Schedule


TimeSpeaker/ Title/ Abstract/ VODLocation
08:00 - 08:30 Breakfast
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08:30 - 09:00 Opening Remark
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09:00 - 09:40 Mogen Jensen / Arnold Tongues and Mode Hopping in Cell Dynamics
Oscillating genetic patterns have been observed in networks related to the transcription factors NFkB, p53 and Hes1. We have identified the central feed-back loops leading to oscillations. By applying an external eriodic signal, it is possible to lock the internal oscillation to the external signal. For the NF-kB systems in single cells we have observed that the two signals lock when the ration between the two frequencies is close to basic rational numbers [1]. The resulting response of the cell can be mapped out as Arnold tongues. When the tongues start to overlap we may observe a chaotic dynamics of the concentration in NF-kB [1]. The group of Savas Tay (ETH, Zurich) has in single cell dynamics of the NF-kB system observed transitions from one tongue to the other when they overlap. We investigate this effect by Gillespie simulations and observe mode hopping transitions between different tongues in good agreement with the experiments [2]. The distribution of waiting times between subsequent mode hoppings follow a stretched exponential indicating strong correlations. The mode hopping dynamics in the stochastic system resembles very much the chaotic dynamics of the deterministic system [2].
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09:40 - 10:10 Yasuaki Kobayashi / Dynamics and distribution of epidermal stem cells on a membrane
Epidermal stem cells and transit amplifying cells they produce form a monolayer called the basal layer, which is attached to the basal membrane that separates the epidermis and the dermis. It is well known that the dermis has an undulated surface with many protrusions toward the epimermis, and the stem cells tend to be found on top of the protrusions. Disrupted stem cell distribution may alter the pattern of cell supply and may impair epidermal homeostasis. Hence it is important to understand the mechanism of their distribution, yet little is known about it. Also, while studies have suggested that in biological tissues the growth of a cell layer on top of an elastic structure may cause buckling instabilities leading to undulated structures, in the case of dermal undulations there still have been no suggestion on the factors determining the direction of protrusions and their possible relationship to the stem cell distribution. We approach these problems by introducing a particle-based model of cells repeating division on a deformable membrane. Our model can sucsessfuly simulate the process of protrusion formation in the basal membrane, on top of which stem cells are located. We find that, while the protrusion height is determined by the frequency of cell division, location of the cells is determined by the difference of binding forces between stem cells and transit amplifying cells.
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10:10 - 10:40 Coffee Break
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10:40 - 11:10 Masatoshi Nishikawa /Controlling contractile instabilities in the actomyosin cortex
The actomyosin cortex is an active contractile material for driving cell- and tissue morphogenesis. The cotrtex has a tendency to form a pattern of myosin foci, which is a signature of potentially unstable behavior. A fundamental challenge is to understand how the actomyosin cortex that is prone to such instabilities can reliably drive large scale morphogenetic events. In this talk, I will present in Caenorabditis elegans one-cell stage embryo, that the cortex exhibits the contractile instability, in which the interplay between the active contractility generated by myosin and the RhoA mediated biochemical regulation. We identified a RhoA pacemaking oscillator that control this instability to prevent the collapse of the cortex into dynamic contractile patches. This work highlights how contractile instabilities that are often inevitable in active contractile material can be biochemically controlled to drive large scale morphognetic events.
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11:10 - 11:40 Sungrim Seirin-Lee / A mystery of remodeling process in nuclear architecture
Nuclear architecture, which plays an important role in organizing the function of the nucleus, is composed of heterochromatin and euchromatin. Conventional nuclear architecture is found when the distribution of heterochromatin is enriched in the periphery of the nucleus. Conventional architecture is the primary structure in the majority of eukaryotic cells, and the rod cells of diurnal mammals contain this structure. In contrast, inverted nuclear architecture occurs when the heterochromatin is distributed in the center of the nucleus; this occurs in the rod cells of nocturnal mammals. Surprisingly, the inverted architecture found in the rod cells of the adult mouse is formed through reorganization of the conventional architecture during terminal differentiation. Although an experimental approach has shown the relationship between these two types of nuclear architecture at the molecular level, the mechanisms mediating the long-range reorganization processes remain unknown. Here, we suggest a new mathematical approach to understanding the dynamics of nuclear architecture, by which we found that the deformation of nucleus can play a critical role in the process of chromatin remodeling. With the interdisciplinary work, we succeeded in developing an in vitro experiment and found that the dynamical deformation of nucleus promotes the clustering of chromocenters. With the basis of theoretical observation, we prove that the deformation of nucleus is sufficient condition to induce the remodeling of chromatin architecture.
