Biomedical Mathematics Group

[CANCELED] Approaches to understanding tumour-immune interactions
2021-09-08 17:00 - 18:00 (Asia/Seoul) | ZOOM ID: 709 120 4849 (ibsbimag), (pw: [REDACTED]
CANCELED due to unexpected circumstances
This talk will be presented online. Zoom link: 709 120 4849 (pw: [REDACTED]
Abstract: While the presence of immune cells within solid tumours was initially viewed positively, as the host fighting to rid itself of a foreign body, we now know that the tumour can manipulate immune cells so that they promote, rather than inhibit, tumour growth. Immunotherapy aims to correct for this by boosting and/or restoring the normal function of the immune system. Immunotherapy has delivered some extremely promising results. However, the complexity of the tumour-immune interactions means that it can be difficult to understand why one patient responds well to immunotherapy while another does not. In this talk, we will show how mathematical, statistical and topological methods can contribute to resolving this issue and present recent results which illustrate the complementary insight that different approaches can deliver.

Nonlinear delay differential equations and their application to modeling biological network motifs
2021-09-09 09:00 - 10:00 | B305 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Nonlinear delay differential equations and their application to modeling biological network motifs”, Glass et al., Nature Communications, 2021
Abstract:
Biological regulatory systems, such as cell signaling networks, nervous systems and ecological webs, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight into overall behavior. However, such models often overlook time delays either inherent to biological processes or associated with multi-step interactions. Here we systematically examine explicit-delay versions of the most common network motifs via delay differential equation (DDE) models, both analytically and numerically. We find many broadly applicable results, including parameter reduction versus canonical ordinary differential equation (ODE) models, analytical relations for converting between ODE and DDE models, criteria for when delays may be ignored, a complete phase space for autoregulation, universal behaviors of feedforward loops, a unified Hill-function logic framework, and conditions for oscillations and chaos. We conclude that explicit-delay modeling simplifies the phenomenology of many biological networks and may aid in discovering new functional motifs.

COVID19 – Mathematical Modeling and Machine Learning
2021-09-09 11:00 - 12:00 | B305 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract
This presentation include the following two topics. First of all, we consider a spread model of COVID-19 with time-dependent parameters via deep learning. We developed a SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional model to confirm its convergent nature. Next, We also developed a machine learning model that predicts the mortality of infected patients by using basic patients information such as age, residence, comorbidity, and past medical history. Furthermore, we aim to establish a medical system that allows patients to check their own severity, and informs them to visit the appropriate clinic center by referring to the past treatment details of other patients with similar severity.

Stochastic processes as scientific instruments: efficient inference based on stochastic dynamical systems
2021-09-16 11:00 - 12:00 | ZOOM ID: 709 120 4849 (ibsbimag), (pw: [REDACTED]
This talk will be presented online. Zoom link: 709 120 4849 (pw: [REDACTED]

Abstract: Questions about the mechanistic operation of biological systems are naturally formulated as stochastic processes, but confronting such models with data can be challenging. In this talk, I describe the essence of the difficulty, highlighting both the technical issues and the importance of the “plug-and-play property”. I then illustrate some effective approaches to efficient inference based on such models. I conclude by sketching promising new developments and describing some open problems.

The Oscillation Amplitude, Not the Frequency of Cytosolic Calcium, Regulates Apoptosis Induction
2021-09-17 13:00 - 14:00 | B305 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “The Oscillation Amplitude, Not the Frequency of Cytosolic Calcium, Regulates Apoptosis Induction ”, Qi et al., iScience, 2020
Abstract:
Although a rising concentration of cytosolic Ca2+ has long been recognized as an essential signal for apoptosis, the dynamical mechanisms by which Ca2+ regulates apoptosis are not clear yet. To address this, we constructed a computational model that integrates known biochemical reactions and can reproduce the dynamical behaviors of Ca2+-induced apoptosis as observed in experiments. Model analysis shows that oscillating Ca2+ signals first convert into gradual signals and eventually transform into a switch-like apoptotic response. Via the two processes, the apoptotic signaling pathway filters the frequency of Ca2+ oscillations effectively but instead responds acutely to their amplitude. Collectively, our results suggest that Ca2+ regulates apoptosis mainly via oscillation amplitude, rather than frequency, modulation. This study not only provides a comprehensive understanding of how oscillatory Ca2+ dynamically regulates the complex apoptotic signaling network but also presents a typical example of how Ca2+ controls cellular responses through amplitude modulation.

