Mammalian synthetic biology by controller design
2022-12-02 11:00 - 12:00 (Asia/Seoul) | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract: The ability to reliably engineer the mammalian cell will impact a variety of applications in a disruptive way, including cell fate control and reprogramming, targeted drug delivery, and regenerative medicine. However, our current ability to engineer mammalian genetic circuits that behave as predicted remains limited. These circuits depend on the intra and extra cellular environment in ways that are difficult to anticipate, and this fact often hampers genetic circuit performance. This lack of robustness to poorly known and often variable cellular environment is the subject of this talk. Specifically, I will describe control engineering approaches that make the performance of genetic devices robust to context. I will show a feedforward controller that makes gene expression robust to variability in cellular resources and, more generally, to changes in intra-cellular context linked to differences in cell type. I will then show a feedback controller that uses bacterial two component signaling systems to create a quasi-integral controller that makes the input/output response of a genetic device robust to a variety of perturbations that affect gene expression. These solutions support rational and modular design of sophisticated genetic circuits and can serve for engineering biological circuits that are more robust and predictable across changing contexts.

Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors
2022-12-02 15:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors”, Vipond, Oliver, et al, Proceedings of the National Academy of Sciences 118.41 (2021): e2102166118.
Abstract
Highly resolved spatial data of complex systems encode rich and nonlinear information. Quantification of heterogeneous and noisy data—often with outliers, artifacts, and mislabeled points—such as those from tissues, remains a challenge. The mathematical field that extracts information from the shape of data, topological data analysis (TDA), has expanded its capability for analyzing real-world datasets in recent years by extending theory, statistics, and computation. An extension to the standard theory to handle heterogeneous data is multiparameter persistent homology (MPH). Here we provide an application of MPH landscapes, a statistical tool with theoretical underpinnings. MPH landscapes, computed for (noisy) data from agent-basedMultiparameter persistent homology landscapes identify immune cell spatial patterns in tumors model simulations of immune cells infiltrating into a spheroid, are shown to surpass existing spatial statistics and one-parameter persistent homology. We then apply MPH landscapes to study immune cell location in digital histology images from head and neck cancer. We quantify intratumoral immune cells and find that infiltrating regulatory T cells have more prominent voids in their spatial patterns than macrophages. Finally, we consider how TDA can integrate and interrogate data of different types and scales, e.g., immune cell locations and regions with differing levels of oxygenation. This work highlights the power of MPH landscapes for quantifying, characterizing, and comparing features within the tumor microenvironment in synthetic and real datasets.

Taming Complexity in Data-Limited Nonlinear Nonequilibrium Settings
2022-12-09 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract:
Since before the time of Aristotle and the natural philosophers, reductionism has played a foundational role in western scientific thought. The premise of reductionism is that systems can be broken down into constituent pieces and studied independently, then reassembled to understand the behavior of the system as a whole. It embodies the classical linear perspective. This approach has been successful in developing basic physical laws and especially in engineering where linear analysis dominates and systems are purposefully designed that way. However, reductionism is not universally applicable for natural complex systems where behavior is driven, not by a few factors acting independently, but by complex interactions between many components acting together and changing in time.
Nonlinearity in living systems means that its parts are interdependent – variables do not act in a mutually independent manner; rather they interact, and as a consequence associations (correlations) between them will change as the overall system context (state) changes. This problem is highlighted when extrapolating the results of single-factor experiments to nature, and surely contributes to the frustrating disconnect between experimental findings and clinical outcomes in drug trials. Indeed, while everyone knows Berkeley’s 1710 dictum “correlation does not imply causation” few realize that for nonlinear systems the converse “causation does not imply correlation” is also true. This conundrum runs counter to deeply ingrained heuristic thinking that is at the basis of modern science. Biological systems (esp. ecosystems) are particularly perverse on this issue by exhibiting mirage correlations that can continually cause us to rethink relationships we thought we understood.
Here we examine a minimalist paradigm, empirical dynamics (EDM), for studying non-linear systems and a method (CCM) that can detect causality when there is no correlation among variables. It is a data-driven approach that uses time series to study a system holistically by reconstructing its attractor – a geometric object that embodies the rules of a full set of equations for the system. The ideas are intuitive and will be illustrated with examples from genetics, ecology and epidemiology.
A python version of EDM tools can be found at https://pepy.tech/project/pyEDM

