- ホーム
- > 電子洋書
Description
Over the centuries, civilization has seen considerable advances in healthcare. Cancer is among the most challenging healthcare issues that we face today, but a number of discoveries have led to better care. Despite all the progress and the promise regarding early detection and precision medicine, we are still faced with the nettlesome problem - cancer is a moving target. Even within an individual tumour, deep sequencing analyses now indicate multiple, phenotypically distinct subpopulations, whose representation seems to vary dramatically from one stage to the next as the tumour progresses.Cancer Systems Biology provides state-of-the-art reviews and thought-provoking ideas in a concise and succinct manner. This insightful textbook is a crosspollination of concepts from multiple disciplines and experimental approaches to study cancer. The chapters provide new ideas and thoughts outlining how a quantitative picture of cancer can provide a deeper understanding of the disease, and how a systems level perspective may hold the key to fully comprehend how cancer arises and progresses. Written by experts in multiple disciplines, including systems biologists, science researchers, physicists, mathematicians, and clinicians, Cancer Systems Biology provides a comprehensive, up-to-date, treatise devoted to understanding cancer from a systems perspective. Providing new conceptual insights that can aid precision medicine, it will be essential reading for academic researchers in the field, clinicians, graduate students, and scientists with an interest in cancer biology.
Table of Contents
- Section 1 - Cancer systems biology: An overview
- 1: Sui Huang: The necessary existence of cancer and its progression from first principles of cell state dynamics
- 2: Vera Pancaldi and Jean- Pascal Capp: Non- genetic intratumoral heterogeneity and phenotypic plasticity as consequences of microenvironment- driven epigenomic dysregulation
- 3: Caterina A.M. La Porta and Stefano Zapperi: Dimensions of cellular plasticity: Epithelial– mesenchymal transition, cancer stem cells, and collective cell migration
- 4: Divyjoy Singh, Abhay Gupta, Mohit Kumar Jolly, and Prakash Kulkarni: Phenotypic switching in cancer: A systems- level perspective
- 5: Biplab Bose: Morphological state transition during epithelial– mesenchymal transition
- Section 2 - Cancer systems biology: New paradigms
- 6: Laurie Graves, Ayalur Raghu Subbalakshmi, William C. Eward, Mohit Kumar Jolly, and Jason A. Somarelli: Evolution- informed multilayer networks: Overlaying comparative evolutionary genomics with systems- level analyses for cancer drug discovery
- 7: Jintong Lang, Chunhe Li, and Jinzhi Lei: Landscape of cell- fate decisions in cancer cell plasticity
- 8: Arnab Barua and Haralampos Hatzikirou: The road to cancer and back: A thermodynamic point of view
- 9: Paromita Mitra, Uday Saha, Subhashis Ghosh, and Sandeep Singh: Cellular plasticity as emerging target against dynamic complexity in cancer
- 10: Vishaka Gopalan, Sidhartha Goyal, and Sumaiyah Rehman: Modeling phenotypic heterogeneity and cell- state transitions during cancer progression
- Section 3 - Single cell 'omics' analysis
- 11: Benedict Anchang and Loukia G. Karacosta: Decoding drug resistance at a single- cell level using systems- level approaches
- 12: Manu Setty: Computational methods to infer lineage decision- making in cancer using single-cell data
- 13: Jianhua Xing and Weikang Wang: Analyzing cancer cell- state transition dynamics through live- cell imaging and high- dimensional single-cell trajectory analyses
- 14: Syeda Subia Ahmed, Danielle Pi, Nicholas Bodkin, Vito W. Rebecca, and Yogesh Goyal: Emerging single- cell technologies and concepts to trace cancer progression and drug resistance
- Section 4 - Computational approaches to drug development
- 15: Supriyo Bhattacharya: Navigating protein dynamics: Bridging the gap with deep learning and machine intelligence
