Atjaunināt sīkdatņu piekrišanu

E-grāmata: System Engineering Approach to Planning Anticancer Therapies

, , , ,
  • Formāts: PDF+DRM
  • Izdošanas datums: 19-May-2016
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319280950
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 53,52 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
System Engineering Approach to Planning Anticancer TherapiesPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 19-May-2016
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319280950
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This book focuses on the analysis of cancer dynamics and the mathematically based synthesis of anticancer therapy. It summarizes the current state-of-the-art in this field and clarifies common misconceptions about mathematical modeling in cancer. Additionally, it encourages closer cooperation between engineers, physicians and mathematicians by showing the clear benefits of this without stating unrealistic goals. Development of therapy protocols is realized from an engineering point of view, such as the search for a solution to a specific control-optimization problem. Since in the case of cancer patients, consecutive measurements providing information about the current state of the disease are not available, the control laws are derived for an open loop structure. Different forms of therapy are incorporated into the models, from chemotherapy and antiangiogenic therapy to immunotherapy and gene therapy, but the class of models introduced is broad enough to incorporate other forms of

therapy as well.The book begins with an analysis of cell cycle control, moving on to control effects on cell population and structured models and finally the signaling pathways involved in carcinogenesis and their influence on therapy outcome. It also discusses the incorporation of intracellular processes using signaling pathway models, since the successful treatment of cancer based on analysis of intracellular processes, might soon be a reality. It brings together various aspects of modeling anticancer therapies, which until now have been distributed over a wide range of literature. Written for researchers and graduate students interested in the use of mathematical and engineering tools in biomedicine with special emphasis on applications in cancer diagnosis and treatment, this self-contained book can be easily understood with only a minimal basic knowledge of control and system engineering methods as well as the biology of cancer. Its interdisciplinary character and the autho

rs" extensive experience in cooperating with clinicians and biologists make it interesting reading for researchers from control and system engineering looking for applications of their knowledge. Systems and molecular biologists as well as clinicians will also find new inspiration for their research.

