iTHEMS生物学セミナー
156 イベント
生物学に関連する様々なトピックを扱ったセミナーを定期的に開催しています。生物学と数学・物理学との境界を低くし、接点を見つけ出すことで、新しい学際的な研究のアイデアが生まれることを期待しています。
詳細はiTHEMS生物学セミナースタディーグループのページをご覧下さい。

Simple models of cancer growth, MCMC parameter estimation and identifiability
2021年4月8日(木) 10:00  11:00
カトゥリン・ボシゥメン (数理創造プログラム 副プログラムディレクター / Professor, Department of Physics, Ryerson University, Canada)
I would like to introduce some basic concepts about (very simple) mathematical model of cancer growth, the basic math behind parameter estimation via Markov chain Monte Carlo (MCMC) based on Bayes' theorem, and the different diagnostics you can use to know if the parameters are correctly estimated. I will use a recent example with cancer data in mice. I think this seminar can be interesting to mathematicians (because of the models and the math behind the parameter estimation, but the math is very basic!), to physicists (especially those that have to do some parameter estimation), and to biologists (the cancer model/data and the parameter estimation). I think it will also be interesting to the information theory and prediction science people. MCMC parameter estimation based on physical models is more valuable in my field than machine learning, so I think those interested in machine learning but maybe are not so familiar with MCMC should join to consider them as an alternative approach in certain contexts. *Please refer to the email to get access to the Zoom meeting room.
会場: via Zoom
イベント公式言語: 英語

Structural reduction of chemical reaction networks based on topology
2021年4月1日(木) 10:00  11:00
広野 雄士 (Junior Research Group Leader/Assistant Professor, Research Division, Asia Pacific Center for Theoretical Physics, Republic of Korea)
Chemical reactions form a complex network in living cells and they play vital roles for physiological functions. An amusing question is how the structure of a reaction network is linked to its chemical functionalities. I’ll talk about a method of the reduction of chemical reaction networks, which is convenient for extracting important substructures. Mathematical concepts such as homology and cohomology groups are found to be useful for characterizing the shapes of reaction networks and for tracking the changes of them under reductions. For a given chemical reaction network, we identify topological conditions on its subnetwork, reduction of which preserves the steady state of the remaining part of the network.
会場: via Zoom
イベント公式言語: 英語

Evolutionary conservativeness and diversification of cycads: Understanding the evolution of living fossils
2021年3月25日(木) 10:00  11:00
ホセ サイード・グティエレス オルテガ (千葉大学 国際未来教育基幹 助教)
The cycads are a lineage of gymnosperms that represent an example of biological stasis success. Despite their early origin in the seed plant evolution, they survived multiple events of mass extinction and could diversify in modern tropical ecosystems during the Cenozoic, especially in countries known for their great biodiversity such as Mexico. What factors have allowed their persistence and diversification despite their conservative nature? I have studied the cycad genus Dioon, a group of 17 species occurring in habitats ranging from tropical forests to arid zones in Mexico and Honduras. Phylogenetic and phylogeographic analyses revealed that the diversification of Dioon has been driven by the longterm process of aridification of Mexico since the Miocene. The lineages that shifted from mesic forests to arid zones show leaf trait variations beneficial against water stress; this feature can be also observed at the interpopulation level when comparing mesic versus arid sister pairs. What mechanism allows this aridificationdriven diversification? Using population genetics and ecological niche modeling on sister lineage pairs, I have revealed that lineages at arid zones might tolerate arid environments, but within the arid habitat, they retain the same ancestral niche also observed on their mesic sisters. The surrounding areas that are suboptimal for their niches serve as barriers against gene flow: this promotes allopatric speciation. This research has revealed that the mechanism that allows the diversification process in Dioon involves three factors: 1) a habitat shift due to aridification, 2) niche conservatism that facilitates geographic isolation, 3) gaining unique morphological and anatomical features that help to counteract water stress, probably through longterm stabilizing selection. This research highlights the importance of biological conservatism in evolution, and how “living fossils” can still diversify into modern ecosystems.
会場: via Zoom
イベント公式言語: 英語