A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells
2021-09-24 13:00 - 14:00 | B305 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells”, Unosson et al., bioRxiv, 2021
We propose a stochastic distributed delay model together with a Markov random field prior and a measurement model for bioluminescence-reporting to analyse spatiotemporal gene expression in intact networks of cells. The model describes the oscillating time evolution of molecular mRNA counts through a negative transcriptional-translational feedback loop encoded in a chemical Langevin equation with a probabilistic delay distribution. The model is extended spatially by means of a multiplicative random effects model with a first order Markov random field prior distribution. Our methodology effectively separates intrinsic molecular noise, measurement noise, and extrinsic noise and phenotypic variation driving cell heterogeneity, while being amenable to parameter identification and inference. Based on the single-cell model we propose a novel computational stability analysis that allows us to infer two key characteristics, namely the robustness of the oscillations, i.e. whether the reaction network exhibits sustained or damped oscillations, and the profile of the regulation, i.e. whether the inhibition occurs over time in a more distributed versus a more direct manner, which affects the cells’ ability to phase-shift to new schedules. We show how insight into the spatio-temporal characteristics of the circadian feedback loop in the suprachiasmatic nucleus (SCN) can be gained by applying the methodology to bioluminescence-reported expression of the circadian core clock gene Cry1 across mouse SCN tissue. We find that while (almost) all SCN neurons exhibit robust cell-autonomous oscillations, the parameters that are associated with the regulatory transcription profile give rise to a spatial division of the tissue between the central region whose oscillations are resilient to perturbation in the sense that they maintain a high degree of synchronicity, and the dorsal region which appears to phase shift in a more diversified way as a response to large perturbations and thus could be more amenable to entrainment.

A temporal signaling code to specify immune responses
2021-10-07 11:00 - 12:00 | ZOOM ID: 709 120 4849 (ibsbimag), (pw: [REDACTED]
This talk will be presented online. Zoom link: 709 120 4849 (pw: [REDACTED]
Abstract:
Immune sentinel cells must initiate the appropriate immune response upon sensing the presence of diverse pathogens or immune stimuli. To generate stimulus-specific gene expression responses, immune sentinel cells have evolved a temporal code in the dynamics of stimulus responsive transcription factors. I will present recent works 1) using an information theoretic approach to identify the codewords, termed “signaling codons”, 2) using a machine learning approach to characterize their reliability and points of confusion, and 3) dynamical systems modeling to characterize the molecular circuits that allow for their encoding. I will present progress on how the temporal code may be decoded to specify immune responses. Further, I will discuss to what extent such a code may be harnessed to achieve greater pharmacological specificity when therapeutically targeting pleiotropic signaling hubs.
NFκB Signaling: information theory, signaling codons
Adelaja, A., Taylor, B., Sheu, K.M., Liu, Y., Luecke, S., Hoffmann, A. 2021 Six distinct NFκB signaling codons convey discrete information to distinguish stimuli and enable appropriate macrophage responses. Immunity, 54, pp.916-930. e7. PMID: 33979588
Tang, Y., Adelaja, A., Ye, X, Deeds, E., Wollman, R., Hoffmann, A. 2021. Quantifying information accumulation encoded in the dynamics of biochemical signaling. Nature Communications 12, pp.1-10
Decoding signaling codons to specify immune responses
Sen S., Cheng, Z., Sheu, K., Chen, E.Y.H., Hoffmann, A. 2020 Gene Regulatory Strategies that Decode the Duration of NFkB Dynamics Contribute to LPS- versus TNF-Specific Gene Expression. Cell Systems, 10, pp.1-14. PMID:31972132, PMC7047529
Cheng, Q.J., Ohta, S., Sheu, K.M., Spreafico, R., Adelaja, A., Taylor, B., Hoffmann, A. 2021 NFκB dynamics determine the stimulus-specificity of epigenomic reprogramming in macrophages. Science, 372, pp.1349-1353; PMID: 34140389.
Pharmacologic manipulation of the code
Behar, M., Barken, D., Werner, S.L., Hoffmann, A. 2013 The Dynamics of Signaling as a Pharmacological Target. Cell, 155, pp.448-461. PMID: 24120141, PMC3856316