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11:40 - 12:10 Wei Lin / Adaptive elimination of synchronization in coupled neuronal oscillators
We present here an adaptive control scheme with a feedback delay to achieve elimination of synchronization in a large population of coupled and synchronized oscillators. We validate the feasibility of this scheme not only in the coupled Kuramoto’s oscillators with a unimodal or bimodal distribution of natural frequency, but also in two representative models of neuronal networks, viz., the FitzHugh-Nagumo spiking oscillators and the Hindmarsh-Rose bursting oscillators. More significantly, we analytically illustrate the feasibility of the proposed scheme with a feedback delay and reveal how the exact topological form of the bimodal natural frequency distribution influences the scheme performance. We anticipate that our developed scheme will deepen the understanding and refinement of those controllers, e.g. techniques of deep brain stimulation, which have been implemented in remedying some synchronization-induced mental disorders including Parkinson disease and epilepsy.
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12:10 - 14:00 Lunch
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14:00 - 14:40 Chao Tang / TBA
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14:40 - 15:20 Daehee Hwang / Systems approaches to complex human diseases
Living organisms execute their diverse functions by virtue of the operation of biological networks. Disease arises by genetic or environmental perturbations of these biological networks. A systems view of disease attempts to understand the initiation and progression of disease in terms of their initial disease-perturbations and their dynamic transitions as disease progresses. Systems approaches to diseases have two cardinal features: 1) global analyses to generate comprehensive data sets (e.g. how do all genes, mRNAs or proteins change upon perturbation or during transition) and 2) the integration of different levels of biological information (e.g. DNA, mRNA, protein, interactions, networks, tissues or organs, individuals, etc) to generate coherent hypotheses about health and disease. In this talk, I will present systems approaches used to understand perturbed networks in several diseases and to decode mechanisms underlying disease pathogenesis based on the networks. These approaches transform how one thinks about disease—explaining dynamic aspects of its pathophysiology and/or offering a new approach to diagnostics and therapeutics.
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15:20 - 15:40 Coffee break
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15:40 - 16:20 Santiago Schnell / Challenges in measuring kinetic parameter of enzyme catalyzed reaction
The conditions under which the Michaelis–Menten equation accurately captures the steady-state kinetics of a simple enzyme-catalyzed reaction is contrasted with the conditions under which the same equation can be used to estimate enzyme kinetic parameters from progress curve data. Validity of the underlying assumptions leading to the Michaelis–Menten equation are shown to be necessary, but not sufficient to guarantee accurate estimation of enzyme kinetic paramters. Detailed error analysis and numerical “experiments” show the required experimental conditions for the independent estimation of enzyme kinetic parameters from progress curves. A timescale for measuring the portion of the time course over which the progress curve exhibits substantial curvature provides a novel criterion for accurate estimation of kinetics parameters from a progress curve experiment.
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16:20 - 18:00 Short talk session
Talk Material 1: /
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TimeSpeaker/ Title/ Abstract/ VODLocation
08:30 - 09:00 Breakfast
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09:00 - 09:40 Ryo Kobayasi / Towards the Construction of Dialogical Control
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Conventional control theory has developed highly sophisticated framework by separating the system and the environment, in which the interactions between the system and the environment are treated as a “disturbance”. The theory has been made big successes in the areas where it is applicable. Typical examples are machines (including robots) working in the factories where environment is completely known. However, mobile robots are not the case. They encounter unknown environments as they move into new sites. In such situations, the interaction between the system and the environment can no more be regarded as disturbance. That means the system cannot be closed (like the conventional theory assumed), and we definitely need a new framework of control. The most promising approach seems to learn from the animals, because even the lower animals can make locomotion easily in the complex environment. Thus, collaboration between mathematical biology and robotics can be just the potential way for achieving our goal. I will introduce our challenge to construct a novel control principle for mobile robots.