セミナー
Microeconomics of metabolism
2021年3月10日(水) 10:00  11:00
山岸 純平 (東京大学 金子研究室 博士課程)
Metabolic behaviors of proliferating cells are often explained as a rational choice to optimize cellular growth rate. In contrast, microeconomics formulates consumption behaviors as optimization problems of utilities. We pushed beyond this analogy to precisely map metabolism onto the theory of consumer choice. We thereby revealed the correspondence between and a general mechanism for mysteries in biology and economics: the Warburg effect, a seeminglywasteful but ubiquitous phenomenon where cells favor aerobic glycolysis over more energeticallyefficient respiration, and Giffen behavior, the unexpected consumer behavior where a good is demanded more as its price rises. The correspondence implies that respiration is counterintuitively stimulated when its efficiency is decreased by drug administration. This “microeconomics of metabolism” will serve as a macroscopic phenomenology to predict the metabolic responses against environmental operations. In particular, it offers a universal relationship between the metabolic responses against drug administrations and changes in nutrient availability.
会場: via Zoom
イベント公式言語: 英語

Origin of nonlinearity of large deformation on DNA stretched
2021年2月25日(木) 10:00  11:00
横田 宏 (数理創造プログラム 特別研究員)
Since DNA in a cell is mechanically stretched or rotated by many proteins, the mechanical response of DNA in vitro is expected to be basic point for understanding its behavior. When DNA is stretched by relatively high force, the length of DNA shows the nonlinear response. In this talk, I introduce the theoretical treatment of DNA stretching in high force region based on polymer physics.
会場: via Zoom
イベント公式言語: 英語

セミナー
Highthroughput laboratory evolution with machine learning reveals constraints for drug resistance evolution
2021年2月18日(木) 10:00  11:00
岩澤 諄一郎 (東京大学 大学院理学系研究科物理学専攻 博士課程)
The understanding of evolution is crucial to tackle the problem of antibiotic resistance which is a growing health concern. Although the lack of sufficient data has long hindered the mechanism of evolution, laboratory evolution experiments equipped with highthroughput sequencing/phenotyping are now gradually changing this situation. The emerging data from recent laboratory evolution experiments have revealed repeatable features in evolutionary processes, suggesting the existence of constraints on evolutionary outcomes [1,2]. Despite its importance for understanding evolution, however, we still lack a systematic investigation for evolutionary constraints. In this seminar, I would like to talk about two projects on the investigation of evolutionary constraints using data acquired from laboratory evolution of Escherichia coli. In the first half, I will explain how to extract an effective latent space for probing constraints in resistance evolution using gene expression data. We will further discuss what kind of structure exists in this space [3]. In the latter half, I will talk about our recent study on how to construct a predictive model for evolution using the information of evolutionary constraints.
会場: via Zoom
イベント公式言語: 英語

セミナー
A machine learning approach for prediction of mitochondrial proteins in nonmodel organisms
2021年2月12日(金) 10:00  11:00
久米 慶太郎 (筑波大学 医学医療系 助教)
The evolution of the repertoire of proteins localized to organelles is important for understanding the evolutionary process of organelles. However, experimental methods for identifying organellelocalized proteins have been established only for model organisms and some organisms. Therefore, prediction methods using sequence data obtained from genome and transcriptome analyses, which are relatively easy to obtain, are useful. However, such prediction methods had also been established only for model organisms. In this talk, I will introduce our study in which a machine learning method was used to obtain protein candidates localized to mitochondrionrelated organelles in nonmodel organisms.
会場: via Zoom
イベント公式言語: 英語

