This book presents original papers reflecting topics featured at the international symposium entitled “Fusion of Mathematics and Biology” and organized by the editor of the book. The symposium, held in October 2020 at Osaka University in Japan, was the core event for the final year of the research project entitled “Establishing International Research Networks of Mathematical Oncology.” The project had been carried out since April 2015 as part of the Core-to-Core Program of Japan Society for the Promotion of Science (JSPS). In this book, the editor presents collaborative research from prestigious organizations in France, the UK, and the USA. By utilizing their individual strengths and realizing the fusion of life science and mathematical science, the project achieved a combination of mathematical analysis, verification by biomedical experiments, and statistical analysis of chemical databases.
Mathematics is sometimes regarded as a universal language. It is a valuable property that everyone can understand beyond the boundaries of culture, religion, and language. This unifying force of mathematics also applies to the various fields of science. Mathematical oncology has two aspects, i.e., data science and mathematical modeling, and definitely helps in the prediction and control of biological phenomena observed in cancer evolution.
The topics addressed in this book represent several methods of applying mathematical modeling to scientific problems in the natural sciences. Furthermore, novel reviews are included that may motivate many mathematicians to become interested in biological research.
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-B 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.
