Registered Data

[00114] Computational Biology

  • Session Time & Room :
    • 00114 (1/3) : 3E (Aug.23, 17:40-19:20) @A508
    • 00114 (2/3) : 4C (Aug.24, 13:20-15:00) @A508
    • 00114 (3/3) : 4D (Aug.24, 15:30-17:10) @A508
  • Type : Proposal of Minisymposium
  • Abstract : Besides the traditional (experimental) and theoretical biology, computational biology is the third biology. Its mission is to visualize the activities of living things on the screen to understand their backgrounds theoretically and to predict future status for applications. For this purpose, experimental, data, and simulation sciences are applied, but mathematical formulae are obviously necessary. Computational biology is now widely spreading as a new challenge of industrial and applied mathematics. This minisymposium focuses on recent developments in computational biology.
  • Organizer(s) : Takashi Suzuki
  • Classification : 92-08, 92-10
  • Minisymposium Program :
    • 00114 (1/3) : 3E @A508 [Chair: Takashi Suzuki]
      • [05357] Different mathematical models for membrane electroporation: from equivalent circuit to phase-field models
        • Format : Talk at Waseda University
        • Author(s) :
          • Clair Poignard (INRIA Center of Bordeaux Univ.)
        • Abstract : Electroporation is a complex phenomenon consisting of defects creation in membranes subjected to high short electric pulses. The aim of the talk is to present the mathematical challenges in terms of PDEs and numerical analysis of the phenomenon. A comparison from the 90’s biophysical models based on Deryagin theory to very recent phase-field model are performed. For each approach, advantages and disadvantages are discussed, in terms of physical meaning and validation with the experimental data.
      • [02031] Modeling and characterizing vaccine-elicited antibody responses
        • Format : Talk at Waseda University
        • Author(s) :
          • Shingo Iwami (Nagoya University)
        • Abstract : Recent studies have provided insights into the effect of vaccine boosters on recall immunity. Given a limited global supply of the vaccinations, identifying vulnerable populations with lower sustained vaccine-elicited antibody titers is important to decide target individuals for the booster. Here we investigated longitudinal data among the cohort of the same individuals of 2,526 people in Fukushima, Japan. Antibody titers following a two-dose SARS-CoV-2 vaccination were repeatedly monitored along with the information on lifestyle habits, comorbidities, adverse reactions, and medication. By employing mathematical modeling and machine learning, we characterized the elicited immune response following a two-dose SARS-CoV-2 vaccination.
      • [03370] Mathematical analysis of bone metabolism markers in immobilization mice
        • Format : Talk at Waseda University
        • Author(s) :
          • Marwa Akao (Nagoya University)
        • Abstract : Osteoporosis is a disease that affects more than 200 million people around the world. Although the mechanisms are gradually being revealed, osteoporosis cannot really be cured completely. This study aims to develop the most effective prevention for osteoporosis. We developed mathematical models describing interactions with cells related to bone metabolism and changes in bone mass. And we analyzed the data of bone metabolism markers and compared the difference between immobilization mice and normally fed mice.
      • [02284] Predicting clinical outcomes of acute liver failure
        • Format : Online Talk on Zoom
        • Author(s) :
          • Raiki Yoshimura (Division of Natural Science, Graduate School of Science, Nagoya University)
        • Abstract : We used clinical data on acute liver failure to develop an approach for predicting its clinical outcomes. Specifically, we employed a supervised machine learning approach to analyze clinical datasets, including blood test data and medication history, at the admission to the hospital, and predicted final state, i.e., survived or died. In addition, we developed a scoring system to predict individual clinical outcomes. The findings of this study are expected to be utilized in actual clinical practice as a basis for initial response decisions and may also be applied to the detection of other signs of acute diseases.
    • 00114 (2/3) : 4C @A508 [Chair: Isamu Doku]
      • [03648] Mathematical investigation into the mechanism of hair follicle morphogenesis
        • Format : Talk at Waseda University
        • Author(s) :
          • Masaharu Nagayama (Hokkaido University)
          • Makoto Okumura (Konan University)
          • Yasuaki Kobayashi (Hokkaidio Universiy)
          • Hironobu Fujiwara ( Institute of Physical and Chemical Research)
        • Abstract : Long-term 3D live imaging of hair follicle morphogenesis during development was shown by Fujiwara et al. During hair follicle morphogenesis, basal cells, basement membrane, and mesenchyme were found to undergo dynamic changes. Fujiwara et al. proposed a telescopic model of hair follicle formation based on these results. In this study, to realize this telescope model, we will construct a mathematical model that reproduces 3D cylindrical compartments and investigate by what mechanism the cylindrical compartments are actively formed.
