MASTER ON COMPUTATIONAL BIOTECHNOLOGY

Transcription

1 MASTER ON COMPUTATIONAL BIOTECHNOLOGY

2 1.- GENERAL FEATURES OF THE POSTGRADUATE PROGRAM Program name Communication and Information Technologies Coordinator of the program Mariano Fernández López Organizers of the master Carlos Óscar Sánchez Sorzano. Dep. of Electronica and Communication System Engineering (Polytechnical School, Univ. San Pablo CEU). Carlos Bocos de Prada. Dep. of Biochemistry, Molecular and Cell Biology (Pharmacy School, Univ. San Pablo CEU) Master title Name Master on Computational Biotechnology Institution San Pablo CEU University Kind of master Academia/Research Number of credits 90 ECTS Periodicity Yearly Max. Number of Students 30 Min. Number of Students Study regime Part time, presential

3 2.- PROGRAM JUSTIFICATION Motivation: Objectives The objective of this master is to provide technological as well as biotechnological professionals with the necessary background for solving data analysis problems in biotechnology. The master carefully balances the biotechnological studies with the technical ones. The master concentrates on the practical aspects of the problem and all courses have practices either on a wet lab or a computer Interest for research and academia With the currently available data, in 2004 there were more than 500 companies in Spain whose activity was related to the application of biotechnology to the improvement of productive processes (food, agriculture, pharmacy, biomaterials, etc.) or healthcare. Biotechnology incomes are above 400M euros per year, and the economical prospects are very positive. (Source: Perspectivas económicas de la Biotecnología en España, Genoma España, 2005). Biotechnology is a multidisciplinary subject involving biologists, pharmacists, biochemists, medical doctors, etc. However, due to the complexity of the technologies employed, it is not difficult to also find engineers, computer scientists, physicists and mathematicians. Although the number of technological problems found in Biotechnology is uncountable, we will focus in one of them: analysing the huge amount of data being produced. This problem is specially interesting because we are reaching a situation in which an increase of the data available is not implying a proportional increase of knowledge. Somehow, we could say that there is excess of data that has not yet been processed and analyzed. The amount of data deposited in biotechnologyrelated databases is currently exponentially growing in more than 900 databases. The number of databases grows at a constant rate of more than 100 new databases per year (Source: M. Y. Galperin. The molecular biology database collection: 2007 update. Nucleic Acids Research, 35: D3- D5, 2007). The fact that data analysis in Biotechnology is a real problem can be checked by searching in Google (general Internet search engine), Pubmed (database of scientific articles), and Amazon (online bookstore). The following table shows the number of entries returned by each of the search engines Query Google Pubmed Amazon Biology data analysis" 83.9M Genomic data analysis" 38.0M Biotechnology data analysis" 31.3M Pharmaceutical data analysis" 29.9M Proteomic data analysis" 7.2M Professionals approaching biotechnology must normally complete their educational background by means of specific studies in this field. For this reason, there are a number of masters and Ph.D. programs about Biotechnology and similar topics. Most masters are very much oriented towards the education in the biological techniques and, therefore, most of the attendees come from the life sciences (biologists, pharmacists, biochemists, medical doctors, etc.). From now on, we will refer to

