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  • Produktbild: Methods of Mathematical Oncology
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Methods of Mathematical Oncology Fusion of Mathematics and Biology, Osaka, Japan, October 26–28, 2020

Aus der Reihe Springer Proceedings in Mathematics & Statistics
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173,99 € UVP 197,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.08.2022

Herausgeber

Takashi Suzuki + weitere

Verlag

Springer Singapore

Seitenzahl

308

Maße (L/B/H)

23,5/15,5/1,8 cm

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-981-16-4868-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.08.2022

Herausgeber

Verlag

Springer Singapore

Seitenzahl

308

Maße (L/B/H)

23,5/15,5/1,8 cm

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-981-16-4868-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Methods of Mathematical Oncology
  • Produktbild: Methods of Mathematical Oncology
  • PART 1: Mathematical Modeling: D. Guan, X. Luo, and H. Gao, Constitutive Modelling of Soft Biological Tissue from Ex Vivo to In Vivo: Myocardium as an Example.-  T. Colin, T. Michel, and C. Poignard, Mathematical Modeling of Gastro-intestinal Metastasis Resistance to Tyrosine Kinase Inhibitors.- Y. Tanaka and T. Yasugi, Mathematical Modeling and Experimental Verification of the Proneural Wave.- D. Kumakura and S. Nakaoka, Exploring Similarity between Embedding Dimension of Time-series Data and Flows of an Ecological Population Model.- T. Hayashi, Mathematical Modeling for Angiogenesis.- S. Collin, Corridore and C. Poignard, Floating Potential Boundary Condition in Smooth Domains in an Electroporation  Context.- N. L. Othman and T. Suzuki, Free Boundary Problem of Cell Deformation and Invasion.- L. Preziosi and M. Scianna, Multi-level Mathematical Models for Cell Migration in Confined Environments.- S. Magi, Mathematical Modeling of Cancer Signaling Addressing Tumor Heterogeneity.- N. Sfakianakis and Mark A.J. Chaplain, Mathematical Modelling of Cancer Invasion: A Review.- T. Williams, A. Wilson, and N. Sfakianakis, The First Step towards the Mathematical Understanding of the Role of Matrix Metalloproteinase-8 in Cancer Invasion.- PART II: Biological Prediction: T. Ito, T. Suzuki, and Y. Murakami, Mathematical Modeling of the Dimerization of EGFR and ErbB3 in Lung Adenocarcinoma.- H. Kubota, Selective Regulation of the Insulin-Akt Pathway by Simultaneous Processing of Blood Insulin Pattern in the Liver.- D. Oikawa, N. Hatanaka, T. Suzuki, and F. Tokunaga, Mathematical Simulation of Linear Ubiquitination in T Cell Receptor-mediated NF-κB Activation Pathway.- Y. Ito, D. Minerva, S. Tasaki, M. Yoshida, T. Suzuki, and A. Goto, Time Changes in the VEGF-A Concentration Gradient Lead Neovasculature to Engage in Stair-like Growth.- N. Hatanaka, M. Futakuchi, and T. Suzuki, Mathematical Modeling of Tumor Malignancy in Bone Microenvironment.- M. Yamamoto and Jun-ichiro Inoue, Signaling Networks Involved in the Malignant Transformation of Breast Cancer.- PART III: Data Science: R. Morishita, H. Takahashi, and T. Sawasaki, Cell-free Based Protein Array Technology.- Y. Nojima and Y. Takeda, Omics Data Analysis Tools for Biomarker Discovery and the Tutorial.- M. Oyama and H. Kozuka-Hata, Integrative Network Analysis of Cancer Cell Signaling by High-resolution Proteomics.- N. Nakamura and R. Yamada, Distance-matrix-based Extraction of Motility Features from Functionally Heterogeneous Cell Populations.- S. Kawasaki, H. Hayashi, and Y. Tominaga, Data Analytic Study of Genetic Mechanism of Ovarian Carcinoma from Single Cell RNA-seq Data.