Balanced truncation for model reduction of biological oscillators
2021-10-08 14:00 - 15:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Balanced truncation for model reduction of biological oscillators”, Padoan et al., Biological Cybernetics, 2021
Model reduction is a central problem in mathematical biology. Reduced order models enable modeling of a biological system at different levels of complexity and the quantitative analysis of its properties, like sensitivity to parameter variations and resilience to exogenous perturbations. However, available model reduction methods often fail to capture a diverse range of nonlinear behaviors observed in biology, such as multistability and limit cycle oscillations. The paper addresses this need using differential analysis. This approach leads to a nonlinear enhancement of classical balanced truncation for biological systems whose behavior is not restricted to the stability of a single equilibrium. Numerical results suggest that the proposed framework may be relevant to the approximation of classical models of biological systems.

Sleep Tree: A workshop on an interdisciplinary approach to sleep disorders
2021-10-14 14:00 - 2021-10-16 11:00 | Yeosu, Yeosu, Jeollanam-do, Korea, Republic of
Sleep Tree: A workshop on an interdisciplinary approach to sleep disorders.
This workshop is an interdisciplinary workshop across Mathematics, Medicine, Psychology, and Psychiatry to investigate sleep disorders.
Researchers from groups led by Prof. Jae Kyoung Kim (KAIST/IBS), Sooyeon Suh (Sungshin Women’s Univ.), Eun Yeon Joo (Sungkyunkwan Univ./Samsung Medical Center), and Su Jung Choi (Sungkyunkwan Univ./ Samsung Medical Center) present their works.
Date
Oct 14 (Thu), 2021 – Oct 16 (Sat), 2021
Venue
LaTerrace resort, Yeosu, Jeollanam-do, Korea
여수 라테라스 리조트
http://laterrace-resort.com/

Scaling in development
2021-10-21 11:00 - 12:00 | ZOOM ID: 709 120 4849 (ibsbimag), (pw: [REDACTED]
This talk will be presented online. Zoom link: 709 120 4849 (pw: [REDACTED]

Abstract:
Within a given species, fluctuations in egg or embryo size is unavoidable. Despite this, the gene expression pattern and hence the embryonic structure often scale in proportion with the body length. This scaling phenomenon is very common in development and regeneration and has long fascinated scientists. I will first discuss a generic theoretical framework to show how scaling gene expression pattern can emerge from non-scaling morphogen gradients. I will then demonstrate that the Drosophila gap gene system achieves scaling in a way that is entirely consistent with our theory. Remarkably, a parameter-free model based on the theory quantitatively accounts for the gap gene expression pattern in nearly all morphogen mutants. Furthermore, the regulation logic and the coding/decoding strategy of the gap gene system can be revealed. Our work provides a general theoretical framework on a large class of problems where scaling output is induced by non-scaling input, as well as a unified understanding of scaling, mutants’ behavior and regulation in the Drosophila gap gene and related systems.

Filtering and inference for stochastic oscillators with distributed delays
2021-10-22 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Filtering and inference for stochastic oscillators with distributed delays”, Calderazzo et al., Bioinformatics, 2018 at the Journal Club

Motivation
The time evolution of molecular species involved in biochemical reaction networks often arises from complex stochastic processes involving many species and reaction events. Inference for such systems is profoundly challenged by the relative sparseness of experimental data, as measurements are often limited to a small subset of the participating species measured at discrete time points. The need for model reduction can be realistically achieved for oscillatory dynamics resulting from negative translational and transcriptional feedback loops by the introduction of probabilistic time-delays. Although this approach yields a simplified model, inference is challenging and subject to ongoing research. The linear noise approximation (LNA) has recently been proposed to address such systems in stochastic form and will be exploited here.

Results
We develop a novel filtering approach for the LNA in stochastic systems with distributed delays, which allows the parameter values and unobserved states of a stochastic negative feedback model to be inferred from univariate time-series data. The performance of the methods is tested for simulated data. Results are obtained for real data when the model is fitted to imaging data on Cry1, a key gene involved in the mammalian central circadian clock, observed via a luciferase reporter construct in a mouse suprachiasmatic nucleus.

Availability and implementation
Programmes are written in MATLAB and Statistics Toolbox Release 2016 b, The MathWorks, Inc., Natick, Massachusetts, USA. Sample code and Cry1 data are available on GitHub https://github.com/scalderazzo/FLNADD.