Introduction.- Cell Cycle as an Object of Control.- Therapy Optimization in Population Dynamics Models.- Structured Models and Their Use in Modeling Anticancer Therapies.- Signaling Pathways Dynamics and Cancer Treatment.- Model Identification and Parameter Estimation.- Appendixes: Stability and Controllability of Dynamical Systems.- Pontryagin Maximum Principle and Optimal Control.- Bifurcation Analysis.- Numerical Implementation of the Runge Kutta and Gillespie Methods.
1 Introduction
1(8)
References
7(2)
2 Cell Cycle as an Object of Control
9(46)
2.1 Cell Cycle and Chemotherapy
9(7)
2.2 Optimization of Cell Cycle Dependent Chemotherapy
16(14)
2.2.1 Compartmental Models of Cell Cycle with Control Action
17(3)
2.2.2 Models with a Single Phase-Specific Killing Agent
20(1)
2.2.3 Multiple Drug Therapy and Control
21(1)
2.2.4 Optimization of Treatment Protocols
22(8)
2.3 Pharmacokinetics and Pharmacodynamics
30(3)
2.4 Resonances, Synchronization, Aftereffects
33(2)
2.5 Drug Resistance in Chemotherapy
35(20)
2.5.1 Simple Models of Drug Resistance
35(1)
2.5.2 Infinite-Dimensional Models of Drug Resistance
36(9)
2.5.3 Model Transformation and Optimization of Therapy
45(4)
References
49(6)
3 Therapy Optimization in Population Dynamics Models
55(30)
3.1 Standard Models of Population Dynamics
55(6)
3.2 Tumor Angiogenesis and Antiangiogenic Therapy
61(1)
3.3 Models of Cancer Growth Including Vascularization
62(5)
3.4 Planning Antiangiogenic and Combined Therapies
67(8)
3.5 Models of Gene and Immunotherapy in Cancer Treatment
75(10)
References
80(5)
4 Structured Models and Their Use in Modeling Anticancer Therapies
85(54)
4.1 Tumor Growth and Treatment in Two and Three Dimensions
85(13)
4.1.1 Chemotaxis
86(1)
4.1.2 Spheroids and Tumor Cords
87(2)
4.1.3 Multiphase Theory
89(3)
4.1.4 Modeling Desmoplastic Tumor
92(1)
4.1.5 Neovascularization Model
92(2)
4.1.6 Modeling the Tumor Microenvironment Impact
94(1)
4.1.7 Other Models
95(3)
4.2 Models of Angiogenesis
98(4)
4.3 Modeling the Effects of Therapeutic Actions
102(2)
4.4 Structure in Hematopoiesis and Carcinogenesis and Its Role in Cancer Detection, Prevention, and Treatment
104(14)
4.4.1 Stochasticity or Determinism in Stem Cell Systems?
105(10)
4.4.2 Models of Mutations and Evolution of Disease
115(3)
4.4.3 Prevention, Detection, and Treatment of Cancer Given Hierarchical Structure of Cancer and Its Stepwise Progression
118(1)
4.5 Physiologically Structured Models
118(5)
4.6 Cellular Automata and Agent Based Approach to Treatment Planning
123(16)
4.6.1 Biologically Motivated Cellular Automata
123(3)
4.6.2 Single-Cell-Based Models
126(2)
4.6.3 Models Containing Therapeutic Actions
128(2)
References
130(9)
5 Signaling Pathways Dynamics and Cancer Treatment
139(32)
5.1 Deterministic and Stochastic Models of Regulatory Modules in Signaling Pathways
139(17)
5.1.1 Signaling Pathways
139(2)
5.1.2 Experimental Data
141(2)
5.1.3 Mathematical Modeling
143(4)
5.1.4 Basic Principles Used in Development of the Mathematical Model
147(6)
5.1.5 Stochastic or Deterministic Model?
153(2)
5.1.6 Deterministic Approximation of the Stochastic System
155(1)
5.2 Intracellular Processes as Objects of Control
156(5)
5.3 Example of the Control Signals in the Signaling Pathways
161(10)
5.3.1 p53 Signaling Pathway
161(2)
5.3.2 Interfering RNA
163(1)
5.3.3 Nutlin-3 as an Example of the Chemical Particle-Based Control
164(2)
References
166(5)
6 Model Identification and Parameter Estimation
171(28)
6.1 General Remarks
171(2)
6.2 Compartmental Population Models
173(3)
6.2.1 Length of Cell Cycle Phases
174(1)
6.2.2 Rates of Missense Mutational Events
174(1)
6.2.3 Gene Copy Number Variation
175(1)
6.3 Structured Models, Models Including Angiogenesis and Spatial Models
176(1)
6.4 Models of Signaling Pathways and Intracellular Processes
176(6)
6.4.1 Degradation and Transcription Rates
178(1)
6.4.2 Population vs Single Cell Experiments
178(2)
6.4.3 Relative vs Absolute Measurements
180(1)
6.4.4 Algorithms Used to Estimate Kinetic Parameters
181(1)
6.5 Pharmacokinetics Parameters
182(1)
6.6 Sensitivity of Regulatory Network Models
183(16)
6.6.1 Local Sensitivity Analysis
184(2)
6.6.2 Global Sensitivity Analysis
186(2)
6.6.3 Parameters Ranking
188(2)
6.6.4 Numerical Examples
190(4)
References
194(5)
Appendices
199(34)
A Stability and Controllability of Dynamical Systems
199(8)
References
205(2)
B Pontryagin Maximum Principle and Optimal Control
207(6)
References
211(2)
C Bifurcation Analysis
213(10)
C.1 Flip Bifurcation
214(1)
C.2 Fold Bifurcation
215(1)
C.3 Pitchfork Bifurcation
216(3)
C.4 Transcritical Bifurcation
219(1)
C.5 Hopf Bifurcation
220(2)
References
222(1)
D Numerical Implementation of the Runge--Kutta and Gillespie Methods
223(10)
D.1 Deterministic Algorithms
223(2)
D.2 Stochastic Algorithms
225(1)
D.2.1 Gillespie Algorithm
225(2)
D.2.2 τ-Leap Method
227(3)
D.2.3 Haseltine--Rawlings Modification
230(2)
References
232(1)
Index 233
Andrzej Swierniak received his PhD in Automatic Control from the Silesian University of Technology, Gliwice Poland. He published approximately 300 papers. He is a Fellow of the American Mathematical Society, and a member of the IEEE, Society of Mathematical Biology, Polish Society of Theoretical and Applied Electrotechnics, IFAC Committees 1.4 and 8.2, Committees of Automatic Control and Robotics, and Biocybernetics and Biomedical Engineering of Polish Academy of Science. He is an editorial boards member of Mathematical Bioscience, International Journal of Applied Mathematics and Computer Science, Mathematical Problems in Engineering, and the Editor in Chief of Archives of Control Sciences. His current interests are in control and optimization, biomathematical modeling and systems biology. His research has been supported by Polish National Centre Science, Polish National Centre of Research and Development, NATO, and European Community FP6 and FP7. Dr Swierniak has advised 10PhD and 50 MS students and has organized numerous conferences and international meetings in System Engineering and Systems Biology.

