Guías Docentes Electrónicas
1. General information
Course:
MOVILIDAD Y DINÁMICA CELULAR: INTRODUCCIÓN A LA DINÁMICA DEL CRECIMIENTO TUMORAL
Code:
310221
Type:
ELECTIVE
ECTS credits:
6
Degree:
2351 -
Academic year:
2019-20
Center:
602 -
Group(s):
20 
Year:
1
Duration:
First quarter
Main language:
Spanish
Second language:
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer: VICTOR MANUEL PEREZ GARCIA - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Politécnico/1.09.5
MATEMÁTICAS
3805
victor.perezgarcia@uclm.es
To be determined

2. Pre-Requisites

Basic knowledge of ordinary and partial differential equations

3. Justification in the curriculum, relation to other subjects and to the profession

This subject addresses topics in cellular motility and cellular population dynamics, specifically in the context of tumor growth from a mathematical perspective. Cancer is one of the major health problems in industrialized societies and there is a global perception that mathematical models will play a relevant role in the design of efficient therapeutical strategies. This subject introduces this field of knowledge and uses techiques related to other master' topics in the field of cancer modelling such as partial differential equations, dynamics systems and numerical methods.


4. Degree competences achieved in this course
Course competences
Code Description
CB06 Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context.
CB07 Apply the achieved knowledge and ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to the area of study
CB08 Be able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of knowledge and judgments
CB09 Know how to communicate the conclusions and their supported knowledge and ultimate reasons to specialized and non-specialized audiences in a clear and unambiguous way
CB10 Have the learning skills which allow to continue studying in a self-directed or autonomous way
CE02 Develop the ability to decide the appropriate techniques to solve a specific problem with special emphasis on those problems associated with the Modeling in Science and Engineering, Astrophysics, Physics, and Mathematics
CE06 Prove the necessary capacity to perform a critical analysis, evaluation and synthesis of new and complex results and ideas in the field of astrophysics, physics, mathematics and biomathematics
CE07 Ability to understand and apply advanced knowledge of mathematics and numerical or computational methods to problems of biology, physics and astrophysics, as well as to build and develop mathematical models in science, biology and engineering
CE08 Ability to model, interpret and predict from experimental observations and numerical data
CG02 Ability to generate and independently develop innovative and competitive proposals in research and professional activity in the scientific field of Physics and Mathematics
CG03 Present publicly the research results or technical reports, to communicate the conclusions to a specialized court, interested persons or organizations, and discuss with their members any aspect related to them
CG05 Gain the ability to develop a scientific research work independently and in its entirety. Be able to search and assimilate scientific literature, formulate hypotheses, raise and develop problems and draw conclusions from the obtained results
CT01 Promote the innovative, creative and enterprising spirit
CT04 Understand and reinforce the ethical and deontological responsibility and commitment in the performance of the professional and research activity and as a citizen
CT05 Autonomous learning and responsibility (analysis, synthesis, initiative and teamwork)
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Modeling in biological processes. Active particles
Treatment of biological data
Critical analysis of classic models based on linear diffusion
Understanding of a scientific article on topics related to the course
Understanding of individual behavior versus collective behavior in biomedical and social sciences
Public exhibition and critical analysis of a research article related to the subject of the course.
Interpretation of phenomenological results and ability to model them
Additional outcomes
Not established.
6. Units / Contents
  • Unit 1: Introduction to cancer for mathematicians.
  • Unit 2: Elementary mathematical models of tumor growth.
  • Unit 3: Mathematical models of response to therapies: radiotherapy, chemotherapy and novel therapies.
  • Unit 4: Models with spatio-temporal dependences.
  • Unit 5: Multiscale models.
  • Unit 6: Mathematical models of the development of resistances.
  • Unit 7: Mathematical models in neuro-oncology.
  • Unit 8: Mathematical models of leukemias.
  • Unit 9: Fractals and scaling laws in cancer.
  • Unit 10: Other examples of applications: Breast cancer, prostate cancer.
7. Activities, Units/Modules and Methodology
Training Activity Methodology Related Competences ECTS Hours As Com R Description
Class Attendance (theory) [ON-SITE] Lectures 1.6 40 N N N
Class Attendance (practical) [ON-SITE] Project/Problem Based Learning (PBL) 0.32 8 Y Y Y
Practicum and practical activities report writing or preparation [OFF-SITE] Problem solving and exercises 0.6 15 Y Y Y
Problem solving and/or case studies [ON-SITE] Case Studies 0.24 6 N N N
Study and Exam Preparation [OFF-SITE] 2.4 60 N N N
Writing of reports or projects [OFF-SITE] Reading and Analysis of Reviews and Articles 0.84 21 N N N
Total: 6 150
Total credits of in-class work: 2.16 Total class time hours: 54
Total credits of out of class work: 3.84 Total hours of out of class work: 96
As: Assessable training activity
Com: Training activity of compulsory overcoming
R: Rescheduling training activity
8. Evaluation criteria and Grading System
  Grading System  
Evaluation System Face-to-Face Self-Study Student Description
Practicum and practical activities reports assessment 30.00% 30.00%
Theoretical papers assessment 60.00% 60.00%
Assessment of active participation 10.00% 10.00%
Total: 100.00% 100.00%  

Evaluation criteria for the final exam:
Evaluation criteria not defined
Specifications for the resit/retake exam:
Evaluation criteria not defined
Specifications for the second resit / retake exam:
Evaluation criteria not defined
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours
Writing of reports or projects [AUTÓNOMA][Reading and Analysis of Reviews and Articles] 21

Unit 2 (de 10): Elementary mathematical models of tumor growth.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 6
Class Attendance (practical) [PRESENCIAL][Project/Problem Based Learning (PBL)] 4
Practicum and practical activities report writing or preparation [AUTÓNOMA][Problem solving and exercises] 7
Study and Exam Preparation [AUTÓNOMA][ ] 12

Unit 3 (de 10): Mathematical models of response to therapies: radiotherapy, chemotherapy and novel therapies.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 6
Class Attendance (practical) [PRESENCIAL][Project/Problem Based Learning (PBL)] 4
Practicum and practical activities report writing or preparation [AUTÓNOMA][Problem solving and exercises] 8
Study and Exam Preparation [AUTÓNOMA][ ] 12

Unit 4 (de 10): Models with spatio-temporal dependences.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 7
Study and Exam Preparation [AUTÓNOMA][ ] 10

Unit 7 (de 10): Mathematical models in neuro-oncology.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 7
Problem solving and/or case studies [PRESENCIAL][Case Studies] 3
Study and Exam Preparation [AUTÓNOMA][ ] 8

Unit 10 (de 10): Other examples of applications: Breast cancer, prostate cancer.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 8
Problem solving and/or case studies [PRESENCIAL][Case Studies] 3
Study and Exam Preparation [AUTÓNOMA][ ] 6

Global activity
Activities hours
10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
D. Wodarz, N. L. Komarova Dynamics of Cancer: Mathematical Foundations of Oncology Singapur World Scientific 978-981-4566-36-0 2014 Libro centrado en los aspectos evolutivos del cancer desde un punto de vista matemático. No correlaciona con datos. Ficha de la biblioteca
P. M. Altrock, L. L. Liu, F. Michor The mathematics of cancer: integrating quantitative models Londres Nature Reviews Cancer, 15, 730-745 2015 Un review reciente sobre modelos en cancer  
Y. Kuang, J. D. Nagy, S. E. Eikenberry Introduction to mathematical oncology Nueva York CRC Press 9781584889908 2016 Un libro sobre distintos tipos de modelos matemáticos en cancer. No hay contraste con datos. Ficha de la biblioteca



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