07369nam 2200553 450 99646641730331620231110233213.03-030-93302-4(MiAaPQ)EBC6941414(Au-PeEL)EBL6941414(CKB)21435612700041(PPN)261518852(EXLCZ)992143561270004120221113d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierActive particlesVolume 3 advances in theory, models, and applications /Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editorsCham, Switzerland :Springer International Publishing,[2022]©20221 online resource (230 pages)Modeling and Simulation in Science, Engineering and Technology Print version: Bellomo, Nicola Active Particles, Volume 3 Cham : Springer International Publishing AG,c2022 9783030933012 Intro -- Preface -- Contents -- Variability and Heterogeneity in Natural Swarms: Experiments and Modeling -- 1 Introduction -- 2 Sources of Variability in Nature -- 2.1 Development as a Source of Variation -- 2.2 Transient Changes in the Behavior of Individuals -- 2.3 Environmentally Induced Variations -- 2.4 Social Structure -- 2.5 Inherent/Intrinsic Properties and Animal Personality -- 2.6 Variability in Microorganisms -- 3 Experiments with Heterogeneous Swarms -- 3.1 Fish -- 3.2 Mammals -- 3.3 Insects -- 3.4 Microorganisms -- 4 Modeling Heterogeneous Collective Motion -- 4.1 Continuous Models -- 4.2 Agent-Based Models -- 4.3 Specific Examples: Locust -- 4.4 Specific Examples: Microorganisms and Cells -- 5 Summary and Concluding Remarks -- References -- Active Crowds -- 1 Introduction -- 2 Models for Active Particles -- 2.1 Continuous Random Walks -- 2.1.1 Excluded-Volume Interactions -- 2.2 Discrete Random Walks -- 2.3 Hybrid Random Walks -- 3 Models for Externally Activated Particles -- 3.1 Continuous Models -- 3.2 Discrete Models -- 4 General Model Structure -- 4.1 Wasserstein Gradient Flows -- 4.2 Entropy Dissipation -- 5 Boundary Effects -- 5.1 Mass Conserving Boundary Conditions -- 5.2 Flux Boundary Conditions -- 5.3 Other Boundary Conditions -- 6 Active Crowds in the Life and Social Science -- 6.1 Pedestrian Dynamics -- 6.2 Transport in Biological Systems -- 7 Numerical Simulations -- 7.1 One Spatial Dimension -- 7.2 Two Spatial Dimensions -- References -- Mathematical Modeling of Cell Collective Motion Triggered by Self-Generated Gradients -- 1 Introduction -- 2 The Keller-Segel Model and Variations -- 2.1 The Construction of Waves by Keller and Segel -- 2.2 Positivity and Stability Issues -- 2.3 Variations on the Keller-Segel Model -- 2.4 Beyond the Keller-Segel Model: Two Scenarios for SGG.3 Scenario 1: Strongest Advection at the Back -- 4 Scenario 2: Cell Leakage Compensated by Growth -- 5 Conclusion and Perspectives -- References -- Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker-Planck Interpolation -- 1 Introduction -- 1.1 Mean Shift-Based Methods -- 1.1.1 Lifting the Dynamics to the Wasserstein Space -- 1.2 Spectral Methods -- 1.2.1 Normalized Versions of the Graph Laplacian -- 1.2.2 More General Spectral Embeddings -- 1.3 Outline -- 2 Mean Shift and Fokker-Planck Dynamics on Graphs -- 2.1 Dynamic Interpretation of Spectral Embeddings -- 2.2 The Mean Shift Algorithm on Graphs -- 2.2.1 Mean Shift on Graphs as Inspired by Wasserstein Gradient Flows -- 2.2.2 Quickshift and KNF -- 3 Fokker-Planck Equations on Graphs -- 3.1 Fokker-Planck Equations on Graphs via Interpolation -- 3.2 Fokker-Planck Equation on Graphs via Reweighing and Connections to Graph Mean Shift -- 4 Continuum Limits of Fokker-Planck Equations on Graphs and Implications -- 4.1 Continuum Limit of Mean Shift Dynamics on Graphs -- 4.2 Continuum Limits of Fokker-Planck Equations on Graphs -- 4.3 The Witten Laplacian and Some Implications for Data Clustering -- 5 Numerical Examples -- 5.1 Numerical Method -- 5.2 Simulations -- 5.2.1 Graph Dynamics as Density Dynamics -- 5.2.2 Comparison of Graph Dynamics and PDE Dynamics -- 5.2.3 Clustering Dynamics -- 5.2.4 Effect of the Kernel Density Estimate on Clustering -- 5.2.5 Effect of Data Distribution on Clustering -- 5.2.6 Blue Sky Problem -- 5.2.7 Density vs. Geometry -- References -- Random Batch Methods for Classical and Quantum Interacting Particle Systems and Statistical Samplings -- 1 Introduction -- 2 The Random Batch Methods -- 2.1 The RBM Algorithms -- 2.2 Convergence Analysis -- 2.3 An Illustrating Example: Wealth Evolution -- 3 The Mean-Field Limit -- 4 Molecular Dynamics.4.1 RBM with Kernel Splitting -- 4.2 Random Batch Ewald: An Importance Sampling in the Fourier Space -- 5 Statistical Sampling -- 5.1 Random Batch Monte Carlo for Many-Body Systems -- 5.2 RBM-SVGD: A Stochastic Version of Stein Variational Gradient Descent -- 6 Agent-Based Models for Collective Dynamics -- 6.1 The Cucker-Smale Model -- 6.2 Consensus Models -- 7 Quantum Dynamics -- 7.1 A Theoretical Result on the N-Body Schrödinger Equation -- 7.1.1 Mathematical Setting and Main Result -- 7.2 Quantum Monte Carlo Methods -- 7.2.1 The Random Batch Method for VMC -- 7.2.2 