Inferring causality in biological oscillators
2021-10-27 11:00 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract
TBA

Systems pharmacology towards personalized chronotherapy
2021-10-27 17:00 - 18:00 | ZOOM ID: 709 120 4849 (ibsbimag), (pw: [REDACTED]
This talk will be presented online. Zoom link: 709 120 4849 (pw: [REDACTED]
Abstract:
Chronotherapeutics- that is administering drugs following the patient’s biological rhythms over the 24 h span- may largely impact on both drug toxicities and efficacy in various pathologies including cancer [1]. However, recent findings highlight the critical need of personalizing circadian delivery according to the patient sex, genetic background or chronotype. Chronotherapy personalization requires to reliably account for the temporal dynamics of molecular pathways of patient’s response to drug administration [2]. In a context where clinical molecular data is usually minimal in individual patients, multi-scale- from preclinical to clinical- systems pharmacology stands as an adapted solution to describe gene and protein networks driving circadian rhythms of treatment efficacy and side effects and allow for the design of personalized chronotherapies.
Such a multiscale approach is being undertaken for personalizing the circadian administration of irinotecan, one of the cornerstones of chemotherapies against digestive cancers. Irinotecan molecular chronopharmacology was studied at the cellular level in an in vitro/in silico investigation. Large transcription rhythms of period T= 28 h 06 min (SD 1 h 41 min) moderated drug bioactivation, detoxification, transport, and target in synchronized Caco-2 colorectal cancer cell cultures. These molecular rhythms translated into statistically significant changes according to drug timing in irinotecan pharmacokinetics, pharmacodynamics, and drug-induced apoptosis. Clock silencing through siBMAL1 exposure ablated all the chronopharmacology mechanisms. Mathematical modeling highlighted circadian bioactivation and detoxification as the most critical determinants of irinotecan chronopharmacology [3]. The cellular model of irinotecan chronoPK-PD was further tested on SW480 and SW620 cell lines, and connected to a new clock model to investigate the feasibility of irinotecan timing personalization solely based on clock gene expression monitoring (Hesse, Martinelli et al., under review).
To step towards the clinics, on one side, mathematical models of irinotecan, oxaliplatin and 5-fluorouracil pharmacokinetics were designed to precisely compute the exposure concentration of tissue over time after complex chronomodulated drug administration through programmable pumps [4]. On the other side, we aimed to design a model learning methodology predicting from non-invasively measured circadian biomarkers (e.g. rest-activity, body temperature, cortisol, food intake, melatonin), the patient peripheral circadian clocks and associated optimal drug timing [5]. We investigated at the molecular scale the influence of systemic regulators on peripheral clocks in four classes of mice (2 strains, 2 sexes). Best models involved a modulation of either Bmal1 or Per2 transcription most likely by temperature or nutrient exposure cycles. The strengths of systemic regulations were found to be significantly different according to mouse sex and genetic background.
References
1. Ballesta, A., et al., Systems Chronotherapeutics. Pharmacol Rev, 2017. 69(2): p. 161-199.
2. Sancar, A. and R.N. Van Gelder, Clocks, cancer, and chronochemotherapy. Science, 2021. 371(6524).
3. Dulong, S., et al., Identification of Circadian Determinants of Cancer Chronotherapy through In Vitro Chronopharmacology and Mathematical Modeling. Mol Cancer Ther, 2015.
4. Hill, R.J.W., et al., Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy. PLoS Comput Biol, 2020. 16(1): p. e1007218.
5. Martinelli, J., et al., Model learning to identify systemic regulators of the peripheral circadian clock. 2021.

The Graph convolutional Networks (GCN) with Persistent Homology and its application 1/4
2021-10-29 15:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
(1) GCN and its Application.
We introduce the GCN by reviewing the monumental paper ” Semi-Supervised Classification with the Graph Convolutional Networks”, ICLR 2018 by Kipf and Welling. We are going to much detail the algorithm of message aggregation and passings and learning processes.
Code ; https://github.com/tkipf/gcn
(2) Graph Attention networks(GAT) and its Applications. Bengio et al improved greatly the capability of GCN by employing the Attention mechanism to GCN on the paper,” Graph Attention Networks, ICLR,2018. We review closely the derivation of algirithms, learning processes and discuss its super performance.
Code; https://github.com/PetarV-/GAT