セミナー
System identification of mechanochemical epithelial sheet dynamics
2021年2月4日(木) 10:00  11:00
浅倉 祥文 (京都大学 大学院生命科学研究科 博士課程)
Collective migration of epithelial cells is a fundamental process of multicellular organisms. Our recent study using live imaging with FRETbased biosensor discovered that cell migration within an epithelial sheet is oriented by traveling waves of ERK activation. However, it is still elusive how the cells make a decision on migration direction by integrating mechanochemical signals. Here, we performed reverseengineering approach to extract a hidden control mechanism in the epithelial sheet dynamics in a datadriven manner. Our model has an ability to forecast cell migration quantified in timelapse images. Therefore, our approach would be powerful to understand mechanochemical epithelial sheet dynamics.
会場: via Zoom
イベント公式言語: 英語

セミナー
Numerical inference of the molecular origin of the cyanobacterial circadian rhythm
2021年1月28日(木) 10:00  11:00
甲田 信一 (分子科学研究所 計算分子科学研究領域 助教)
The cyanobacterial clock proteins, KaiA, KaiB, and KaiC, are known as the simplest biological clock; Just by mixing them with ATP in a test tube, selfsustaining oscillation with a nearly 24h temperaturecompensated period is reconstituted. To elucidate the molecular mechanisms of this oscillator, experimental studies have revealed and investigated in detail various elementary reactions/processes, ranging from local chemical reactions of ligands to global (dis)assembly of the proteins. Yet, proposing molecularly detailed mechanisms of the clock functions is still difficult because almost all experimentally measurable quantities are the results of complicated interplays between many elementary processes, i.e. independent measurement of an elementary process is difficult. In this talk, I will present a numerical approach to obtain the rate constants of the elementary processes from experimental data [1, 2]. First, a reaction model consisting of rate equations of the elementary processes is built. Then, their rate constants and temperature dependence are inferred by simultaneously fitting model outputs to multiple types of experimental data (such as phosphorylation reactions and ATPase activity) at various temperatures. On the basis of the inferred parameter values, we can quantitatively discuss how the clock functions arise from the interplays between elementary processes. As an example, I will present a potential molecular mechanism of the temperature compensation of period.
会場: via Zoom
イベント公式言語: 英語

Introduction to Boolean modeling and Boolean networks as information processing units
2021年1月21日(木) 10:00  11:00
岡田 崇 (数理創造プログラム 上級研究員)
Boolean networks are widely used in physics, biology, social science, and computer science. In this talk, I will introduce the basics of Boolean networks and give an overview of Biological applications. Then, I will discuss information transfer in Boolean networks and discuss optimal design principles. The latter part of the talk is based on joint work with Fumito Mori (Kyushu Univ).
会場: via Zoom
イベント公式言語: 英語

What are genes and how can we find them?
2021年1月14日(木) 10:00  11:00
ジェフリ・フォーセット (数理創造プログラム 上級研究員)
Although 'gene' is a word that is used frequently in the society, most people probably do not know what genes actually are. In fact, its definition is not so straightforward. In this talk, I will first give a historical perspective and our current understanding of what genes are and what they look like. Then, I will talk about 'gene prediction'. Once we obtain the DNA (genome) sequence data of a given species, we must 'find' the genes within the genome. This involves computational prediction utilizing probabilistic models and various sources of external evidence. I will briefly explain how this is done.
会場: via Zoom
イベント公式言語: 英語

セミナー
From local resynchronization to global pattern recovery in the zebrafish segmentation clock
2021年1月7日(木) 10:00  11:00
瓜生 耕一郎 (金沢大学 生命理工学類 助教)
Tissuescale developmental patterns are often generated by local cellular interactions and global tissue deformation. An example is gene expression rhythms in vertebrate, termed the segmentation clock. The oscillatory spatial pattern of the segmentation clock across a tissue determines the timing of body segment formation. In this seminar, we discuss pattern recovery in the zebrafish segmentation clock after perturbation in oscillator coupling. To predict pattern recovery in embryos, we develop a physical model that describes both cell mechanics and genetic oscillations. We show that the physical model explains experimentally observed intermingled segmental defects, and their axial distributions in different embryonic developmental stages. Our analysis suggests that pattern recovery in developing tissues occurs at two scales; local pattern formation and transport of these patterns through tissue morphogenesis.
会場: via Zoom
イベント公式言語: 英語