4 this collectivity as biotechnological professionals. Consequently, biotechnological professionals are the ones who in practice absorb most of the work related to biotechnology and the ones who face the complex problems encountered. In particular, as already mentioned, these professionals face a serious data analysis problem due to the current production rate. The appropriate treatment of this data is something that must be handled from a technical perspective: engineering, computer science and statistics. However, the educational background of biologists, biochemists, etc. does not cover these aspects. Two are the current solutions adopted in companies and research centers: either the researcher facing the problem acquires (most of the times on their own) the necessary programming skills and the data analysis knowledge, or some technical professionals are hired to perform these tasks although they largely ignore the meaning of the data being analyzed and how it is produced. In any of the two cases, the general complain is the lack of knowledge of the other discipline hindering the efficiency of a research team and the development of new techniques and applications Equivalent programs in other institutions To the best of our knowledge, there does not exist an absolutely equivalent master in any university around the world. However, there exist a number of related programs of variable duration ranging from 4-year graduate programs (recently started at the Univ. of Duke) to twohours courses. Masters in bioinformatics are not uncommon. However, our master gives more weight to the biotechnological aspects than the usual bioinformatics masters. The following list shows a small sample of related programs (there is a large number of related courses inside graduate/ph.d. programs that are similar to some of our courses): - Computational biology and bioinformatics 4 years, Duke Univ. ( - Master on Biotechnology, Master on Bioinformatics, 2 years, John Hopkins University ( - Master of Information Technology degree in Bioinformatics, 2 years, Aarhus Univ., ( - Master program on Bioinformatics, 2 years, Helsinki Univ. ( - Computational molecular biology 2 years, Washington Univ and Fred Hutchinson Cancer Research Center ( - Integrated data analysis for high throughput biology 14 days, Cold Spring Harbor Laboratory ( - Bioinformatics and genome data analysis, 7 days, Institute Pasteur Paris ( - Practical DNA microarray analysis 3 days, Max-Planck Institute of Molecular Genetics ( - Molecular marker data analysis 2 hours, University of Illionois at Urbana-Champaign ( Educational objectives The master presumes a set of skills already acquired during the graduate program (either coming from the biological side or the technical side). These skills are specialized to the particular case of

5 data analysis in biotechnology. This specialization allows the student successfully finishing the master to develop new ideas and work methodologies in an industry with a high level of research and development. Moreover, the student will acquire the capability of facing new problems and looking for already existing solutions or for solutions completely new. All this, taking into account the ethical aspects derived from this sensible technology. The student must develop during the master the ability to communicate his results and skills to a specialist as well as non-specialist audience Coherence with other programs of the same University: This master holds strong relationships with other programs already offered by San Pablo CEU University. The connection to the graduate programs in Pharmacy and Computer Science are obvious. Electrical engineering is another clear reference since, in our experience, electrical engineers are involved in many signal and data analysis tasks. The connection to the summerschool in "Advanced data analysis and modelling" is also very clear. The master is also related to the Ph.D. programs in "Perinatal biology and pathology" and "Medical chemistry" of the Pharmacy School. Finally, the master is also connected to other masters like the one in Clinical Chemistry Situation of the R&D activities in the industry: Biotechnology is a highly-skilled technology in which research and development (R&D) play a fundamental role. According to the 2005 ASEBIO report [1], in 2005, there were 538 companies that carried out biotechnological activities in Spain and the total gross income was of 370M euros. According to the same report, 86% of the income is reinvested in R&D activities. Spain is the fourth country in the European Union in biotechnological scientific production [2]. Spanish research in biotechnology is more basic than applied, although the indexes of technological transfer are higher in Spain than in other neighboring countries [2]. According to the COTEC report [3], more than 50% of the Spanish biotechnology is related to the healthcare and pharmaceutical industry. The economical indicators of Spanish biotechnology show a sector growing in terms of public investment as well as private [2]. The public investment in biotechnology nearly doubles the private investment, although since 2001 the private investment is superior to the public subsidies. According to the data of the Genoma España Foundation, private investment in biotechnology grows annually at a rate of 30%. Most of the investments concentrates in Madrid and Cataluña. In fact the White Book of Innovation in the Comunidad de Madrid [4] identifies biotechnology as one of the central topics in the region around which the Scientific Park of Madrid is focused. Other growing regions in Spain are Andalucía, Comunidad Valenciana, Galicia, Murcia and Castilla y León. Complementing this optimistic view of all reports, these also point out a few weaknesses as the lack of a legal European framework [1], lack of size of Spanish companies to compete in the international markets [2], and the lack of multidisciplinarity in the biotechnological research [1] (this is precisely the point at which the master is addressed). On the other hand, it is also important to highlight that, in 2004, the COTEC foundation identified data mining as one of the technological opportunities in Spain. Although this report does not offer economical data about this activity in Spain, it indicates that the rate at which data is produced and communicated in all productive sectors makes the use of advanced data analysis techniques absolutely a must. In particular, biotechnology is also experiencing this exponential growth of data available, thus requiring an appropriate statistical treatment.