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09:40 - 10:10 Lei Zhang / Rare event and transition state in complex biological systems
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The dynamics of complex biological systems is often driven by multiscale, rare but important events. Transition state theory provides a powerful mathematical tool to find the dynamic bottleneck and transition pathway. In this talk, I will apply three biological examples to show how rare event and transition state can help us understand the mechanisms and functions in biology, including stem cell differentiation, boundary sharpening in zebrafish hindbrain, and neuroblast delamination in Drosophila. The joint work with Qing Nie (UC Irvine), Yan Yan (HKUST), Chao Tang (PKU).
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10:10 - 10:40 Coffee Break
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10:40 - 11:10 Andrew Phillips / Using physiologically-based models to understand human sleep dynamics in the real world
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From laboratory studies, it is known that the human circadian clock is highly sensitive to environmental light patterns, including electric sources of light. Sleep timing in modern societies is also known to be very different from sleep timing in pre-industrial societies or modern societies that lack access to electric light. Using a mathematical model of human sleep and circadian physiology, we investigated how our modern patterns of light consumption affect the timing of sleep and circadian timing across the population. The model provides a quantitative framework for understanding how individual differences arise, why we experience social jet-lag, and how changes to our social constraints are predicted to affect sleep and circadian timing.
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11:10 - 11:40 Jinzhi Lei / Data driven single-cell based model reveals multiple pathways of inflammation-induced tumorigenesis
Chronic inflammation is a high-risk factor for tumorigenesis, how- ever the routes from inflammation to cancer are poorly understood. Here, driven by major pathways affected by high frequent mutant genes that are associated with inflammation and cancer, we presented a single-cell based computational model for long-term dynamics from inflammation to tumorigenesis. The model incorporates crosstalk among multi-scale interactions, including DNA damage response, gene mutation, cell behavior, population dynamics, inflammation, and metabolism-immune balance. Model simulation reveals two stages of inflammation-induced tumorigenesis: precancerous lesion and tumorigenesis. Precancerous lesion is mainly caused by mutations in the proliferation pathways; the transition from precancerous lesion to tumorigenesis is induced by mutations in the path- ways of cell apoptosis, differentiation, and metabolism-immune balance. We identified the two sides of inflammation in tumorigenesis: mild inflammation removes DNA damaged cell through DNA damage- induced cell death, while severe inflammation accelerates the accumulation of mutations and hence promote tumorigenesis. These results provide insights to the dynamics of inflammation-induced can- cer, and highlight the combination of inflammation and metabolism- immune imbalance in tumorigenesis. The approach establishes a modeling framework of quantifying the cancer risk and discovering the driver paths of mutations in inflammation-induced tumorigenesis, which are useful in guiding the validation of promising targets for therapeutic intervention.
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11:40 - 12:10 Masakazu Akiyama / A mathematical model of collective cell migration based on cell polarity
Individual cells migrate toward the direction of the polarity induced by interior or exterior factors. In situations without guides such as chemoattractants, cells migrate randomly. However, it is observed that cell aggregation may lead to systematic collective cell migration. For example, Dictyostelium discoideum and epithelial MDCK cells exhibit typical collective cell migration patterns such as uniformly directional migration and rotational migration. In particular, it has been observed from experimental investigations that rotational migrations are intimately related to the morphogenesis of organized cells and tissues in several species. Thus, it has been suggested that collective cell migrations are controlled by a universal mechanism of cells. In this talk, we review the actual experimental data related to collective cell migrations on dishes and show that our self-propelled particle model based on the cell polarity can accurately represent the observed migration behaviors. Furthermore, we show that the collective cell migration modes observed in our model are robust.
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12:10 - 14:00 Lunch
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14:00 - 14:30 Ramon Grima / Mean field theory of diffusion in heterogeneous intracellular crowded conditions
It is now well established that cell interiors are significantly crowded by macromolecules, which impede diffusion and enhance binding rates. However, it is not fully appreciated that levels of crowding are heterogeneous, and can vary substantially between subcellular regions. Starting from a stochastic microscopic model, we derive coupled nonlinear PDEs for the concentrations of two populations of large and small spherical particles with steric volume exclusion. By performing a perturbative expansion in the ratio of the particle sizes, we find that the diffusion of a small particle in the presence of large particles obeys an advection-diffusion equation, with a reduced diffusion coefficient and a velocity directed towards less crowded regions. The interplay between advection and diffusion leads to behaviour that differs significantly from Brownian diffusion. We show that biologically plausible distributions of macromolecules can lead to (i) highly non-Gaussian probability densities for the small particle position, including asymmetrical and multimodal densities, (ii) both sub- and super-diffusion for short times. We confirm all our results using hard-sphere Brownian dynamics simulations.