Mathematical modelbased quantitative data analysis for COVID19
2020年12月22日(火) 10:00  11:00
岩見 真吾 (九州大学 大学院理学研究院 生物科学部門数理生物学研究室 准教授)
The recent spread of corona threatens the health of people around the world. We urgently need strategies to reduce COVID19 spread and to enhance antiviral drug development for individual patients. Mathematics could contribute to control of COVID19 pandemic by informing decisions about pandemic planning, resource allocation, and implementation of social distancing measures and other interventions. My group is conducting interdisciplinary research to elucidate "Quantitative Population Dynamics" with original mathematical theory and computational simulation, which are both our CORE approach. Our mathematical modelbased approach has quantitatively improved a current goldstandard approach essentially relying on the statistical analysis of "snapshot data" during dynamic interaction processes in virus infection. In my talk, I would like to discuss how our approach improves our current understanding of COVID19 research, and help an establishment of a "standard antiviral treatment" for COVID19 as well.
会場: via Zoom
イベント公式言語: 英語

セミナー
Autoimmune diseases initiated by pathogen infection: mathematical modeling
2020年12月17日(木) 10:00  11:00
原 朱音 (九州大学 システム生命科学府 一貫制博士課程)
The pathogen with proteins similar to host’s proteins is likely to cause autoimmunity, which is called “molecular mimicry”. To understand the mechanism of autoimmunity development caused by pathogen infection, we considered the following scenario: the infection activates the immune system, which results in clearance of pathogens, and the enhanced immune responses to the host’s body may remain and attack the host’s cells after the pathogen clearance. We developed a mathematical model describing the dynamics of T helper (Th) cells, viruses, selfantigens, and memory T cells and identified the conditions necessary to realize the scenario. We considered the crossimmunity of three different modes of action: [1] virus elimination by Th cells reactive to the selfantigen, [2] activation of Th cells reactive to viruses by selfantigens and Th cells reactive to selfantigens by viruses, and [3] enhancement of immune responses to selfantigens by Th cells reactive to viruses after the infection. The crossimmunity of type [3] was found to be most important for autoimmunity development. In contrast, [1] and [2] suppressed autoimmunity by effectively decreasing the viral abundance.
会場: via Zoom
イベント公式言語: 英語

How to obtain the large amount of sequence data from the eukaryote
2020年12月10日(木) 10:00  11:00
矢﨑 裕規 (数理創造プログラム 特別研究員)
Most of the modern biology is supported by genetic sequence data. Recent advances in sequencing technology have made it possible to obtain comprehensive and large numbers of sequence data from a small amount of samples, which are deposited in public databases and are easily available. In this talk, I want to give an overview of how these large scale sequence data are obtained from samples and how they become available for us to use in our biological studies, through my eukaryotic sequence studies.
会場: via Zoom
イベント公式言語: 英語

セミナー
Rotifer can be a good model organism for theoretical biology
2020年11月27日(金) 10:00  11:00
小南 友里 (東京大学 大学院農学生命科学研究科 特任助教)
Rotifers are cylindrical zooplankton which constitute the phylum Rotifera. They have organs and tissues including ganglia, muscles, digestive organs, ovaries, and sensory organs in their <1mm body. Rotifers are suitable for the study on the population dynamics and longevity due to their short generation time. Furthermore the most attractive characteristic of the rotifers is asexual propagation, makes it easy to obtain clonal cultures. The genomic and transcriptomic database are developed and molecular biological techniques such as RNAi for using rotifers have been established. In this seminar, other attractive characteristics of rotifer as a model organism for theoretical biology and great studies using rotifers will be introduced. Our recent results of investigating the effects of calorie condition on longevity will be discussed.
会場: via Zoom
イベント公式言語: 英語