Biofluiddynamics of reproduction
2021-11-11 11:00 - 12:00 | ZOOM ID: 709 120 4849 (ibsbimag), (pw: [REDACTED]
This talk will be presented online. Zoom link: 709 120 4849 (pw: [REDACTED]
Abstract: From fertilization to birth, successful mammalian reproduction relies on interactions of elastic structures with a fluid environment. Sperm flagella must move through cervical mucus to the uterus and into the oviduct, where fertilization occurs. In fact, some sperm may adhere to oviductal epithelia, and must change their pattern of oscillation to escape. In addition, coordinated beating of oviductal cilia also drive the flow. Sperm-egg penetration, transport of the fertilized ovum from the oviduct to its implantation in the uterus and, indeed, birth itself are rich examples of elasto-hydrodynamic coupling. We will discuss successes and challenges in the mathematical and computational modeling of the biofluids of reproduction.

Detecting and quantifying causal associations in large nonlinear time series datasets
2021-11-12 11:00 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Detecting and quantifying causal associations in large nonlinear time series datasets”, Runge et al., Science Advances, 2019
Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.

The Graph convolutional Networks (GCN) with Persistent Homology and its application 2/4
2021-11-12 13:30 - 15:30 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Simplicial Complexes, Persistent Homology and Persistent Diagrams. After a brief review on the persistent homology( Cohen-Steiner, Edelsbrunner, Harer,2010), we discuss constructive procedures persistent diagrams from the persistent homology. Code; 9 software packages generating persistent homology are introduced at ” A roadmap for the computation of persistent homology”, EPJ Data Science, a Springer Open Journal.

Following the energy in cellular information processing
2021-11-18 11:00 - 12:00 | ZOOM ID: 709 120 4849 (ibsbimag), (pw: [REDACTED]
This talk will be presented online. Zoom link: 709 120 4849 (pw: [REDACTED]
Abstract: John Hopfield first pointed out that there are barriers – we call them Hopfield barriers – to biological information-processing at thermodynamic equilibrium. I will explain how the widely-used Hill function with coefficient n is the universal Hopfield barrier to the sharpness of binding to n sites. Away from thermodynamic equilibrium, I will describe the challenge of path dependent complexity and introduce the entropy-production index as a measure of non-equilibrium complexity.

Solving Singular Control Problems in Mathematical Biology, Using PASA
2021-11-18 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Solving Singular Control Problems in Mathematical Biology, Using PASA”, Atkins et al., arXiv, 2020
In this paper, we will demonstrate how to use a nonlinear polyhedral constrained optimization solver called the Polyhedral Active Set Algorithm (PASA) for solving a general singular control problem. We present methods of discretizing a general optimal control problem that involves the use of the gradient of the Lagrangian for computing the gradient of the cost functional so that PASA can be applied. When a numerical solution contains artifacts that resemble “chattering”, a phenomenon where the control oscillates wildly along the singular region, we recommend a method of regularizing the singular control problem by adding a term to the cost functional that measures a scalar multiple of the total variation of the control, where the scalar is viewed as a tuning parameter. We then demonstrate PASA’s performance on three singular control problems that give rise to different applications of mathematical biology. We also provide some exposition on the heuristics that we use in determining an appropriate size for the tuning parameter.

Quantitative comparisons between models and data to provide new insights in cell and developmental biology
2021-11-25 18:00 - 19:00 | ZOOM ID: 709 120 4849 (ibsbimag), (pw: [REDACTED]
This talk will be presented online. Zoom link: 709 120 4849 (pw: [REDACTED]
Abstract:
Simple mathematical models have had remarkable successes in biology, framing how we understand a host of mechanisms and processes. However, with the advent of a host of new experimental technologies, the last ten years has seen an explosion in the amount and types of quantitative data now being generated. This sets a new challenge for the field – to develop, calibrate and analyse new models to interpret these data. In this talk I will use examples relating to intracellular transport and cell motility to showcase how quantitative comparisons between models and data can help tease apart subtle details of biological mechanisms.
References:
• T. P. Prescott, K. Zhu, M. Zhao and R. E. Baker (2021). Quantifying the impact of electric fields on single-cell motility. Biophys. J. In press.
• J. U. Harrison, R. M. Parton, I. Davis and R. E. Baker (2019). Testing models of mRNA localization reveals robustness regulated by reducing transport between cells. Biophys. J. 117(11):2154-2165.

