Biomedical Mathematics Group

Biofluiddynamics of reproduction
2021-11-11 11:00 - 12:00 (Asia/Seoul) | 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).

Structure-based analysis of chemical reaction networks 2/2
2022-01-06 16:00 - 17:30 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
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.
[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).

Fundamental limits on the suppression of molecular fluctuations
2022-01-07 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Fundamental limits on the suppression of molecular fluctuations”, Lestas et al, Nature, 2010
Abstract: Negative feedback is common in biological processes and can increase a system’s stability to internal and external perturbations. But at the molecular level, control loops always involve signalling steps with finite rates for random births and deaths of individual molecules. Here we show, by developing mathematical tools that merge control and information theory with physical chemistry, that seemingly mild constraints on these rates place severe limits on the ability to suppress molecular fluctuations. Specifically, the minimum standard deviation in abundances decreases with the quartic root of the number of signalling events, making it extremely expensive to increase accuracy. Our results are formulated in terms of experimental observables, and existing data show that cells use brute force when noise suppression is essential; for example, regulatory genes are transcribed tens of thousands of times per cell cycle. The theory challenges conventional beliefs about biochemical accuracy and presents an approach to the rigorous analysis of poorly characterized biological systems.

Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation
2022-01-13 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation”, Wagner et al, bioRxiv, 2021
Motivation: The Chemical Master Equation is the most comprehensive stochastic approach to describe the evolution of a (bio-)chemical reaction system. Its solution is a time-dependent probability distribution on all possible configurations of the system. As the number of possible configurations is typically very large, the Master Equation is often practically unsolvable. The Method of Moments reduces the system to the evolution of a few moments of this distribution, which are described by a system of ordinary differential equations. Those equations are not closed, since lower order moments generally depend on higher order moments. Various closure schemes have been suggested to solve this problem, with different advantages and limitations. Two major problems with these approaches are first that they are open loop systems, which can diverge from the true solution, and second, some of them are computationally expensive.
Results: Here we introduce Quasi-Entropy Closure, a moment closure scheme for the Method of Moments which estimates higher order moments by reconstructing the distribution that minimizes the distance to a uniform distribution subject to lower order moment constraints. Quasi-Entropy closure is similar to Zero-Information closure, which maximizes the information entropy. Results show that both approaches outperform truncation schemes. Moreover, Quasi-Entropy Closure is computationally much faster than Zero-Information Closure. Finally, our scheme includes a plausibility check for the existence of a distribution satisfying a given set of moments on the feasible set of configurations. Results are evaluated on different benchmark problems.

다중 오믹스 분야의 현황 및 유전자-환경 상호 모델링의 필요성 (Current status of multi-omics research field and necessity of gene-by-environment (GxE) interaction modeling)
2022-01-18 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
본 발표에서는 다양한 기초 생명-의학 분야에서 생성되고 있는 오믹스 자료의 연구 개발 현황에 대해서 다룰 예정이다. 보다 큰 규모로, 보다 빠르게, 보다 정확하게, 보다 정밀하게 라는 궁극적인 목표하에 이뤄지고 있는 오믹스 자료의 진화에 발맞춰, 이를 분석하는 수리통계적 모형 역시 진화하고 있다. 그 중, 이번 발표에서는 미국의 초 대형 정밀의료 프로젝트인 TopMed에서 진행하고 있는 COPD에 관한 다중 오믹스 자료의 통합 분석 방법 및 결과에 대해서 자세히 다룰 예정이다. 아울러 정밀의료라는 목표를 달성하기 위해 반드시 모형에서 고려해야 하는 “환경 특이적 효과”에 대해 강연할 예정이다.

Network design principle for robust oscillatory behaviors with respect to biological noise
2022-01-19 11:00 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Network design principle for robust oscillatory behaviors with respect to biological noise”, Qiao et al, bioRxiv, 2021
Oscillatory behaviors, which are ubiquitous in transcriptional regulatory networks, are often subject to inevitable biological noise. Thus a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. Here, we search all two- and three-node transcriptional regulatory network topologies for those robustly capable of accurate oscillation against the parameter variability (extrinsic noise) or stochasticity of chemical reactions (intrinsic noise). We find that, no matter what source of the noise is applied, the topologies containing the repressilator with positive auto-regulation show higher robustness of accurate oscillation than those containing the activator-inhibitor oscillator, and additional positive auto-regulation enhances the robustness against noise. Nevertheless, the attenuation of different sources of noise is governed by distinct mechanisms: the parameter variability is buffered by the long period, while the stochasticity of chemical reactions is filtered by the high amplitude. Furthermore, we analyze the noise of a synthetic human nuclear factor κB (NF-κB) signaling network by varying three different topologies, and verify that the addition of a repressilator to the activator-inhibitor oscillator, which leads to the emergence of high-robustness motif—the repressilator with positive auto-regulation, improves the oscillation accuracy in comparison to the topology with only an activator-inhibitor oscillator. These design principles may be applicable to other oscillatory circuits.

