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Cancer Systems Biology

Translational Mathematical Oncology

9780192867636
1 088,10 zł
979,29 zł Zniżka 108,81 zł Brutto
Najniższa cena w okresie 30 dni przed promocją: 979,29 zł
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Opis
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 distinctsubpopulations, 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 andprogresses. 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.
Szczegóły produktu
OUP Oxford
104079
9780192867636
9780192867636

Opis

Rok wydania
2025
Numer wydania
1
Oprawa
twarda
Liczba stron
472
Wymiary (mm)
219 x 276
  • Section 1 - Cancer systems biology: An overview; The necessary existence of cancer and its progression from first principles of cell state dynamics; Non- genetic intratumoral heterogeneity and phenotypic plasticity as consequences of microenvironment- driven epigenomic dysregulation; Dimensions of cellular plasticity: Epithelial– mesenchymal transition, cancer stem cells, and collective cell migration; Phenotypic switching in cancer: A systems- level perspective; Morphological state transition during epithelial– mesenchymal transition; Section 2 - Cancer systems biology: New paradigms; Evolution- informed multilayer networks: Overlaying comparative evolutionary genomics with systems- level analyses for cancer drug discovery; Landscape of cell- fate decisions in cancer cell plasticity; The road to cancer and back: A thermodynamic point of view; Cellular plasticity as emerging target against dynamic complexity in cancer; Modeling phenotypic heterogeneity and cell- state transitions during cancer progression; Section 3 - Single cell omics analysis; Decoding drug resistance at a single- cell level using systems- level approaches; Computational methods to infer lineage decision- making in cancer using single-cell data; Analyzing cancer cell- state transition dynamics through live- cell imaging and high- dimensional single-cell trajectory analyses; Emerging single- cell technologies and concepts to trace cancer progression and drug resistance; Section 4 - Computational approaches to drug development; Navigating protein dynamics: Bridging the gap with deep learning and machine intelligence; Cancer- related intrinsically disordered proteins: Functional insights from energy landscape analysis; Targeting RAS; Section 5 - Statistical methods and data mining, machine learning, artificial intelligence, and cloud computing; The power of connection-enabling collaborative, multimodal data analysis at petabyte scale to advance understanding of oncology; Interpretation of machine learning models in cancer: The role of model- agnostic explainable artificial intelligence; Applying cloud computing and informatics in cancer; Single-cell sequencing analysis focused on cancer immunotherapy; Application of artificial intelligence to overcome clinical information overload in cancer; Application of artificial intelligence in cancer genomics; Section 6 - Biomechanics; A role for mechanical heterogeneity in the tumor microenvironment in driving cancer cell invasion; Adaptation of cancer cells to altered stiffness of the extra-cellular matrix; Decoding mechano- oncology principles through microfluidic devices and biomaterial platforms; Understanding contribution of fibroblasts in inception of cancer metastasis from an evolutionary perspective; Cell competition in tumorigenesis and epithelial defense against cancer; Section 7 - Translational mathematical oncology; Modelling cell population dynamics during chimeric antigen receptor T- cell therapy; Modeling small cell lung cancer biology through deterministic and stochastic mathematical models; Mathematical models of resistance evolution under continuous and pulsed anti- cancer therapies; Integrating in silico models with ex vivo data for designing better combinatorial therapies in cancer; Tumour- immune co- evolution dynamics and its impact on immuno- therapy optimization; Mechanistic modelling and machine learning to establish structure– activity relationship of nanomaterials for improved tumour delivery; Section 8 - Ecology, evolution, and cancer; Decoding cancer evolution through adaptive fitness landscapes; A case against causal reductionism in acquired therapy resistance; Group behaviour and drug resistance in cancer; The Fundamentals of evolutionary therapy in cancer; Section 9 - Critical transitions and chaos in cancer; Methods for identifying critical transitions during cancer progression; Chaos and complexity: Hallmarks of cancer progression; Cancer formation as creation and penetration of unknown life spaces;
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