Marek Kimmel is a Professor of Statistics and Bioengineering at the Rice University in Houston, TX, USA and a Professor in the Systems Engineering Group in the Silesian University of Technology in Gliwice, Poland. He is an author and co-author of over 200 peer-reviewed publications and 5 monographs. His professional interests are focused in application of stochastic processes in molecular and cell biology and genetics. He advised about 30 doctoral students in USA, Poland and France. His research has been supported by grants from NSF, NIH, NATO, ERC, NCN (Polish National Science Committee), EPSRC (UK). He is the Mathematical Biology Editor of Biology Direct and in 2011 was the Fellow at the Institute for Advanced Study at Warwick University, UK. He is a Fellow of the American Statistical Association.

Jaroslaw Smieja received PhD degree in Automatic Control from the Silesian University of Technology, Gliwice Poland, in 2000. His research interests include systems application of mathematical modeling and control theory methods to analysis of biological systems both at intracellular and cell population levels. In particular, he is involved in development of signaling pathway models, describing cell responses to stress factors and viral infections as well as optimization of anticancer treatment protocols. He is an author and co-author of over 80 peer-reviewed publications. He reviews paper for many international journals, including Journal of Theoretical Biology, Mathematical Biosciences and Mathematical Biosciences and Engineering. Dr Smieja teaches numerous courses at the Silesian University of Technology, including Systems Dynamics, Optimization Methods, Bioinformatics and Mathematical Modeling in Biology and Medicine.

Krzysztof Psiuk-Maksymowicz obtained his PhD degree in Biocybernetics and MedicalEngineering from Silesian University of Technology, Gliwice Poland, in 2009. He works as an assistant professor at Institute of Automatic Control of Silesian University of Technology. In 2004-2007 he worked as a young research scientist in the Department of Physics, Gothenburg University, Sweden. His research focuses on mathematical biology, in particular modeling of tumor growth and therapeutic actions, as well as medical image processing. He has published over 30 papers in journals and refereed conference proceedings. He took part in many national and international projects, focused on either basic research, applications or software development.

Krzysztof Puszynski received his M. Sc degree in automatic control and robotics from Silesian University of Technology in 2003 and Ph.D. in biocybernetics and biomedical engineering also from Silesian University of Technology in 2009. His main research interests include systems biology, bioinformatics and control engineering in biological systems. He was a visiting professor in the Institute of the Systems Analysis and Informatics in Rome in 2011 and University of Alberta in Edmonton, Canada in 2012. He is presently an Assistant Professor in the Department of Automatic Control at the Silesian University of Technology. He is an author or co-author of more than 50 journal articles, book chapters and conference papers.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97833192809502e.html
Keywords: