Jonathan Rubin, Qualitative inverse problems: mapping from limited data to properties of dynamics and parameter values for ODE models
2023-09-14 16:00 - 17:30 (Asia/Seoul) | KAIST E6-1 1501 Auditorium, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea, Republic of

Eui Min Jung, Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks
2023-09-15 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks
”,Briat, Corentin, Ankit Gupta, and Mustafa Khammash., Journal of The Royal Society Interface 15.143 (2018): 20180079
Abstract

The ability of a cell to regulate and adapt its internal state in response to unpredictable environmental changes is called homeostasis and this ability is crucial for the cell’s survival and proper functioning. Understanding how cells can achieve homeostasis, despite the intrinsic noise or randomness in their dynamics, is fundamentally important for both systems and synthetic biology. In this context, a significant development is the proposed antithetic integral feedback (AIF) motif, which is found in natural systems, and is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. From the standpoint of applications, one drawback of this motif is that it often leads to an increased cell-to-cell heterogeneity or variance when compared to a constitutive (i.e. open-loop) control strategy. Our goal in this paper is to show that this performance deterioration can be countered by combining the AIF motif and a negative feedback strategy. Using a tailored moment closure method, we derive approximate expressions for the stationary variance for the controlled network that demonstrate that increasing the strength of the negative feedback can indeed decrease the variance, sometimes even below its constitutive level. Numerical results verify the accuracy of these results and we illustrate them by considering three biomolecular networks with two types of negative feedback strategies. Our computational analysis indicates that there is a trade-off between the speed of the settling-time of the mean trajectories and the stationary variance of the controlled species; i.e. smaller variance is associated with larger settling-time.

Balazs Erdos, Quantifying the dynamics of postmeal metabolism: Inference from challenge test data
2023-09-18 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of

Sebastian Walcher, Reaction networks: Reduction of dimension and critical parameters
2023-09-20 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Typically, the mathematical description of reaction networks involves a system of parameter-dependent ordinary differential equations. Generally, one is interested in the qualitative and quantitative behavior of solutions in various parameter regions. In applications, identifying the reaction parameters is a fundamental task. Reduction of dimension is desirable from a practical perspective, and even necessary when different timescales are present. For biochemical reaction networks, a classical reduction technique assumes quasi-steady state (QSS) of certain species. From a general mathematical perspective, singular perturbation theory – involving a small parameter – is often invoked. The talk is mathematically oriented. The following points will be discussed: Singular perturbation reduction in general coordinates. (“How does one compute reductions?”) Critical parameters for singular perturbations. (“How does one find small parameters?”) Quasi-steady state and singular perturbations. (“What is applicable, what is correct?”)

Yun Min Song, A data-driven approach for timescale decomposition of biochemical reaction networks
2023-09-22 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “A data-driven approach for timescale decomposition of biochemical reaction networks”, Amir Akbari, Zachary B. Haiman, Bernhard O. Palsson, bioRxiv (2023)
Abstract

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications.

Jonathan Rubin, Qualitative inverse problems: mapping from limited data to properties of dynamics and parameter values for ODE models
2023-09-26 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of

Olive Cawiding, Power spectral estimate for discrete data
2023-10-06 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Power spectral estimate for discrete data”, Nobert Marwan and Tobias Braun, Chaos (2023).

Abstract

The identification of cycles in periodic signals is a ubiquitous problem in time series analysis. Many real-world datasets only record a signal as a series of discrete events or symbols. In some cases, only a sequence of (non-equidistant) times can be assessed. Many of these signals are furthermore corrupted by noise and offer a limited number of samples, e.g., cardiac signals, astronomical light curves, stock market data, or extreme weather events. We propose a novel method that provides a power spectral estimate for discrete data. The edit distance is a distance measure that allows us to quantify similarities between non-equidistant event sequences of unequal lengths. However, its potential to quantify the frequency content of discrete signals has so far remained unexplored. We define a measure of serial dependence based on the edit distance, which can be transformed into a power spectral estimate (EDSPEC), analogous to the Wiener–Khinchin theorem for continuous signals. The proposed method is applied to a variety of discrete paradigmatic signals representing random, correlated, chaotic, and periodic occurrences of events. It is effective at detecting periodic cycles even in the presence of noise and for short event series. Finally, we apply the EDSPEC method to a novel catalog of European atmospheric rivers (ARs). ARs are narrow filaments of extensive water vapor transport in the lower troposphere and can cause hazardous extreme precipitation events. Using the EDSPEC method, we conduct the first spectral analysis of European ARs, uncovering seasonal and multi-annual cycles along different spatial domains. The proposed method opens new research avenues in studying of periodic discrete signals in complex real-world systems.