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14:30 - 15:00 Jaeyoung Sung / Chemical fluctuation theorem for intracellular reaction networks
Conventional control theory has developed highly sophisticated framework by separating the system and the environment, in which the interactions between the system and the environment are treated as a “disturbance”. The theory has been made big successes in the areas where it is applicable. Typical examples are machines (including robots) working in the factories where environment is completely known. However, mobile robots are not the case. They encounter unknown environments as they move into new sites. In such situations, the interaction between the system and the environment can no more be regarded as disturbance. That means the system cannot be closed (like the conventional theory assumed), and we definitely need a new framework of control. The most promising approach seems to learn from the animals, because even the lower animals can make locomotion easily in the complex environment. Thus, collaboration between mathematical biology and robotics can be just the potential way for achieving our goal. I will introduce our challenge to construct a novel control principle for mobile robots.
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15:00 - 15:30 Chunhe Li / Landscape and path of gene networks
Cellular functions in biological systems are regulated by the underlying gene regulatory networks. How to investigate the global properties of gene networks is a challenging problem. In this talk, I will present some approaches we recently developed, i.e. the potential landscape and path framework, to study the stochastic dynamics of gene networks. The basins on the landscape characterize different cell states. The landscape topography in terms of barrier heights between stable states quantifies the global stability of the gene regulatory system. The kinetic paths quantify the transition processes between different cell states. I will also discuss some applications of this approach in the biological systems, including cancer, cell cycle, and stem cell differentiation.
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15:30 - 16:00 Coffee Break
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16:00 - 16:30 Young (Yang) Cao / Hybrid stochastic model and simulation of the budding yeast cell cycle control mechanism
The budding yeast cell cycle is regulated by a complex chemical reaction network. Several deterministic models have been proposed to model this control mechanism. However, experimental data exhibit considerable variability from cell to cell during cell growth and division. The observed variability comes from two sources: intrinsic noise coming from fluctuations of molecules and extrinsic noise introduced by variations in the division process. As a result, molecular fluctuations cannot be neglected, and they may significantly affect the behavior of a cell. To accurately model the cell cycle control mechanism, stochastic models are needed. A rigorous solution to this problem is to convert a deterministic model to its stochastic equivalent and apply Gillespie's stochastic simulation algorithm (SSA). But the conversion process is not straightforward and often results in a much larger system. Moreover, the high computational cost of Gillespie's algorithm make it difficult to simulate a practical budding yeast cell cycle model. Here we present a hybrid (ODE/SSA) stochastic model for the budding yeast cell cycle. Based on error analysis for multiscale systems and the observation that fluctuations of mRNAs are the primary sources of noise, in this hybrid model the dynamics of mRNAs are simulated by Gillespie's algorithm, while the cell cycle mechanism at the protein level is modeled by ordinary differential equations (ODEs). Numerical experiments, implemented by Haseltine and Rawlings' hybrid simulation algorithm, demonstrate that the hybrid model matches very well with biological experimental data for both wild-type cells and mutant cells in different nutrient media.
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16:30 - 17:00 Yong Jung Kim / Lotka-Volterra equations with finite time extinction
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Most of population models, if not all of them, do not have a finite time extinction property. The Lotka-Volterra ordinary differential equations are such cases and solutions never become zero if they are not initially zero. Their PDE extensions also have the same property and solutions are always strictly positive everywhere. However, the population becomes extinct locally in space always and the survival of a species is a global phenomenon, but not a local one. The spatial pattern of biological organisms reflects its history of extinction and growth. In this paper Lotka-Volterra equations equipped with extinction dynamics are introduced. We will see beautiful patterns of life generated by the newly added extinction dynamics.
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17:00 - 17:30 Jae Kyoung Kim / When can we use the Michales-Menten function for the stochastic simulations?