セミナー
Symmetry and conservation laws in neural networks
2020年11月20日(金) 10:00  11:00
Hidenori Tanaka (Group Leader & Senior Scientist, Physics & Informatics Laboratories, NTT Research, Inc., USA / Visiting Scholar, Stanford University, USA)
Symmetry is the central guiding principle in the exploration of the physical world but has been underutilized in understanding and engineering neural networks. We first identify simple yet powerful geometrical properties imposed by symmetry. Then, we apply the theory to answer a series of following important questions: (i) What, if anything, can we quantitatively predict about the complex learning dynamics of realworld deep learning models driven by realworld datasets? (ii) How can we make deep learning models more efficient by removing parameters without disconnecting information flow? (iii) How can we distill experimentally testable neuroscientific hypotheses by reducing the complexity of deep learning models mimicking the brain? Overall, our approach demonstrates how we can harness the principles of symmetry and conservation laws to reduce deep learning models' complexity and make advances in the science and engineering of biological and artificial neural networks.
会場: via Zoom
イベント公式言語: 英語

セミナー
Evolution of a peak of genetic divergence driven by local adaptation
2020年11月5日(木) 10:00  11:00
坂本 貴洋 (総合研究大学院大学 先導科学研究科 特別研究員)
In species that are distributed in various environments, each subpopulation adapts to the local environment. In general, when there is migration between subpopulations, genetic divergence does not proceed because the genomes are exchanged between subpopulations. However, around the loci involved in local adaptation, genetic divergence proceeds. This is because different genotypes are favored between subpopulations, so that the alleles of migrants are purged by natural selection and the exchange of genomes is suppressed. It has not been theoretically known how the degree of genetic differentiation evolves over time, making the interpretation of population genomic data difficult. In this study, we constructed and analyzed a model of population genetics to clarify the dynamics of genetic divergence.
会場: via Zoom
イベント公式言語: 英語

Basics of population genomic data analysis
2020年10月29日(木) 10:00  11:00
ジェフリ・フォーセット (数理創造プログラム 上級研究員)
In recent years, it has become possible to obtain the DNA sequence data of a large number of individuals of the same species. This data set is basically a M (number of samples) x N (number of genomic positions) matrix where each data point is 0 or 1. Using this data set, we try to understand, for example, the relationship between each sample or group of samples, and the population process that has generated the data set. In this talk, I will introduce the basic concepts behind the approaches we use to analyze such data sets.
会場: via Zoom
イベント公式言語: 英語

セミナー
Bayesian nonparametric estimation of Random Dynamical Systems
2020年10月21日(水) 14:00  15:00
Christos Merkatas (Postdoctoral Researcher, Aalto University, Finland)
In this talk, a Bayesian nonparametric framework for the estimation and prediction, from observed time series data, of discretized random dynamical systems is presented [1]. The size of the observed time series can be small and the additive noise may not be Gaussian distributed. We show that as the dynamical noise departs from normality, simple Markov Chain Monte Carlo method (MCMC) models are inefficient. The proposed models assume an unknown error process in the form of a countable mixture of zero mean normals, where a–priori the number of the countable normal components and their variances is unknown. Our method infers the number of unknown components and their variances, i.e., infers the density of the error process directly from the observed data. An extension for the joint estimation and prediction of multiple discrete time random dynamical systems based on multiple timeseries observations contaminated by additive dynamical noise is presented [2]. In this case the model assumes an unknown joint error process with a pairwise dependence in the sense that to each pair of unknown dynamical error processes, we assign a– priori an independent Geometric StickBreaking process mixture of normals with zero mean. These mixtures a–posteriori will capture common characteristics, if there are any, among the pairs of noise processes. We show numerically that when the unknown error processes share common characteristics, it is possible under suitable prior specification to induce a borrowing of strength relationship among the dynamical error pairs. Then timeseries with an inadequate sample size for an independent Bayesian reconstruction can benefit in terms of model estimation accuracy. Finally, possible directions for future research will be discussed.
会場: via Zoom
イベント公式言語: 英語
156 イベント
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