using two different approaches.

Seho Park, Dynamical information enables inference of gene regulation at single-cell scale
2023-03-03 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”, Zhang, Stephen Y., and Michael PH Stumpf., bioRxiv (2023): 2023-01.
Abstract

Cellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem that is central to systems biology. The majority of existing network inference methods work at the population level and construct a static representations of gene regulatory networks; they do not naturally allow for inference of differential regulation across a heterogeneous cell population. Building upon recent dynamical inference methods that model single cell dynamics using Markov processes, we propose locaTE, an information-theoretic approach which employs a localised transfer entropy to infer cell-specific, causal gene regulatory networks. LocaTE uses high-resolution estimates of dynamics and geometry of the cellular gene expression manifold to inform inference of regulatory interactions. We find that this approach is generally superior to using static inference methods, often by a significant margin. We demonstrate that factor analysis can give detailed insights into the inferred cell-specific GRNs. In application to two experimental datasets, we recover key transcription factors and regulatory interactions that drive mouse primitive endoderm formation and pancreatic development. For both simulated and experimental data, locaTE provides a powerful, efficient and scalable network inference method that allows us to distil cell-specific networks from single cell data.

Martin Nowak, Evolution of cooperation
2023-03-10 10:00 - 11:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Cooperation means that one individual pays a cost for another to receive a benefit. Cooperation can be at variance with natural selection. Why should you help competitors? Yet cooperation is abundant in nature and is important component of evolutionary innovation. Cooperation can be seen as the master architect of evolution and as the third fundamental principle of evolution beside mutation and selection. I will present five mechanisms for the evolution of cooperation: direct reciprocity, indirect reciprocity, spatial selection, group selection and kin selection. Global cooperation and the cooperation with future generations is necessary to ensure the survival of our species.
Further reading:
Nowak MA (2006). Evolutionary Dynamics. Harvard University Press
Nowak MA & Highfield R (2011) SuperCooperators. Simon & Schuster.
Hauser OP, Rand DG, Peysakhovich A & Nowak MA (2014). Cooperating with the future. Nature 511: 220-223
Hilbe C, Šimsa Š, Chatterjee K & Nowak MA (2018). Evolution of cooperation in stochastic games. Nature 559: 246-249
Hauser OP, Hilbe C, Chatterjee K & Nowak MA (2019). Social dilemmas among unequals. Nature 572: 524-527

Eui Min Jung, Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks
2023-03-10 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks”, Briat, Corentin, Ankit Gupta, and Mustafa Khammash. Cell systems 2.1 (2016): 15-26.
Abstract

The ability to adapt to stimuli is a defining feature of many biological systems and critical to maintaining homeostasis. While it is well appreciated that negative feedback can be used to achieve homeostasis when networks behave deterministically, the effect of noise on their regulatory function is not understood. Here, we combine probability and control theory to develop a theory of biological regulation that explicitly takes into account the noisy nature of biochemical reactions. We introduce tools for the analysis and design of robust homeostatic circuits and propose a new regulation motif, which we call antithetic integral feedback. This motif exploits stochastic noise, allowing it to achieve precise regulation in scenarios where similar deterministic regulation fails. Specifically, antithetic integral feedback preserves the stability of the overall network, steers the population of any regulated species to a desired set point, and adapts perfectly. We suggest that this motif may be prevalent in endogenous biological circuits and useful when creating synthetic circuits.

Marko Ćosić, The morphological analysis of the collagen straightness in the colon mucosa away from the cancer
2023-03-13 11:00 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract: The morphological method – based on the topology and singularity theory and originally developed for the analysis of the scattering experiments – was extended to be applicable for the analysis of biological data. The usefulness of the topological viewpoint was demonstrated by quantification of the changes of collagen fiber straightness in the human colon mucosa (healthy mucosa, colorectal cancer, and uninvolved mucosa far from cancer).
This has been done by modeling the distribution of collagen segment angles by the polymorphic beta-distribution. Its shapes were classified according to the number and type of critical points. We found that biologically relevant shapes could be classified as shapes without any preferable orientation (i.e. shapes without local extrema), transitional forms (i.e. forms with one broad local maximum), and highly oriented forms (i.e. forms with two minima at both ends and one very narrow maximum between them). Thus, changes in the fiber organization were linked to the metamorphoses of the beta-distribution forms.
The obtained classification was used to define a new, shape-aware/based, measure of the collagen straightness, which revealed a slight, and moderate increase of the straightness in mucosa samples taken 20 cm and 10 cm away from the tumor. The largest increase of collagen straightness was found in samples of cancer tissue. Samples of the healthy individuals have a uniform distribution of beta-distribution forms. We found that this distribution has the maximal information entropy. At 20 cm and 10 cm away from cancer, the transition forms redistribute into unoriented and highly oriented forms. Closer to cancer the number of unoriented forms decreases rapidly leaving only highly oriented forms present in the samples of the cancer tissue, whose distribution has minimal information entropy. The polarization of the distribution was followed by a significant increase in the number of quasi-symmetrical forms in samples 20 cm away from cancer which decreases closer to cancer.
This work shows that the evolution of the distribution of the beta-distribution forms – an abstract construction of the mind – follows the familiar laws of statistical mechanics. Additionally, the polarization of the beta-distribution forms together with the described change in the number of quasi-symmetrical forms, clearly visible in the parametric space of the beta-distribution and very difficult to notice in the observable space, can be a useful indicator of the early stages in the development of colorectal cancer.