Static and Dynamic Absolute Concentration Robustness
2022-12-13 16:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Absolute Concentration Robustness (ACR) was introduced by Shinar and Feinberg (Science 327:1389-1391, 2010) as robustness of equilibrium species concentration in a mass action dynamical system. Their aim was to devise a mathematical condition that will ensure robustness in the function of the biological system being modeled. The robustness of function rests on what we refer to as empirical robustness — the concentration of a species remains unvarying, when measured in the long run, across arbitrary initial conditions. Even simple examples show that the ACR notion introduced in Shinar and Feinberg (here referred to as static ACR) is neither necessary nor sufficient for empirical robustness. To make a stronger connection with empirical robustness, we define dynamic ACR, a property related to long-term, global dynamics, rather than only to equilibrium behavior. We discuss general dynamical systems with dynamic ACR properties as well as parametrized families of dynamical systems related to reaction networks. In particular, we find necessary and sufficient conditions for dynamic ACR in complex balanced reaction networks, a class of networks that is central to the theory of reaction networks.This is joint work with Badal Joshi (CSUSM)

Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators
2022-12-16 13:00 - 15:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators”, Karapetyan, Sargis, and Nicolas E. Buchler,Physical Review E 92.6 (2015): 062712.
Abstract

Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.

Mini-workshop on Recent Trends in Pure and Applied Mathematics
2022-12-19 09:40 - 17:20 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
IBS BIMAG will host a mini-workshop on Monday, 19 December 2022, that will feature some of the current trends in pure and applied mathematics. Several faculties from the Institute of Mathematics, University of the Philippines Diliman are invited to share their academic group’s ongoing research. Prof. Seunggyu Lee (Korea University), Dr. Dae Wook Kim (University of Michigan), and Dr. Soyoung Kim will give a talk on the said activity. In addition, some BIMAG members will also present their current research projects.

Abstracts download

Olive Cawiding, Optimal control of aging in complex networks
2022-12-23 15:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Optimal control of aging in complex networks”,
Sun, Eric D., Thomas CT Michaels, and L. Mahadevan, Proceedings of the National Academy of Sciences 117.34 (2020): 20404-20410.
Abstract

Many complex systems experience damage accumulation, which leads to aging, manifest as an increasing probability of system collapse with time. This naturally raises the question of how to maximize health and longevity in an aging system at minimal cost of maintenance and intervention. Here, we pose this question in the context of a simple interdependent network model of aging in complex systems and show that it exhibits cascading failures. We then use both optimal control theory and reinforcement learning alongside a combination of analysis and simulation to determine optimal maintenance protocols. These protocols may motivate the rational design of strategies for promoting longevity in aging complex systems with potential applications in therapeutic schedules and engineered system maintenance.

IBS BIMAG Winter Internship Workshop
2022-12-26 10:00 - 12:00 | IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
IBS BIMAG will host a kick-off workshop for the winter internships on Monday, 26 December 2022. The internship participants from Pusan National University and Postech will give 8 minutes presentations on their research topics.
Presentation List:

김미지 (Miji Kim) – A Comparison Study of Dropout to Prevent Overfitting Problem in CNN Image Data Classification
김지현 (Jihyeon Kim)- Study of Ensemble Kalman Filter
이시은 (Sieun Lee) – Early Detection using Epidemic Data
이유진 (Youjin Lee) – On Parameter Estimation Approaches for Biomathematical Models through Physics-Informed Neural Networks
장근수 (Geunsoo Jang) – Development of mathematical model for impact evaluation of Radioactive Water Discharge in Fukushima
김진영 (Jinyoung Kim) – Stochastic aggregation models in 2D and 3D spaces to describe Liquid-Liquid Phase Separation (LLPS)
김민준 (Minjoon Kim) – Stability of Chemical reaction networks