Human Frontier Science Program Awardees' Symposium
2023-10-17 - 2023-10-18 | IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of

Tetsuya J. Kobayashi, Optimality of Biological Information Processing
2023-10-20 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract:
Almost all biological systems possess the ability to gather environmental information and modulate their behaviors to adaptively respond to changing environments. While animals excel at sensing odors, even simple bacteria can detect faint chemicals using stochastic receptors. They then navigate towards or away from the chemical source by processing this sensed information through intracellular reaction systems.
In the first half of our talk, we demonstrate that the E. coli chemotactic system is optimally structured for sensing noisy signals and controlling taxis. We utilize filtering theory and optimal control theory to theoretically derive this optimal structure and compare it to the quantitatively verified biochemical model of chemotaxis.
In the latter half, we discuss the limitations of traditional information theory, filtering theory, and optimal control theory in analyzing biological systems. Notably, all biological systems, especially simpler ones, have constrained computational resources like memory size and energy, which influence optimal behaviors. Conventional theories don’t directly address these resource constraints, likely because they emerged during a period when computational resources were continually expanding. To address this gap, we introduce the “memory-limited partially observable optimal control,” a new theoretical framework developed by our group, and explore its relevance to biological problems.

Hyeontae Jo, AutoScore:A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records
2023-10-20 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “AutoScore:A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records”, Xie, Feng, et al., JMIR medical informatics 8.10 (2020): e21798.
Abstract
Background: Risk scores can be useful in clinical risk stratification and accurate allocations of medical resources, helping health providers improve patient care. Point-based scores are more understandable and explainable than other complex models and are now widely used in clinical decision making. However, the development of the risk scoring model is nontrivial and has not yet been systematically presented, with few studies investigating methods of clinical score generation using electronic health records.
Objective: This study aims to propose AutoScore, a machine learning-based automatic clinical score generator consisting of 6 modules for developing interpretable point-based scores. Future users can employ the AutoScore framework to create clinical scores effortlessly in various clinical applications.
Methods: We proposed the AutoScore framework comprising 6 modules that included variable ranking, variable transformation, score derivation, model selection, score fine-tuning, and model evaluation. To demonstrate the performance of AutoScore, we used data from the Beth Israel Deaconess Medical Center to build a scoring model for mortality prediction and then compared the data with other baseline models using the receiver operating characteristic analysis. A software package in R 3.5.3 (R Foundation) was also developed to demonstrate the implementation of AutoScore.
Results: Implemented on the data set with 44,918 individual admission episodes of intensive care, the AutoScore-created scoring models performed comparably well as other standard methods (ie, logistic regression, stepwise regression, least absolute shrinkage and selection operator, and random forest) in terms of predictive accuracy and model calibration but required fewer predictors and presented high interpretability and accessibility. The nine-variable, AutoScore-created, point-based scoring model achieved an area under the curve (AUC) of 0.780 (95% CI 0.764-0.798), whereas the model of logistic regression with 24 variables had an AUC of 0.778 (95% CI 0.760-0.795). Moreover, the AutoScore framework also drives the clinical research continuum and automation with its integration of all necessary modules.
Conclusions: We developed an easy-to-use, machine learning-based automatic clinical score generator, AutoScore; systematically presented its structure; and demonstrated its superiority (predictive performance and interpretability) over other conventional methods using a benchmark database. AutoScore will emerge as a potential scoring tool in various medical applications.

Hyun Kim, Significance analysis for clustering with single-cell RNA-sequencing data
2023-10-27 14:00 - 16:00 | Daejeon
We will discuss about “Significance analysis for clustering with single-cell RNA-sequencing data”, Grabski, Isabella N., Kelly Street, and Rafael A. Irizarry., Nature Methods (2023): 1-7.
Abstract

Unsupervised clustering of single-cell RNA-sequencing data enables the identification of distinct cell populations. However, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty. We find that not addressing known sources of variability in a statistically rigorous manner can lead to overconfidence in the discovery of novel cell types. Here we extend a previous method, significance of hierarchical clustering, to propose a model-based hypothesis testing approach that incorporates significance analysis into the clustering algorithm and permits statistical evaluation of clusters as distinct cell populations. We also adapt this approach to permit statistical assessment on the clusters reported by any algorithm. Finally, we extend these approaches to account for batch structure. We benchmarked our approach against popular clustering workflows, demonstrating improved performance. To show practical utility, we applied our approach to the Human Lung Cell Atlas and an atlas of the mouse cerebellar cortex, identifying several cases of over-clustering and recapitulating experimentally validated cell type definitions.