      • [03506] Parameter estimation of the compartmental model of systemic circulation describing the Glucose, Insulin and C peptide dynamics
        • Format : Talk at Waseda University
        • Author(s) :
          • Yueyuan Gao (Hokkaido University)
          • Hiroshi Suito (Tohoku University)
          • Hayato Chiba (Tohoku University)
          • Masaharu Nagayama (Hokkaido University)
          • Hideki Katagiri (Tohoku University)
        • Abstract : In this talk, we explain the construction of the mathematical compartmental model of systemic circulation describing the Glucose, Insulin and C peptide dynamics and we present the application of Markov chain Monte Carlo method to estimate the parameters of the model from clinical data.
      • [02898] Effective nonlocal kernels on Reaction-diffusion networks
        • Format : Talk at Waseda University
        • Author(s) :
          • Shin-Ichiro Ei (Hokkaido University)
        • Abstract : A new method to derive an essential integral kernel from any given reaction-diffusion network is proposed. Any network describing metabolites or signals with arbitrary many factors can be reduced to a single or a simpler system of integro-differential equations called ‘‘effective equation’’ in the convolution type.
      • [05537] Application of stochastic analysis to neuronal model dynamics
        • Author(s) :
          • Takashi Suzuki (Osaka University)
          • Yasushi Ishikawa (Ehime University)
          • Takanobu Yamanobe (Hokkaido University)
        • Abstract : This talk is an introduction to the theory of an asymptotic expansion of the transition density of a nonlinear oscillator involving a jump and diffusion process. In particular, we introduce a procedure based on stochastic calculus of variations for jumps and diffusion processes. The study provides a new mathematical framework for analyzing the dynamics of stochastic mathematical neuronal models using jump and diffusion processes. The essential tools are the asymptotic expansion of characteristic functions associated with stochastic models with fine properties. Here, we consider no boundary condition. Computational analyses show that our method could be applied to other jump-diffusions.
    • 00114 (3/3) : 4D @A508 [Chair: Takashi Suzuki]
      • [04349] Mathematical model of mTORC1 pathway sensing intracellular amino-acids and glucose
        • Format : Talk at Waseda University
        • Author(s) :
          • Takanori Nakamura (Ehime University)
          • Shigeyuki Nada (Osaka University)
          • Takashi Suzuki (Osaka University)
          • Masato Okada (Osaka University)
        • Abstract : mTORC1, a master regulator of metabolism, is activated upon Insulin and Amino-acids (AA) addition, but its regulatory mechanism is not fully understood. We therefore constructed an integrated mathematical model of mTORC1 regulation through the two distinct AA- and Insulin-sensing axes. Using the mathematical simulation with experimental data, we found the selective dephosphorylation during AA deprivation, which ensures full mTORC1 activation only upon the concurrently sensing of nutrient Insulin and AA.
      • [05379] Mathematical modeling of cancer immune escape
        • Format : Talk at Waseda University
        • Author(s) :
          • Hiroshi Haeno (Tokyo University of Science)
          • Koichi Saeki (Tokyo University of Science)
        • Abstract : A tumor evolves under the pressure of immune responses. Immune checkpoint inhibitors (ICIs) are expected to reactivate antitumor immunity and inhibit tumor progression. Here, we developed a mathematical model of the tumor evolution under immune responses. As a result, we confirmed that patients who had high mutational load were likely to have a durable benefit. Moreover, we found that the growth rate of tumor cells would be informative to identify responders to ICIs.
      • [02179] Computational modelling of cancer invasion using organotypic invasion assay data
        • Format : Talk at Waseda University
        • Author(s) :
          • Mark Chaplain (University of St Andrews)
          • Nikoloas Sfakianakis (University of St Andrews)
          • Linnea Franssen (Roche, Basel)
        • Abstract : We present computational simulation results from a three-dimensional hybrid atomistic-continuum model that describes the invasive growth dynamics of individual cancer cells in tissue. The framework explicitly accounts for phenotypic variation by distinguishing between cancer cells of an epithelial-like and a mesenchymal-like phenotype. It also describes mutations between these cell phenotypes. The full model consists of a hybrid system of partial and stochastic differential equations describing the evolution of cancer cells, extracellular matrix and matrix-degrading enzymes.
      • [05467] The prognostic value of immune infiltration patterns on the outcome of chemotherapy in breast cancer
        • Format : Online Talk on Zoom
        • Author(s) :
          • Nikolaos Ioannis Kavallaris (Karlstad University)
        • Abstract : In this work, based on a breast cancer biopsy dataset, taken from the ADAPT clinical trial, we shed light on the changes of tumor microenvironment after cytostatic chemotherapy. We combine machine learning workflow to identify cell density patterns identifying responders and non-responders. We also develop a dynamic model that allows us to elucidate the reasons of therapy failure. Finally, using our model we can reason on therapy combinations that could improve the therapeutic outcomes.