- 16: Vitor B.P. Leite, Murilo N. Sanches, and Rafael G. Viegas: Cancer- related intrinsically disordered proteins: Functional insights from energy landscape analysis
- 17: Priyanka Prakash: Targeting RAS
- Section 5 - Statistical methods and data mining, machine learning, artificial intelligence, and cloud computing
- 18: Brandi N. Davis- Dusenbery, Cera R. Fisher, Rowan Beck, and Zelia F. Worman: The power of connection—enabling collaborative, multimodal data analysis at petabyte scale to advance understanding of oncology
- 19: Colton Ladbury and Arya Amini: Interpretation of machine learning models in cancer: The role of model- agnostic explainable artificial intelligence
- 20: Jay G. Ronquillo: Applying cloud computing and informatics in cancer
- 21: Luciane T. Kagohara and Joseph Tandurella: Single-cell sequencing analysis focused on cancer immunotherapy
- 22: Arnulf Stenzl, Jenny Ghith, and Bob J.A. Schijvenaars: Application of artificial intelligence to overcome clinical information overload in cancer
- 23: Xiwei Wu and Supriyo Bhattacharya: Application of artificial intelligence in cancer genomics
- Section 6 - Biomechanics
- 24: Madhurima Sarkar, Asadullah, and Shamik Sen: A role for mechanical heterogeneity in the tumor microenvironment in driving cancer cell invasion
- 25: Christina R. Dollahon, Ting- Ching Wang, Srinikhil S. Vemuri, Suchitaa Sawhney, and Tanmay P. Lele: Adaptation of cancer cells to altered stiffness of the extra-cellular matrix
- 26: Ajay Tijore, Alka Kumari, and Abhishek Goswami: Decoding mechano- oncology principles through microfluidic devices and biomaterial platforms
- 27: Yasir Suhail, Wenqiang Du, Günter Wagner, and Kshitiz: Understanding contribution of fibroblasts in inception of cancer metastasis from an evolutionary perspective
- 28: Medhavi Vishwakarma and Amrapali Datta: Cell competition in tumorigenesis and epithelial defense against cancer
- Section 7 - Translational mathematical oncology
- 29: Philipp M. Altrock, Guranda Chitadze, Arne Traulsen, and Frederick L. Locke: Modelling cell population dynamics during chimeric antigen receptor T- cell therapy
- 30: Srisairam Achuthan, Rishov Chatterjee, and Atish Mohanty: Modeling small cell lung cancer biology through deterministic and stochastic mathematical models
- 31: Jasmine Foo and Einar Bjarki Gunnarsson: Mathematical models of resistance evolution under continuous and pulsed anti- cancer therapies
- 32: Mohammad Kohandel, Cameron Meaney, and Dorsa Mohammadrezaei: Integrating in silico models with ex vivo data for designing better combinatorial therapies in cancer
- 33: Annice Najafi and Jason George: Tumour- immune co- evolution dynamics and it's impact on immuno- therapy optimization
- 34: Maria Jose Peláez, Shreya Goel, Vittorio Cristini, Zhihui Wang, and Prashant Dogra: Mechanistic modelling and machine learning to establish structure– activity relationship of nanomaterials for improved tumour delivery
- Section 8 - Ecology, evolution, and cancer
- 35: Rowan Barker- Clarke, Eshan S. King, Jeff Maltas, J. Arvid Ågren, Dagim Tadele, and Jacob G. Scott: Decoding cancer evolution through adaptive fitness landscapes
- 36: Andriy Marusyk: A case against causal reductionism in acquired therapy resistance
- 37: Ravi Salgia, Supriyo Bhattacharya, Atish Mohanty, and Govindan Rangarajan: Group behaviour and drug resistance in cancer
- 38: Jeffrey West, Jill Gallaher, Maximilian A.R. Strobl, Mark Robertson- Tessi, and Alexander R.A. Anderson: The Fundamentals of evolutionary therapy in cancer
- Section 9 - Critical transitions and chaos in cancer
- 39: Smita Deb, Subhendu Bhandary, Mohit Kumar Jolly, and Partha Sharathi Dutta: Methods for identifying critical transitions during cancer progression
- 40: Abicumaran Uthamacumaran: Chaos and complexity: Hallmarks of cancer progression
- 41: Andrzej Kasperski and Henry H. Heng: Cancer formation as creation and penetration of unknown life spaces