The quasi steady-state approximation (QSSA) is frequently used to reduce deterministic models of biochemical networks. The resulting equations provide a simplified description of the network in terms of non-elementary reaction functions (e.g. Hill functions). Such deterministic reductions are frequently a basis for heuristic stochastic models in which non-elementary reaction functions are used as propensities of Gillespie algorithm. Despite the popularity of this heuristic stochastic simualtions, it remains unclear when such stochastic reductions are valid. In this talk, I will present conditions under which the stochastic models with the non-elementary propensity functions accurately approximate the full stochastic models. If the validity condition is satisfied, we can perform accurate and computationally inexpensive stochastic simulation without converting the non-elementary functions to the elementary functions (e.g. mass action kinetics).
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TimeSpeaker/ Title/ Abstract/ VODLocation
08:30 - 09:00 Breakfast
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09:00 - 09:30 Eunok Jung / Mathematical Models of Emerging Infectious Diseases in the Republic of Korea
Emerging infectious diseases have long been recognized as a continuous, inevitable, unpredictable threat to the global public health. Hence, understanding the underlying dynamics why they spread and what causes epidemics give key ideas of intervention strategies. in this talk, we will present the development of new mathematical models for the spread of two emerging infectious diseases in the Republic of Korea, 2009 A/H1N1 pandemic and 2015 Middle East respiratory syndrome outbreak, and the effects of public health intervention in the early stage of the outbreaks. Using the laboratory-confirmed case data, the spreading dynamics of transmission is investigated. Results in this work suggest that heterogeneity plays a key role in the spread of two emerging infectious diseases in the Republic of Korea. Our findings show that interventions in the early stage of the outbreak could reduce the epidemic size up to 19% for the 2009 pandemic influenza, and up to 80% for the 2015 MERS outbreak.
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09:30 - 10:00 Shingo Iwami / Quantification of virus infection in cell culture
Current in vitro cell culture studies of viral replication deliver detailed time courses of several virological variables, like the amount of virions and the number of target cells, measured over several days of the experiment. Each of these time points solely provides a snap-shot of the virus infection kinetics and is brought about by the complex interplay of target cell infection, and viral production and cell death. It remains a challenge to interpret these data quantitatively and to reveal the kinetics of these underlying processes to understand how the viral infection depends on these kinetic properties. In order to decompose the kinetics of virus infection, we introduce a method to “quantitatively” describe the virus infection in in vitro cell cultures, and discuss the potential of the mathematical based analyses for experimental virology.
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10:00 - 10:30 Chang Hyeong Lee / Mathematical modeling and computation of animal infectious diseases in Korea
Recently there has been much economic loss in Korea due to the spread of the animal infectious diseases such as avian influenza (AI), and foot-and-mouth disease (FMD). The outbreak of such infectious diseases may occur more frequently and severely by recent environmental changes in Korea. In this talk, we present the mathematical modeling and computation methods for describing the spread of the animal infectious diseases in Korea.
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10:30 - 11:00 Coffee Break
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11:00 - 11:30 Junghyo Jo / Design principles of pancreatic islets for glucose homeostasis
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Counter-regulatory elements maintain dynamic equilibrium ubiquitously in living systems. The most prominent example, which is critical to mammalian survival, is that of pancreatic α and β cells producing glucagon and insulin for glucose homeostasis. These cells are not found in a single gland but are dispersed in multiple micro-organs known as the islets of Langerhans. Within an islet, these two reciprocal cell types interact with each other and with an additional cell type: the δ cell. By testing all possible motifs governing the interactions of these three cell types, we found that a unique set of positive/negative intra-islet interactions between different islet cell types functions not only to reduce the superficially wasteful zero-sum action of glucagon and insulin but also to enhance/suppress the synchronization of hormone secretions between islets under high/normal glucose conditions. This anti-symmetric interaction motif confers effective controllability for network (de)synchronization.
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11:30 - 12:10 Marty Golubitsky / A Singularity Theory Approach to Homeostasis
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Homeostasis occurs when some output variable remains approximately constant as input parameters vary over some intervals. We formulate homeostasis in the context of singularity theory by replacing `approximately constant over an interval' with `zero derivative of the output with respect to inputs at a point'. The `chair' singularity has been shown by Best, Nijhout, and Reed to be especially important in applications is discussed in detail. We explain why the hyperbolic umbilic can also be expected to be important in homeostasis with two inputs.
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