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Introduction to Boolean modeling and Boolean networks as information processing units
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).
What are genes and how can we find them?
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.
From local resynchronization to global pattern recovery in the zebrafish segmentation clock
瓜生 耕一郎 (金沢大学 生命理工学類 助教)
Tissue-scale 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.
Mathematical model-based quantitative data analysis for COVID-19
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 COVID-19 spread and to enhance antiviral drug development for individual patients. Mathematics could contribute to control of COVID-19 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 model-based approach has quantitatively improved a current gold-standard 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 COVID-19 research, and help an establishment of a "standard antiviral treatment" for COVID-19 as well.
Autoimmune diseases initiated by pathogen infection: mathematical modeling
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, self-antigens, and memory T cells and identified the conditions necessary to realize the scenario. We considered the cross-immunity of three different modes of action: [1] virus elimination by Th cells reactive to the self-antigen, [2] activation of Th cells reactive to viruses by self-antigens and Th cells reactive to self-antigens by viruses, and [3] enhancement of immune responses to self-antigens by Th cells reactive to viruses after the infection. The cross-immunity 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.
How to obtain the large amount of sequence data from the eukaryote
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.
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.
Symmetry and conservation laws in neural networks
Dr. 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 real-world deep learning models driven by real-world 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.
Evolution of a peak of genetic divergence driven by local adaptation
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.
Basics of population genomic data analysis
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.
Bayesian nonparametric estimation of Random Dynamical Systems
2020年10月21日(水) 14:00 - 15:00
Dr. 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 time-series 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 Stick-Breaking 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 time-series 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.
A PDE model for the localization and spread of flu in the human respiratory tract
クリスチアン・キルエットゥ (Ph.D. Student, Department of Medical Physics, Ryerson University, Canada)
Within the human respiratory tract (HRT), virus diffuses through the periciliary fluid (PCF) bathing the epithelium. But it also undergoes advection: as the mucus layer sitting atop the PCF is pushed along by the ciliated cell's beating cilia, the PCF and its virus contents are also pushed along, upwards towards the nose and mouth. Our PDE model represents the HRT as a one-dimensional track extending from the nose down to the lower HRT, wherein stationary cells interact with virus which moves within (diffusion) and along with (advection) the PCF. In the PDE model, diffusion is negligible in the presence of advection which effectively sweeps away virus, preventing infection from spreading below the depth of deposition. Higher virus production rates (10-fold) are required at higher advection speeds (40 micron/s) to maintain equivalent infection severity and timing. Because virus is entrained upwards, upper parts of the HRT located downstream of the advection flow see more virus than lower parts, and so infection grows, peaks, and resolves later in the lower HRT. Clinically, the infection would appear to progress from the upper towards the lower HRT, as reported in mice. When the PDE model is expanded to include cellular regeneration and an immune response, it reproduces tissue damage levels reported in patients. This new PDE model offers a convenient and unique platform from which to study the localization and spread of respiratory viruses (flu, RSV, COVID-19) within the HRT during an infection.
Phase Transitions in Biological Systems
Biological systems are built hierarchically by DNA, proteins, cells, tissues, organs, individuals, etc. Recent experiments have clarified the existence of interesting mesoscale phenomena inside cells, where the concept of condensed matter physics such as phase transition can be useful in its understanding. For example, interacting nucleosomes in a chromatin chain can cause the mega-base scale structural change, and sub-micron scale dense droplets of proteins/mRNAs can appear through phase separation. In this talk, I will discuss our recent topics: (i) structural transition of a chromatin with epigenetic marks, (ii) intracellular wetting of phase-separated droplets, and (iii) spontaneous aggregation of self-propelled individuals.
Eco-evolutionary dynamics with novel mutations
Dr. Hye Jin Park (Junior Research Group Leader, Statistical physics of ecology and evolution group, Asia Pacific Center for Theoretical Physics, Republic of Korea)
Evolution is driven by individual birth and death that are determined by interactions between individuals. Hence studying interactions is crucial to understand the population evolution. However, traditional approaches dealt with those interaction structures are given while spontaneous random mutations can generate new interactors. We considered "mutant interactors," which lead to new interactions between the residents and invading mutants that can drive the population away from the previous equilibrium and lead to changes in the population composition. Thus, first, we investigated the changes in the population size induced by mutant interactors[1]. And then, we applied this approach to answer the question about relationships between species[2]: Why is cyclic dominance so rare?