Ji Won Oh, From Grave to Cradle: Human Somatic Mosaicism and Unsolved Questions
2022-12-28 14:00 - 15:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
사람이 어떻게 만들어지고 각 기관이 어떻게 발달하는지에 대한 질문은 아주 오래전부터 있었습니다. 체외수정(IVF)의 고유의 장점으로 인해 과학자들이 수정란을 외부에서 관찰할 수 있게 되었습니다. 하지만, 1979년도에 제정된 14일 규정(the 14-day rule)으로 인해, 수정 후 최대 14일까지의 배아 만의 연구가 가능합니다. 따라서, 이 14일 규정은 발생 생물학자들이 사람 발생학 연구에 있어서 수정 후 2주 이상(신경계 발달, 기관 형성 등)에 나타나는 현상을 연구하고자 할 경우 다른 방향을 모색할 수밖에 없게 되었습니다. 본 연구는 이 지점에서부터 시작합니다. 연구진들은 세포 분열 때 우연히 발생하는 생리학적 체세포 변이(Post-zygotic Variants)를 추적하여 각 세포들의 운명을 재구성하였습니다. 특히 사망 후 기증된 시신에서 단일 세포를 배양하고, 최근 개발된 차세대 염기서열 분석 기술을 사용하여 인간 발생 연구의 후향적 혈통 추적(Retrospective Lineage Tracing)을 수행하는 과정을 발표하고자 합니다. 이번 발표를 통해서 이런 방법론이 어떻게 가능했는지에 대한 생물학적 및 과학적 배경과 인간 발생학의 미래에서 해결해야 할 과제와 가설을 강조할 예정입니다. 추가로, 이 과정에서 필요한 수학적인 해석이 필요한 질문들에 대해서도 논의할 예정입니다. 여러분들의 참신한 시각과 질문을 크게 환영합니다.

1) Park, S., Mali, N.M., Kim, R. et al. Clonal dynamics in early human embryogenesis inferred from somatic mutation. Nature 597, 393–397 (2021). https://doi.org/10.1038/s41586-021-03786-8
2) Kwon, S.G., Bae, G.H., Choi, J.H. et al. Asymmetric Contribution of Blastomere Lineages of First Division of the Zygote to Entire Human Body Using Post-Zygotic Variants. Tissue Eng Regen Med 19, 809–821 (2022). https://doi.org/10.1007/s13770-022-00443-7

Candan Celik, Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms
2022-12-30 15:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms”,Jia, Chen, and Youming Li, BioRxiv (2022).
Abstract

Classical gene expression models assume exponential switching time distributions between the active and inactive promoter states. However, recent experiments have shown that many genes in mammalian cells may produce non-exponential switching time distributions, implying the existence of multiple promoter states and molecular memory in the promoter switching dynamics. Here we analytically solve a gene expression model with random bursting and complex promoter switching, and derive the time-dependent distributions of the mRNA and protein copy numbers, generalizing the steady-state solution obtained in [SIAM J. Appl. Math. 72, 789-818 (2012)] and [SIAM J. Appl. Math. 79, 1007-1029 (2019)]. Using multiscale simplification techniques, we find that molecular memory has no influence on the time-dependent distribution when promoter switching is very fast or very slow, while it significantly affects the distribution when promoter switching is neither too fast nor too slow. By analyzing the dynamical phase diagram of the system, we also find that molecular memory in the inactive gene state weakens transient and stationary bimodality of the copy number distribution, while molecular memory in the active gene state enhances such bimodality.

Aurelio A. de los Reyes V, Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
2023-01-06 15:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems”, Linka, Kevin, et al., Computer Methods in Applied Mechanics and Engineering Volume 402, 1 December 2022, 115346
Abstract

Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics, robustly solve forward and inverse problems, and perform well for both interpolation and extrapolation, even for a small amount of noisy and incomplete data. At only minor additional cost, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and, more broadly, to a wide variety of nonlinear dynamical systems. Our source code and examples are available at https://github.com/LivingMatterLab/xPINNs.