6 The following reports also reveal the situation of biotechnology in Madrid as well as in Spain: Fundación Genoma España. Perspectivas económicas de la biotecnología en España Fundación Genoma España. La biotecnología en España Fundación Genoma España. Industria biotecnológica en la comunidad de Madrid Bibliography [1] Asociación ASEBIO. Informe [2] Fundación Genoma España. Avance del estudio estratégico de la biotecnología en España: descripción e indicadores [3] Fundación COTEC. Biotecnología en la medicina del futuro [4] Fundación COTEC. Libro blanco de la innovación en la comunidad de Madrid [5] Fundación COTEC. Minería de datos. 2004

7 2.2.- Program structure: Program coherence The master program has been designed to provide students with a balanced education in the biotechnological techniques producing the data as well as the statistical/programming techniques to analyze them. Approximately 50% of the time is devoted to each one of these aspects. This is one of the main differences between this master and others more biologically oriented or bioinformatics oriented. They are more unbalanced and do not really complement the education of the student in the field that is not his background. The courses offered represent the state-of-art research in biotechnology as in data analysis. In this way, students receive a quality education that will allow them to smoothly integrate in the biotechnological market and research Modular structure The program has been divided into 6 modules: An adaptation module depending on the student academic background (those with a biological profile study technical topics and viceversa). Two modules of biotechnological content Two modules of statistical and data analysis content One module in bioinformatics, discipline in which the two sides of the master naturally gather The adaptation module is the first one, followed by the four specific modules and, finally, the bioinformatics module. There is not a technical difference between the two biotechnological modules and the data analysis modules, and the division mostly corresponds to organizational reasons. The student must perform a master thesis on a project similar to what he will find in his professional life as an analyst of biotechnological data.

8 3.- PROGRAM DESCRIPTION Educational objectives At the end of the master the student must: know the experimental and research techniques currently used in biotechnology know the specific problems related to these techniques and the kind of data generated know the data analysis techniques available for the biotechnological data be able of implementing on his own any data analysis technique be able of developing new data processing algorithms adapted to the specificities of a given problem Program structure Modules and courses Module 1A. Biotechnological adaptation BT0.1.- Adaptation 1 BT0.2.- Adaptation 2 Module 1B. Computational adaptation DA0.1.- Adaptation 1 DA0.2.- Adaptation 2 Module 2. Data Analysis 1 DA1.1.- Statistical inference, Regression and Experiment design DA1.2.- Multivariate data analysis DA1.3.- Bayesian networks DA1.4.- Neural networks Module 3. Biotechnology 1 BT1.1.- Molecular biology and Recombinant DNA technology BT1.2.- Sequencing, genotyping and transcriptomics BT1.3.- Proteomics BT1.4.- Metabolomics Module 4. Data analysis 2 DA2.1.- Classification and clustering DA2.2.- Dynamic models DA2.3.- Associative rules, logic networks and grammars Module 5. Biotechnology 2 BT2.1.- Structural biology and protein engineering BT2.2.- Biotechnology BT2.3.- Synthetic and systems biology Module 6. Bioinformatics BI.1.- Bioinformatic databases and literature analysis BI.2.- DNA, protein and structure analysis BI.3.- Interaction networks and arrays analysis (10 ECTS) (6 ECTS) (4 ECTS) (10 ECTS) (6 ECTS) (4 ECTS) (14 ECTS) (5 ECTS) (3 ECTS) (3 ECTS) (3 ECTS) (14 ECTS) (5 ECTS) (3 ECTS) (3 ECTS) (3 ECTS) (11 ECTS) (5 ECTS) (3 ECTS) (3 ECTS) (11 ECTS) (3 ECTS) (4 ECTS) (4 ECTS) (15 ECTS) (3 ECTS) (7 ECTS) (5 ECTS)

9 Finally, the student must perform a master thesis of 15 ECTS. The first 60 ECTS of the master will be given from the beginning of October to middle June. The last 15 ECTS from the beginning of September to the end of October. Classes will take the last 4 hours of the afternoon from Monday to Friday so that attendance to the master is compatible with a part time job or a Ph.D. thesis.