A Random Matrix Theory Approach to Denoise Single-Cell Data
2021-11-26 10:00 - 11:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “A Random Matrix Theory Approach to Denoise Single-Cell Data”, Aparicio et al., Patterns, 2020
Single-cell technologies provide the opportunity to identify new cellular states. However, a major obstacle to the identification of biological signals is noise in single-cell data. In addition, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and denoise single-cell sequencing data. The method uses the universal distributions predicted by random matrix theory for the eigenvalues and eigenvectors of random covariance/Wishart matrices to distinguish noise from signal. In addition, we explain how sparsity can cause spurious eigenvector localization, falsely identifying meaningful directions in the data. We show that roughly 95% of the information in single-cell data is compatible with the predictions of random matrix theory, about 3% is spurious signal induced by sparsity, and only the last 2% reflects true biological signal. We demonstrate the effectiveness of our approach by comparing with alternative techniques in a variety of examples with marked cell populations.

IBS BIMAG Vision Workshop
2021-11-27 08:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
IBS BIMAG Vision Workshop
This is a workshop for sharing the direction of the IBS BIMAG with leading mathematical biology researchers and receiving feedback.
Date
Nov. 27, 2021 (Saturday)
Venue
B378 Seminar room, IBS

The Graph convolutional Networks (GCN) with Persistent Homology and its applications 3/4
2021-12-10 15:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Neural Networks with the Persistent Diagrams and Graph Classification. We introduce the first paper connecting persistent diagrams to the Neural Networks by Carrier et al,” A neural Network Layer for Persistent Diagrams and New Graph Topological Signatures, 2019, arXiv. We are going to analyse the End-to-End algorithm and learning processes and applications.
Code; tensorflow at https:// github.com/MathieuCarriere/perslay

Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
2021-12-15 14:30 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics”, Ji et al., The Journal of Physical Chemistry A, 2020
The recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network such that the network not only conforms to the measurements and initial and boundary conditions but also satisfies the governing equations. This work first investigates the performance of the PINN in solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). The results elucidate the challenges of utilizing the PINN in stiff ODE systems. Consequently, we employ quasi-steady-state assumption (QSSA) to reduce the stiffness of the ODE systems, and the PINN then can be successfully applied to the converted non-/mild-stiff systems. Therefore, the results suggest that stiffness could be the major reason for the failure of the regular PINN in the studied stiff chemical kinetic systems. The developed stiff-PINN approach that utilizes QSSA to enable the PINN to solve stiff chemical kinetics shall open the possibility of applying the PINN to various reaction-diffusion systems involving stiff dynamics.

Methods for characterizing circadian physiology from wearables
2021-12-23 16:30 - 17:30 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract
Non-invasive data collection in real-world settings with wearables provides a new opportunity for characterizing daily physiology. However, accurate and efficient characterization remains an open problem because the complex autoregressive noise of the data makes it challenging to use a simple and efficient method for inference of clock proxies, least squares method. In this talk, we will introduce a simple approximation that alters the noise structure and thus enables one to use the least squares method. We will show its usefulness for real-time personalized fever detection in cancer patients.

Information Integration and Energy Expenditure in Gene Regulation
2021-12-24 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Information Integration and Energy Expenditure in Gene Regulation”, Estrada et al., Cell, 2016
Abstract: The quantitative concepts used to reason about gene regulation largely derive from bacterial studies. We show that this bacterial paradigm cannot explain the sharp expression of a canonical developmental gene in response to a regulating transcription factor (TF). In the absence of energy expenditure, with regulatory DNA at thermodynamic equilibrium, information integration across multiple TF binding sites can generate the required sharpness, but with strong constraints on the resultant “higher-order cooperativities.” Even with such integration, there is a “Hopfield barrier” to sharpness; for n TF binding sites, this barrier is represented by the Hill function with the Hill coefficient n. If, however, energy is expended to maintain regulatory DNA away from thermodynamic equilibrium, as in kinetic proofreading, this barrier can be breached and greater sharpness achieved. Our approach is grounded in fundamental physics, leads to testable experimental predictions, and suggests how a quantitative paradigm for eukaryotic gene regulation can be formulated.