Singular point implies coexistence in adaptive dynamics
2020年9月9日(水) 10:00 - 11:00
立川 正志 (数理創造プログラム 客員研究員 / 京都⼤学 ウイルス・再⽣医学研究所 ⽣命システム研究部⾨ 准教授)
Adaptive dynamics is a relatively new mathematical framework for studying evolution(~1990s). Under the influence of the mathematical ecology and the game theory, adaptive dynamics considers the effect of resident populations on the fitness landscape. As a result, it explains a possible mechanism of evolutionary branching. In this talk, I introduce adaptive dynamics and Pairwise Invasibility Plot (PIP) analysis, a standard method for understanding the adaptive dynamics. Then, I propose a new approach to analyze the adaptive dynamics which enable us to understand higher dimensional systems than PIP does.
The hitch-hiker's guide to the concept of adaptive dynamics
Adaptation is of multi-causality, composed of mutation and selection processes. I will talk about how we model adaptation on the basis of the adaptive dynamics framework. This is a very quick, conceptual talk, rather than heavily mathematical, to draw attention from more people.
Modeling biological timing
Under stay-at-home situation, some of you may suffer from sleep disorder. Efficacy of a drug often depends on the timing of its prescription. We know this fact about our "timing", but we don't know why. This time, I wish to introduce two big mysteries in regard to biological timing. First is our internal daily clock. In general, biochemical process is believed to accelerate with temperature. In contrast, the period of our daily clock, made up of biochemical reactions is somehow stable to temperature. The prediction from simpler biochemical mathematical model, and its experimental verification will be presented. Second is hibernation. During winter, some birds and mammals decrease drastically their body temperature possibly to decrease their energy expenditure. Many studies about hibernation have been conducted for many years. However, basic mechanisms of hibernation (e.g. how the duration of hibernation is determined?) are largely unknown. Recently, we started to investigate body temperature time-series of hibernating hamsters over 100 days in the collaboration with experimental biologists. Preliminary results will be presented.
Heavy tails in the brain
Dr. Lukasz Kusmierz (理化学研究所 脳神経科学研究センター (CBS) 研究員)
In my talk I will discuss the relation between two seemingly unrelated measures in the brain that exhibit heavy tails: neuronal avalanches, i.e. bursts of activity with power-law distributions of sizes and lifetimes, and synaptic weights that are believed to be distributed according to the log-normal distribution. Many current models of neuronal avalanches do not rely on heavy-tailed synaptic weight distributions, suggesting that heavy tails of these two quantities may not be related. However, our recent theoretical considerations indicate that this independence no longer holds if two biologically relevant constraints are introduced, i.e., that neurons (1) receive many incoming connections and (2) do not spike if the membrane potential is below some positive threshold, e.g., in the absence of inputs. Under these assumptions we have shown that heavy tails of synaptic weights are necessary to generate biologically plausible low activity levels and associated neuronal avalanches. Our results suggest that the observed distributions of synaptic weights may play important functional roles in the brain.
Time-dependent bias emerges in population models with broad offspring number distributions
It has been increasingly recognized that natural populations exhibit broad offspring number distributions, either because offspring numbers are strongly variable (e.g. marine organisms) or because range expansion processes generate jackpot events. In this talk, I will review the basic concepts of theoretical population genetics and then discuss how broad offspring number distributions affect the evolutionary dynamics.
Human Time vs. Mouse Time in Embryonic Development
戎家 美紀 (Group Leader, European Molecular Biology Laboratory, Barcelona, Spain)
Different species have different tempos of development: larger animals tend to grow more slowly than smaller animals. My group has been trying to understand the molecular basis of this interspecies difference in developmental time, using the segmentation clock as a model system. The segmentation clock is the oscillatory gene expressions that regulate the timing of body segment formation during early embryogenesis. We have recently succeeded in recapitulating the segmentation clock from both human and mouse pluripotent stem cells, detecting oscillations and traveling waves in vitro. Interestingly, the oscillation period of human segmentation clock was 5-6 hours while that of mouse was 2-3 hours. Taking advantage of our in vitro system and simple mathematical models, we have been comparing the genome sequences and molecular processes of the segmentation clock between human and mouse to explain the interspecies difference in the oscillation period.