10 3.3.- Course plan All courses have the following commonalities: Objectives The student must understand the basic concepts of the subject and be able to express real problems in the terms of the discipline studied. The course must show advanced topics related to the subject and provide mechanisms so that the student can look for new information if interested. The student must acquire a certain level of practice (either in the wet laboratory or in a computer). The course must promote the critical thinking in the subject. In the case of technical courses: show application cases in biotechnology; in the case of biotechnological courses: show the data analysis tools used Criterios y métodos de evaluación: At the end of each course there will be an exam to evaluate the student acquired knowledge. The final grade is a weighted sum of the grade in that exam (70%) and the grade in the practical sessions (30%). The final grade is a number between 0 and 10. To pass a course, the grade must be higher than 6.5. If a student fails a course, he will have another exam. If he fails this second exam, he will have to retake the course next year. If a student fails more than two courses in a year, he will not be able to obtain the master degree, although he will receive a diploma for each of the courses he passed Learning resources Each professor will distribute beforehand the material used during the course. Discussions and debates about specific topics may require the use of research articles or books. Professors will complement their lectures with so many documents as they consider necessary. There will be a virtual campus for the interchange of files between students and professors Language English

11 Biotechnological adaptation 1 Microbiology (2,5 ECTS) o Microbial structure, function, growth and control. o Interactions between microorganisms and humans on infectious diseases. o Applied microbiology. Cell biology and Genetics (3,5 ECTS) o The cell and its components. Structure and function of cellular organelles. o Heredity and variation. Genetic analysis. Mutation. Genetic transmission at chromosomal level. There will be 13 class hours and 17 personal work hours for each ECTS. This subject amounts for 6 ECTS that will be distributed as 78 class hours, and 102 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Biotechnological adaptation 2 General Chemistry (1,5 ECTS) o Identification and differentiation of compounds. Nomenclature. o Structure, stereochemistry and reactivity of the substances. o Chemical reactions and equilibrium. Chemical bond. o Role of the compounds on the biological processes. Biochemistry (2,5 ECTS) o Structure, properties and functionality of the main energetic and structural components of the organisms. o Main metabolic pathways and their regulation. Integration of the metabolic pathways. Signal transduction systems. o Basic techniques on the study of molecules and biochemical processes. There will be 13 class hours and 17 personal work hours for each ECTS. This subject amounts for 4 ECTS that will be distributed as 52 class hours, and 68 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc.

12 Computational adaptation 1 Programming (2 ECTS) o Fundamentals of structured programming o Making small programs of data analysis Statistics (2 ECTS) o Random discrete and continuous variables, in one or more dimensions. Random processes. o Fundamentals of descriptive statistics Databases (2 ECTS) o Fundamentals of relational databases and differences with other archiving techniques o Interaction with a database manager There will be 13 class hours and 17 personal work hours for each ECTS. This subject amounts for 6 ECTS that will be distributed as 48 class hours, 30 practice hours, and 102 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Computational adaptation 2 Abstract data types (2 ECTS) o Lists, queues, trees, etc. o Fundamentals of object-oriented programming Analysis and design of algorithms (2 ECTS) o Graph algorithms, tree algortihms, sort algorithms, numerical algorithms, etc. o Get to know an environment of already programmed algorithms There will be 13 class hours and 17 personal work hours for each ECTS. This subject amounts for 4 ECTS that will be distributed as 32 class hours, 20 practice hours, and 68 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Statistical inference, regression and experiment design