Julio Saez-Rodriguez, Dynamic logic models complement machine learning for personalized medicine
2023-03-15 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract:
Multi-omics technologies, and in particular those with single-cell and spatial resolution, provide unique opportunities to study the deregulation of intra- and inter-cellular signaling processes in disease. I will present recent methods and applications from our group toward this aim, focusing on computational approaches that combine data with biological knowledge within statistical and machine learning methods. This combination allows us to increase both the statistical power of our analyses and the mechanistic interpretability of the results. These approaches allow us to identify key processes, that can be in turn studied in detailed with dynamic mechanistic models. I will then present how cell-specific logic models, trained with measurements upon perturbations, can provides new biomarkers and treatment opportunities. Finally, I will show how, using novel microfluidics-based technologies, this approach can also be applied directly to biopsies, allowing to build mechanistic models for individual cancer patients, and use these models to prose new therapies.

Olive Cawiding, Single-sample landscape entropy reveals the imminent phase transition during disease progression
2023-03-17 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Single-sample landscape entropy reveals the imminent phase transition during disease progression”, Liu R, Chen P, Chen L., Bioinformatics. 2020 Mar 1;36(5):1522-1532.
Abstract

Motivation: The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt, that is, there is a tipping point during such a process at which the system state shifts from the normal state to a disease state. It is challenging to predict such disease state with the measured omics data, in particular when only a single sample is available.
Results: In this study, we developed a novel approach, i.e. single-sample landscape entropy (SLE) method, to identify the tipping point during disease progression with only one sample data. Specifically, by evaluating the disorder of a network projected from a single-sample data, SLE effectively characterizes the criticality of this single sample network in terms of network entropy, thereby capturing not only the signals of the impending transition but also its leading network, i.e. dynamic network biomarkers. Using this method, we can characterize sample-specific state during disease progression and thus achieve the disease prediction of each individual by only one sample. Our method was validated by successfully identifying the tipping points just before the serious disease symptoms from four real datasets of individuals or subjects, including influenza virus infection, lung cancer metastasis, prostate cancer and acute lung injury.

Marko Ćosić, Stewart’s Catastrophic Swing
2023-03-20 11:00 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract
The standard approach to problem-solving in physics consists of identifying state variables of the system, setting differential equations governing the state evolution, and solving the obtained. The behavior of the system for different values of parameters can be examined only as a fourth step. On the contrary, the modern approach to studying dynamical systems relies on Morphological/Topological analysis which alleviates the necessity for the explicit solution of differential equations.
The stability analysis of the parabolic swing will demonstrate the merit of such an approach. It will be shown how to construct a qualitatively correct model of system dynamics that is surprisingly quantitatively correct as well. The sudden (catastrophic) change in the swing’s stability, caused by a slight change in the critical value of system parameters, will be linked to the drastic topological change of the corresponding phase-space portraits.
It will be shown that for a system’s parameters close to critical ones, the system’s behavior is identical to a specific simple universal prototype given by catastrophe theory. A short survey of the simplest elementary catastrophes will be given that represents the basis for applying catastrophe theory in other fields of science.

Candan Celik, The effect of microRNA on protein variability and gene expression fidelity
2023-03-24 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “The effect of microRNA on protein variability and gene expression fidelity”, Hilfinger, Andreas, and Raymond Fan., Biophysical journal 122.3 (2023): 537a.
Abstract

Small regulatory RNA molecules such as microRNA modulate gene expression through inhibiting the translation of messenger RNA (mRNA). Such post-transcriptional regulation has been recently hypothesized to reduce the stochastic variability of gene expression around average levels. Here we quantify noise in stochastic gene expression models with and without such regulation. Our results suggest that silencing mRNA post-transcriptionally will always increase rather than decrease gene expression noise when the silencing of mRNA also increases its degradation as is expected for microRNA interactions with mRNA. In that regime we also find that silencing mRNA generally reduces the fidelity of signal transmission from deterministically varying upstream factors to protein levels. These findings suggest that microRNA binding to mRNA does not generically confer precision to protein expression

(Rescheduled: 3/22 -> 3/24) Stefan Bauer, Neural Causal Models for Experimental Design
2023-03-24 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Many questions in everyday life as well as in research are causal in nature: How would the climate change if we lower train prices or will my headache go away if I take an aspirin? Inherently, such questions need to specify the causal variables relevant to the question and their interactions. However, existing algorithms for learning causal graphs from data are often not scaling well both with the number of variables or the number of observations. This talk will provide a brief introduction to causal structure learning, recent efforts in using continuous optimization to learn causal graphs at scale and systematic approaches for causal experimental design at scale.

Sungwoong Cho, HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
2023-03-27 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Fast and accurate predictions for complex physical dynamics are a big challenge across various applications. Real-time prediction on resource-constrained hardware is even more crucial in the real-world problems. The deep operator network (DeepONet) has recently been proposed as a framework for learning nonlinear mappings between function spaces. However, the DeepONet requires many parameters and has a high computational cost when learning operators, particularly those with complex (discontinuous or non-smooth) target functions. In this study, we propose HyperDeepONet, which uses the expressive power of the hypernetwork to enable learning of a complex operator with smaller set of parameters. The DeepONet and its variant models can be thought of as a method of injecting the input function information into the target function. From this perspective, these models can be viewed as a special case of HyperDeepONet. We analyze the complexity of DeepONet and conclude that HyperDeepONet needs relatively lower complexity to obtain the desired accuracy for operator learning. HyperDeepONet was successfully applied to various operator learning problems using low computational resources compared to other benchmarks.

George Karniadakis, BINNS: Biophysics-Informed Neural Networks
2023-04-07 11:00 - 12:00 | 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