Eder Zavala, Quantitative analysis of high-resolution daily profiles of HPA axis hormones
2023-11-01 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: The Hypothalamic-Pituitary-Adrenal (HPA) axis is the key regulatory pathway responsible for maintaining homeostasis under conditions of real or perceived stress. Endocrine responses to stressors are mediated by adrenocorticotrophic hormone (ACTH) and corticosteroid (CORT) hormones. In healthy, non-stressed conditions, ACTH and CORT exhibit highly correlated ultradian pulsatility with an amplitude modulated by circadian processes. Disruption of these hormonal rhythms can occur as a result of stressors or in the very early stages of disease. Despite the fact that misaligned endocrine rhythms are associated with increased morbidity, a quantitative understanding of their mechanistic origin and pathogenicity is missing. Mathematically, the HPA axis can be understood as a dynamical system that is optimised to respond and adapt to perturbations. Normally, the body copes well with minor disruptions, but finds it difficult to withstand severe, repeated or long-lasting perturbations. Whilst a healthy HPA axis maintains a certain degree of robustness to stressors, its fragility in diseased states is largely unknown, and this understanding constitutes a critical step toward the development of digital tools to support clinical decision-making. This talk will explore how these challenges are being addressed by combining high-resolution biosampling techniques with mathematical and computational analysis methods. This interdisciplinary approach is helping us quantify the inter-individual variability of daily hormone profiles and develop novel “dynamic biomarkers” that serve as a normative reference and to signal endocrine dysfunction. By shifting from a qualitative to a quantitative description of the HPA axis, these insights bring us a step closer to personalised clinical interventions for which timing is key.

Matthew Simpson, Efficient prediction, estimation and identifiability analysis with mechanistic mathematical models
2023-11-10 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic, computationally efficient likelihood-based workflow that addresses all three steps in a unified way. Recently developed methods for constructing profile-wise prediction intervals enable this workflow and provide the central linkage between different workflow components. These methods propagate profile-likelihood-based confidence sets for model parameters to predictions in a way that isolates how different parameter combinations affect model predictions. We show how to extend these profile-wise prediction intervals to two-dimensional interest parameters, and then combine profile-wise prediction confidence sets to give an overall prediction confidence set that approximates the full likelihood-based prediction confidence set well. We apply our methods to a range of synthetic data and real-world ecological data describing re-growth of coral reefs on the Great Barrier Reef after some external disturbance, such as a tropical cyclone or coral bleaching event.

Seokjoo Chae, Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning
2023-11-10 14:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning.” bioRxiv (2023): 2023-09.

Abstract
The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems.

Samuel Isaacson, Spatial Particle Modeling of Immune Processes
2023-11-17 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract:
Surface Plasmon Resonance (SPR) assays are a standard approach for quantifying kinetic parameters in antibody-antigen binding reactions. Classical SPR approaches ignore the bivalent structure of antibodies, and use simplified ODE models to estimate effective reaction rates for such interactions. In this work we develop a new SPR protocol, coupling a model that explicitly accounts for the bivalent nature of such interactions and the limited spatial distance over which such interactions can occur, to a SPR assay that provides more features in the generated data. Our approach allows the estimation of bivalent binding kinetics and the spatial extent over which antibodies and antigens can interact, while also providing substantially more robust fits to experimental data compared to classical ODE models. I will present our new modeling and parameter estimation approach, and demonstrate how it is being used to study interactions between antibodies and spike protein. I will also explain how we make the overall parameter estimation problem computationally feasible via the construction of a surrogate approximation to the (computationally-expensive) particle model. The latter enables fitting of model parameters via standard optimization approaches.
Time-permitting, I will also give an introduction to our Catalyst.jl symbolic chemical reaction modeling library, which we have recently demonstrated outperforms a number of popular systems biology simulation packages in solving ODE and stochastic reaction models. A distinguishing feature of Catalyst is the ease with which it integrates with other Julia libraries to enable sensitivity analysis, parameter estimation studies, structural identifiability analysis, bifurcation analysis, solution of the chemical master equation, and a variety of higher-level functionality.

Alfio Quarteroni, Physics-based and data-driven numerical models for computational medicine
2023-11-22 16:00 - 17:00 | 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.