Introduction to Bayesian Variable Selection.
2022-01-27 11:00 - 13:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract:
Variable selection is an approach to identifying a set of covariates that are truly important to explain the feature of a response variable. It is closely connected or belongs to model selection approaches. This talk provides a gentle introduction to Bayesian variable selection methods. The basic notion of variable selection is introduced, followed by several Bayesian approaches with a simple application example.

Mechanisms for the generation of robust circadian oscillations through ultrasensitivity and differential binding affinity
2022-02-04 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Mechanisms for the generation of robust circadian oscillations through ultrasensitivity and differential binding affinity”, Behera, Junco, and Vaikuntanathan, The Journal of Physical Chemistry B, 2021
Biochemical circadian rhythm oscillations play an important role in many signaling mechanisms. In this work, we explore some of the biophysical mechanisms responsible for sustaining robust oscillations by constructing a minimal but analytically tractable model of the circadian oscillations in the KaiABC protein system found in the cyanobacteria S. elongatus. In particular, our minimal model explicitly accounts for two experimentally characterized biophysical features of the KaiABC protein system, namely, a differential binding affinity and an ultrasensitive response. Our analytical work shows how these mechanisms might be crucial for promoting robust oscillations even in suboptimal nutrient conditions. Our analytical and numerical work also identifies mechanisms by which biological clocks can stably maintain a constant time period under a variety of nutrient conditions. Finally, our work also explores the thermodynamic costs associated with the generation of robust sustained oscillations and shows that the net rate of entropy production alone might not be a good figure of merit to asses the quality of oscillations.

Stochastic Modeling of Foot and Mouth Diseases with Vehicle Network & Assessment of Social Distancing for Controlling COVID-19 in Korea
2022-02-08 11:00 - 11:30 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract: TBA

수리모델을 통한 전염병 통제 분석
2022-02-08 11:30 - 12:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of

Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
2022-02-11 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations”, Mircea et al., 2022, Genome Biology
The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (ϕ_clust), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non-random substructure, testably leading to the discovery of previously overlooked phenotypes.

A Deficiency-Based Approach to Parametrizing Positive Equilibria of Biochemical Reaction Systems
2022-02-18 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “A Deficiency-Based Approach to Parametrizing Positive Equilibria of Biochemical Reaction Systems”, Johnston, Müller, and Pantea, Bulletin of Mathematical Biology, 2019
We present conditions which guarantee a parametrization of the set of positive equilibria of a generalized mass-action system. Our main results state that (1) if the underlying generalized chemical reaction network has an effective deficiency of zero, then the set of positive equilibria coincides with the parametrized set of complex-balanced equilibria and (2) if the network is weakly reversible and has a kinetic deficiency of zero, then the equilibrium set is nonempty and has a positive, typically rational, parametrization. Via the method of network translation, we apply our results to classical mass-action systems studied in the biochemical literature, including the EnvZ–OmpR and shuttled WNT signaling pathways. A parametrization of the set of positive equilibria of a (generalized) mass-action system is often a prerequisite for the study of multistationarity and allows an easy check for the occurrence of absolute concentration robustness, as we demonstrate for the EnvZ–OmpR pathway.

Spatiotemporal reconstruction of static single-cell genomics data
2022-03-03 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: Cells make fate decisions in response to dynamic environments and multicellular structure emerges from interplays among cells in space and time. The recent single-cell genomics technology provides an unprecedented opportunity to profile cells. However, those measurements are taken as snapshots for groups of individual cells with only static information. Can one infer interactions among cells from such datasets? Is it possible to recover spatial information from non-spatial datasets? How to obtain temporal relationships of cells from the static measurements? In this talk I will present our newly developed computational tools that reconstruct interactions and spatiotemporal relationships for cells using single-cell RNA-seq, ATAC-seq, and spatial transcriptomics datasets. Through applications of those methods to systems in development and regeneration, we show the discovery power of such methods and identify areas for further development in spatiotemporal reconstruction.

Modeling polypharmacy side effects with graph convolutional networks
2022-03-04 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Modeling polypharmacy side effects with graph convolutional networks”, Zitnik, Agrawal, and Leskovec, Bioinformatics, 2018
Motivation
The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.
Results
Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.

Transcription factor competition facilitates self-sustained oscillations in single gene genetic circuits
2022-03-11 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract: Genetic feedback loops can be used by cells as a means to regulate internal processes or keep track of time. It is often thought that, for a genetic circuit to display self-sustained oscillations, a degree of cooperativity is needed in the binding and unbinding of actor species. This cooperativity is usually modeled using a Hill function, regardless of the actual promoter architecture. Moreover, genetic circuits do not operate in isolation and often transcription factors are shared between different promoters. In this work we show how mathematical modelling of genetic feedback loops can be facilitated with a mechanistic fold-change function that takes into account the titration effect caused by competing binding sites for transcription factors. The model shows how the titration effect aids self-sustained oscillations in a minimal genetic feedback loop: a gene that produces its own repressor directly — without cooperative transcription factor binding. The use of delay differential equations leads to a stability contour that predicts whether a genetic feedback loop will show self-sustained oscillations, even when taking the bursty nature of transcription into account.