13 Statistical inference (2 ECTS) o Parameter estimation of parametric distributions o Hypothesis testing Regression (2 ECTS) o Simple linear regression o Multiple linear regression o Non linear regression Experiment design (1 ECTS) o Random designs o Block desgins o Factorial designs There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 5 ECTS that will be distributed as 35 class hours, 15 practice hours, and 100 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Multivariate analysis Fundamentals of Multivariate distributions Multivariate analysis of variance Principal Component Analysis Correspondence Analysis Discriminant Analysis Canonical Correlation Analysis There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Bayesian networks

14 Fundamentals of Bayesian networks Inference using Bayesian networks Learning algorithms for Bayesian networks There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Neural networks Fundamentals of: Neural networks: perceptron and multilayer perceptron Supervised learning rules Unsupervised learning rules There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Molecular biology and recombinant DNA technology Gene transmission processes at molecular level and their regulation. Basic techniques on molecular biology. Tools for DNA manipulation: recombinant DNA technology. To solve problems at molecular level on food, pharmaceutical, industrial, and environmental analyses. There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 5

15 ECTS that will be distributed as 35 class hours, 15 practice hours, and 100 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Sequencing, genotyping and transcriptomics To know concepts, methods and applications of functional genomics on biomedicine and biotechnology in order to solve biochemical and genetic problems related to functional genomics. Identification of polymorphisms and regions on the genome, and their implication on disease development and interindividual variation. Tools for gene localization on the genome. Methods of detection of the genetic and environmental alterations on the levels of transcriptome components. There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Proteomics What the proteomics is? To know concepts, methods and applications of proteomics on biomedicine and biotechnology in order to solve biochemical and genetic problems related to proteomics. Methods of detection of genetic and environmental alterations on the levels of proteome components. Search of new markers and targets for alleviating those alterations. There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc.

16 Metabolomics Biochemical and molecular data from patient samples (metrology) and their clinical significance (semiology). Main biochemical parameters involved in metabolic diseases, and organ and system alterations. Basic principles of analysis techniques. To know to select the more appropriated technique for analytes determination in each case. To understand the analysis of small molecules as a part of the metabolome. Study of sample profiles for both the characterization of changes and the differentiation of control, pathologic and treated samples. Models generation. Techniques for discerning models that detect signals with variations, that is, the markers of the alterations. There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Classification and clustering Classification (2.5 ECTS) o Classification algorithms o Combination of classifiers o Evaluation of the classification results Clustering (2.5 ECTS) o Exploratory Data Analysis o Preprocessing o Metrics o Clustering algorithms o Detection of outliers There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 5 ECTS that will be distributed as 30 class hours, 20 practice hours, and 100 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc.

17 Dynamic models Fundamental and applications of Hidden Markov Models Fundamental and applications of time series Fundamental and applications of differential equations There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Associative rules, logic networks and grammars Fundamentals of rule-based systems Fundamentals of rules and boolean combinations in logic networks Applications of grammars to genomics There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Structural biology and protein engineering Modelization, dynamics and folding of proteins. Fundamentals to elucidate a protein sequence from a gene sequence. Understanding the 3D conformation which determines the structure and function of the protein. Tools for the design of protein-interacting substances: molecular mechanics and dynamics, flexible docking, quantum mechanics calculations, modellating by homology, etc.

18 There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 20 class hours, 10 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Biotechnology Utilization of microbial and eukaryotic systems as biological factories for production of proteins based on recombinant DNA technology. Strategies for optimizing the expression of a cloned gene in cells. Industrial use of microorganisms and their metabolic processes. Large-scale production of proteins from recombinant microorganisms. Bioreactors. Methods of obtention of several products. There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 4 ECTS that will be distributed as 30 class hours, 10 practice hours, and 80 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. Synthetic and system biology Fusion of engineering and biology for introduction in cells of new biological circuits (protein and nucleic acids) by means of processes based on standarization and/or systematization. Utilization of those circuits on multiple cellular models. Design of small components for interaction between molecules. Study from the qualitative and static description of biological processes to the quantitative and dynamic knowledge of a biological system. Fundamentals of development of mathematics models applied to biological systems, such as the construction in silico of a cell for simulation by computer of biological experiments. There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 4 ECTS that will be distributed as 30 class hours, 10 practice hours, and 80 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc.