Hyun Kim, Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics
2023-01-12 13:00 - 15:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics”
, Lin, Baihan., arXiv preprint arXiv:2204.14048 (2022).
Abstract

The absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate, differentiate, and compete, i.e. the cell ecology. With the recent advancement of the single-cell RNA-sequencing (RNA-seq), we can potentially describe such a link by constructing network graphs that characterize the similarity of the gene expression profiles of the cell-specific transcriptional programs, and analyzing these graphs systematically using the summary statistics informed by the algebraic topology. We propose the single-cell topological simplicial analysis (scTSA). Applying this approach to the single-cell gene expression profiles from local networks of cells in different developmental stages with different outcomes reveals a previously unseen topology of cellular ecology. These networks contain an abundance of cliques of single-cell profiles bound into cavities that guide the emergence of more complicated habitation forms. We visualize these ecological patterns with topological simplicial architectures of these networks, compared with the null models. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38,731 cells, 25 cell types and 12 time steps, our approach highlights the gastrulation as the most critical stage, consistent with consensus in developmental biology. As a nonlinear, model-independent, and unsupervised framework, our approach can also be applied to tracing multi-scale cell lineage, identifying critical stages, or creating pseudo-time series.

Jong-Eun Park, Single-cell analysis reveals recurring programs in cancer microenvironment
2023-01-19 10:00 - 11:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Complexity of the cellular organization of the tumor microenvironment as an ecosystem remains to be fully appreciated. Here, for a comprehensive investigation of tumor ecosystems across a wide variety of cancer types, we performed integrative transcriptome analyses of 4.4 million single cells from 978 tumor and 474 normal samples in combination with 9,510 TCGA and 1,339 checkpoint inhibitor-treated bulk tumors. Our analysis enabled us to define 28 different epithelial cell states, some of which had prognostic effects in cancers of relevant origin. Malignant fibroblast signatures defined according to the organ of origin demonstrated prognostic significance across diverse cancer types and revealed FN1, BGN, THBS2, and CTHRC1 as common cancer-associated fibroblast genes. Novel associations were revealed between the AKR1C1+ inflammatory fibroblast and myeloid-derived PRR-induced activation states and between the CXCL10+ fibroblast and squamous/LAMP3+ DC/SPP1+ macrophage states. We discovered tumor-specific rewiring of the tertiary lymphoid structure (TLS) network, involving previously unappreciated DC1, and pDC.. Along with other TLS component states, the tumor-associated germinal center B cell state identified from adjacent normal tissues was able to predict responses to checkpoint immunotherapy. Distinct groups of pan-cancer ecosystems were identified and characterized along the axis of immunotherapy responses. Our systematic, high-resolution dissection of tumor ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

Yun Min Song, A scalable approach for solving chemical master equations based on modularization and filtering
2023-01-20 11:00 - 13:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “A scalable approach for solving chemical master equations based on modularization and filtering
”, Fang, Zhou, Ankit Gupta, and Mustafa Khammash., bioRxiv (2022).
Abstract

Solving the chemical master equation (CME) that characterizes the probability evolution of stochastically reacting processes is greatly important for analyzing intracellular reaction systems. Conventional methods for solving CMEs include the simulation-based Monte-Carlo methods, the direct approach (e.g., the finite state projection), and so on; however, they usually do not scale very well with the system dimension either in terms of accuracy or efficiency. To mitigate this problem, we propose a new computational method based on modularization and filtering. Our method first divides the whole system into a leader system and several conditionally independent follower subsystems. Then, we solve the CME by applying the Monte Carlo method to the leader system and the direct approach to the filtered CMEs that characterize the conditional probabilities of the follower subsystems. The system decomposition involved in our method is optimized so that all the subproblems above are low dimensional, and, therefore, our approach scales more favorably with the system dimension. Finally, we demonstrate the efficiency and accuracy of our approach in high-dimensional estimation and inference problems using several biologically relevant examples.

Seokjoo Chae, Optimal information networks: Application for data-driven integrated health in populations
2023-01-27 11:00 - 13:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
We will discuss about “Optimal information networks: Application for data-driven integrated health in populations”, Servadio, Joseph L., and Matteo Convertino, Science Advances 4.2 (2018): e1701088.
Abstract

Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables, instead considering only individual correlations. In addition, a unified method for assessing integrated health statuses of populations is lacking, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator, representing integrated health in a city.

Hyukpyo Hong, Estimating and Assessing Differential Equation Models with Time-Course Data
2023-02-03 14:00 - 16:00 | 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.