디지털 표현형의 진단 및 치료적 적용
2021-12-29 15:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
디지털 표현형의 진단 및 치료적 적용 조철현(세종충남대학교병원) 디지털 표현형(digital phenotype)은 각 개개인이 일상생활에서 사용하는 다양한 디지털 기기를 통해서 실시간으로 얻어지는 다양한 형태의 데이터를 뜻하는 것으로, 디지털 기기의 사용이 보편화되면서 의료적 적용에 대한 가능성이 한층 높아지고 있다. 디지털 표현형은 이전에는 측정(measure)하기 힘들었던 영역에 대한 측정을 가능케 함으로써, 의학적 평가나 진단적인 측면에서 임상적 함의를 갖는다고 볼 수 있겠다. 실제 의료현장에서 충분히 접근하고 파악하지 못했던 임상적인 의미를 도출해 내거나 새로운 발견을 할 수 있는 근거로 활용할 수도 있겠다. 임상적 상태의 변화나 치료 효과, 예후 평가를 위한 기준으로 활용할 수도 있겠다. 또한, 디지털치료제의 개발과 적용에 있어서 디지털 표현형을 고려하고 반영하는 것은 매우 중요한 부분이 될 것이다. 디지털치료제(Digital Therapeutics)는 사람을 대상으로 치료, 예방, 예후 개선 등을 목적으로 인지, 행동, 생활습관 등의 변화를 이끌어내기 위한 소프트웨어 형태로서 디지털 시대의 새로운 치료적 옵션으로 주목받고 있다. 특히, 개인별, 맞춤형 치료적 접근을 위해서는 디지털 표현형에 대한 이해를 높이고 잘 활용하는 것이 필수적이다. 본 발표에서는 디지털 표현형의 정의와 특성, 임상적으로 어떤 함의를 가지고 있는 지에 대해 논의하고자 한다. 아울러, 디지털 표현형의 활용 가능성, 실제적 적용, 디지털치료제에의 적용을 위한 방향성에 대해 발표하고자 한다.

The Generalized Multiset Sampler
2021-12-31 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “The Generalized Multiset Sampler”, Kim and MacEachern, The Journal of Computation and Graphical Statistics, 2021
Abstract: The multiset sampler, an MCMC algorithm recently proposed by Leman and coauthors, is an easy-to-implement algorithm which is especially well-suited to drawing samples from a multimodal distribution. We generalize the algorithm by redefining the multiset sampler with an explicit link between target distribution and sampling distribution. The generalized formulation replaces the multiset with a K-tuple, which allows us to use the algorithm on unbounded parameter spaces, improves estimation, and sets up further extensions to adaptive MCMC techniques. Theoretical properties of the algorithm are provided and guidance is given on its implementation. Examples, both simulated and real, confirm that the generalized multiset sampler provides a simple, general and effective approach to sampling from multimodal distributions. Supplementary materials for this article are available online.

Stem cell dynamics in the intestine and stomach
2022-01-04 11:10 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
In adult tissues, stem cells undergo clonal competition because they proliferate while the stem cell niche provides limited space. This clonal competition allows the selection of healthy stem cells over time as unfit stem cells tend to lose from the competition. It could also be a mechanism to select a supercompetitor with tumorigenic mutations, which may lead to tumorigenesis. I am going to explain general concepts of clonal competition and how a simple model can explain the behaviour of adult stem cells. I will also show how geometric constraints affect the overall dynamics of stem cell competition.

Structure-based analysis of chemical reaction networks 1/2
2022-01-05 16:00 - 17:30 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract: Inside living cells, chemical reactions form a large web of networks. Understanding the behavior of those complex reaction networks is an important and challenging problem. In many situations, it is hard to identify the details of the reactions, such as the reaction kinetics and parameter values. It would be good if we can clarify what we can say about the behavior of reaction systems, when we know the structure of reaction networks but reaction kinetics is unknown. In these talks, I plan to introduce two approaches in this spirit. Firstly, we will discuss the sensitivity analysis of reaction systems based on the structural information of reaction networks [1]. I will give an introduction to the method of identifying subnetworks inside which the effects of the perturbation of reaction parameters are confined. Secondly, I will introduce the reduction method that we developed recently [2]. In those two methods, an integer determined by the topology of a subnetwork, which we call an influence index, plays a crucial role.
References
[1] T. Okada, A. Mochizuki, “Law of Localization in Chemical Reaction Networks,” Phys. Rev. Lett. 117, 048101 (2016); T. Okada, A. Mochizuki, “Sensitivity and network topology in chemical reaction systems,” Phys. Rev. E 96, 022322 (2017).
[2] Y. Hirono, T. Okada, H. Miyazaki, Y. Hidaka, “Structural reduction of chemical reaction networks based on topology”, Phys. Rev. Research 3, 043123 (2021).