The Random Batch Method for DMC -- References -- Trends in Consensus-Based Optimization -- 1 Introduction -- 1.1 Notation and Assumptions -- 1.1.1 The Weighted Average -- 2 Consensus-Based Global Optimization Methods -- 2.1 Original Statement of the Method -- 2.1.1 Particle Scheme -- 2.1.2 Mean-Field Limit -- 2.1.3 Analytical Results for the Original Scheme Without Heaviside Function -- 2.1.4 Numerical Methods -- 2.2 Variant 1: Component-Wise Diffusion and Random Batches -- 2.2.1 Component-Wise Geometric Brownian Motion -- 2.2.2 Random Batch Method -- 2.2.3 Implementation and Numerical Results -- 2.3 Variant 2: Component-Wise Common Diffusion -- 2.3.1 Analytical Results -- 2.3.2 Numerical Results -- 3 Relationship of CBO and Particle Swarm Optimization -- 3.1 Variant 4: Personal Best Information -- 3.1.1 Performance -- 4 CBO with State Constraints -- 4.1 Variant 5: Dynamics Constrained to Hyper-Surfaces -- 4.1.1 Analytical Results -- 5 Overview of Applications -- 5.1 Global Optimization Problems: Comparison to Heuristic Methods -- 5.2 Machine Learning -- 5.3 Global Optimization with Constrained State Space -- 5.4 PDE Versus SDE Simulations -- 6 Conclusion, Outlook and Open problems -- References.Modeling and Simulation in Science, Engineering and Technology Mathematical optimizationMathematical optimizationComputer programsModels matemàticsthubOptimització matemàticathubLlibres electrònicsthubMathematical optimization.Mathematical optimizationComputer programs.Models matemàticsOptimització matemàtica519.3Bellomo N.Carrillo José AntonioTadmor EitanMiAaPQMiAaPQMiAaPQBOOK996466417303316Active particles1537369UNISA04115nam 22005655 450 991099279190332120250730215012.0981-9627-63-X10.1007/978-981-96-2763-9(CKB)38165287900041(DE-He213)978-981-96-2763-9(MiAaPQ)EBC31979874(Au-PeEL)EBL31979874(EXLCZ)993816528790004120250328d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierCell Signaling Pathways and Their Therapeutic Implication in Cancers /edited by Muneeb U. Rehman, Mosin Saleem Khan1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (XX, 435 p. 55 illus., 46 illus. in color.)981-9627-62-1 Chapter 1. An Introduction to Cell Signalling pathways and their Dysregulation in cancer -- Chapter 2. Involvement of Mitogen activated protein (MAP) Kinase pathway in the etiopathogenesis of cancer and its role in cancer therapeutics -- Chapter 3. Association of PI3K/AKT/mTOR pathway with cancer and its therapeutic implications -- Chapter 4. Role and therapeutic implication of Hedgehog signalling pathway in cancer -- Chapter 5. Hippo LATS pathway in cancer and its role as a potential target for anti-cancer drugs -- Chapter 6. Role and therapeutic implication of Notch signalling pathway in cancer -- Chapter 7. Role and therapeutic implication of Wnt signalling pathway in cancer -- Chapter 8. Involvement and therapeutic implication of JAK-STAT pathway in cancer -- Chapter 9. Role of cAMP–PKA–CREB signalling pathway in cancer -- Chapter 10. Role and therapeutic implications of vitamin D signalling pathway in cancer -- Chapter 11. TGF-β signalling pathway in cancer and its therapeutic role -- Chapter 12. Role of signaling pathways regulating cell cycle progression in cancer -- Chapter 13. Role of signaling pathways regulating genomic stability in cancer.This book discusses different signaling pathways involved in tumor development and progression. It discusses the pathways that allow tumor cells to proliferate, survive, and invade other tissues. Further, the book reviews the signal transduction regulating phagocytosis, production of cytokines, cell division, and differentiation. It elucidates the dysregulation of cellular signal transduction induced by the genetic and epigenetic changes that drive cancer and enumerates the signaling network encompassing the extracellular matrix, blood vessels, and the immune system. Additionally, the book provides mechanistic insights into inhibitors of the receptor tyrosine kinases, BRAF, EGFR, and ALK that improve clinical response and increase the survival of patients with cancer. The book covers the signal transduction pathways that regulate cell cycle progression and genomic stability, providing a mechanistic understanding of the major checkpoint pathways, and reviewing their diagnostic and therapeutic potential. This volume is essential reading for researchers to understand the therapeutic potential of important signaling molecules against cancer.CancerCell divisionCancerGenetic aspectsCancer BiologyCell DivisionCancer Genetics and GenomicsCancer.Cell division.CancerGenetic aspects.Cancer Biology.Cell Division.Cancer Genetics and Genomics.571.978616.994Rehman Muneeb Uedthttp://id.loc.gov/vocabulary/relators/edtKhan Mosin Saleemedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910992791903321Cell Signaling Pathways and Their Therapeutic Implication in Cancers4349111UNINA