19 Bioinformatic databases and literature analysis To know the variety of different databases in molecular biology, genomics, proteomics, metabolomics, structural biology, systems biology, etc. Scientific literature algorithms and databases available There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 3 ECTS that will be distributed as 15 class hours, 15 practice hours, and 60 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc. DNA, protein and structure analysis DNA analysis (2.5 ECTS) o Pattern search o Search of coding regions and genes o Analysis of enzyme restriction sites o Search of transcription factors o Automatic analysis of DNA properties o Phylogenetics Protein analysis (2.5 ECTS) o Pattern search o Sequence alignment o Automatic analysis of protein properties Structure analysis (2 ECTS) o Secondary structure analysis o Tertiary and Cuaternary structure analysis o Tools for structure prediction o Tools for docking and fitting There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 7 ECTS that will be distributed as 35 class hours, 35 practice hours, and 140 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving,

20 etc. Interaction networks and array analysis Protein-protein interaction networks Tools for interaction prediction Gene regulation networks DNA arrays analysis Protein arrays analysis There will be 10 class hours and 20 personal work hours for each ECTS. This subject amounts for 5 ECTS that will be distributed as 25 class hours, 25 practice hours, and 100 personal work hours. The class hours will be divided into lectures, guided practices, group discussions, problem solving, etc.

21 3.4.- Practices in external institutions and other external activities Students will visit leading-edge research centers in Madrid to see in situ the application of the techniques studied in the master program. The master thesis can be done in any R&D center or R&D department of a company. Agreements are currently being signed with several top-quality institutions and companies Mobility Students may carry out their master thesis inside San Pablo Univ. or in any of the institutions or companies with an agreement. The duration of the master thesis is of 450 work hours.

22 4.- ORGANIZATION AND MANAGEMENT OF THE PROGRAM Direction board and management procedures Structure and composition of both academic coordination and management board and administrative support: The master will be directed by the coordinator of the program and the two organizers of the master (one of them for the technical contents and the other one for the biotechnological aspects) Management of the academic record and title expedition: - Management of the academic record: It will be managed by the academic Secretary of the centre where the program is developed. - Title expedition: It will be carried out by the Unit of Titles from the San Pablo-CEU University, following the requisites that the Education and Science Ministery established in the corresponding Order (artículo 3.1 del Real Decreto 55/2005, de 21 de enero, BOE), which regulates the structure and control of the university education Management of agreements with collaborating organizations and companies, if appropriate: The Chancellor will sign the agreements with collaborating organizations, following the indications in the article 37, letter c) from the Normes of organization and management of the San Pablo-CEU University (DECRETO 24/2005, de 10 de febrero, del Consejo de Gobierno, por el que se aprueban las Normas de Organización y Funcionamiento de la Universidad San Pablo-CEU. BOCM, nº 43, de 21 de febrero, pp.13 y ss.) Planning and management of the teachers and students mobility: In principle, it is not foreseen the mobility of the teachers in order to the master be performed. Students may carry out stays in any of the institutions or companies with an agreement in order to their master thesis be done Selection and admission Admission board: Structure and functioning. The selection will be performed based on both academic record and professional/research experience of the applicant. Students applying for the entire master will have high priority opportunities. Selection process will be the next: - Students will apply for a place to study the master by sending us their academic record and a professional/research experience report. Applications will be presented before a deadline. - Two weeks after the deadline, the coordinator of the program and the two organizers of the master will carry out the selection process and the results will be notified to the applicants. - Registration will be formalized by the selected students. For those students wishing for studying separately one or more subjects, the deadline will be one month before being given that corresponding subject.

23 Prerequisites and previous skills to be required to students of the master: Students with a bachelor s degree both in science and engineering will might gain admittance to the master. Subsequently, when the European Higher Education Area (EHEA) is introduced, the students with the graduate program passed will might gain admittance to the master. During selection process it will be considered the previous skills of the applicants in order to evaluate if the adaptation module (10 ECTS) provides the students with knowledge enough to understand the field was lacking in their academic background. In case to be considered insufficient, the student will not be selected to study the master System of admission and criterion to gain admittance: The coordinator of the program and the two organizers of the master will value the academic record and the professional/research experience report of the applicants. Students applying for the entire master will have high priority opportunities. In case of applicants with the same merits, application date will be determinant Criterion for the validation of previous qualifications: On request, a committee will evaluate the validation of subjects related to the master that have been already passed by the student.

24 5.- HUMAN RESOURCES Teachers and researchers: Profile/Qualifications (academic category): Federico Abascal Sebastián de Erice Postdoc fellowship Juan José Álvarez Millán Adjunct Professor Encarnación Amusquivar Arias Assistant Professor Riánsares Arriazu Navarro Assistant Professor Coral Barbas Arribas Associate Professor María Concepción Bielza Lozoya Associate Professor Carlos Bocos de Prada Adjunct Professor Pedro Carmona Sáez Predoc fellowship Mónica Chagoyen Quiles Adjunct Professor Ricardo Díaz Martín Adjunct Professor Ana Dopazo Head of Unit Antonia García Fernández Adjunct Professor Concepción Gil García Associate Professor Celia Gutierrez Cosío Assistant Professor Nuno Henriques Gil Associate Professor Gonzalo Herradón Gil Gallardo Assistant Professor Maite Iglesias Badiola Associate Professor Pedro Larrañaga Múgica Professor Sonia López Giral Assistant Professor Víctor de Lorenzo Researcher Roberto Marabini Ruiz Ramón y Cajal Contract Sonsoles Martín Santamaría Ramón y Cajal Contract Juan Alberto Medina Auñón Predoc fellowship Juan Julián Merelo Guevós Associate Professor Modesto Orozco Professor Paola Otero Gómez Adjunct Professor Abraham Otero Quintana Assistant Professor Mª Isabel Panadero Antón Assistant Professor Alberto Pascual Montano Ramón y Cajal Contract Beatriz de Pascual Teresa Associate Professor Florencio Pazos Cabaleiro Researcher Carmen Pérez García Adjunct Professor Juan Poyatos Ramón y Cajal Contract Ana María Ramos González Associate Professor Antonio Rausell Predoc fellowship Pablo Redondo Martín Adjunct Professor Eva Ruiz Casares Assistant Professor Alejandro Sánchez Suárez Associate Professor Carlos Oscar Sánchez Sorzano Adjunct Professor Cruz Santos Tejedor Adjunct Professor María Teresa de Troya Franco Adjunct Professor Oswaldo Trelles Salazar Associate Professor Javier Velázquez Muriel Postdoc fellowship Igor Zwir Research Specialist

25 ECTS distribution: Federico Abascal Sebastián de Erice 2.0 ECTS Juan José Álvarez Millán 1.0 ECTS Encarnación Amusquivar Arias 1.25 ECTS Riansares Arriazu Navarro 0.5 ECTS Coral Barbas Arribas 1.0 ECTS María Concepción Bielza Lozoya 1.5 ECTS Pedro Carmona Sáez 1.5 ECTS Jesús Blázquez Gómez 1.5 ECTS Carlos Bocos de Prada 2.75 ECTS Mónica Chagoyen Quiles 1.5 ECTS Ricardo Díaz Martín 0.75 ECTS Ana Dopazo 1.0 ECTS Antonia García Fernández 1.0 ECTS Concepción Gil García 1.0 ECTS Celia Gutierrez Cosío 2.0 ECTS Nuno Henriques Gil 1.25 ECTS Gonzalo Herradón Gil Gallardo 2.0 ECTS Maite Iglesias Badiola 0.5 ECTS Pedro Larrañaga Múgica 3.5 ECTS Sonia López Giral 0.5 ECTS Víctor de Lorenzo 1.25 ECTS Roberto Marabini Ruiz 2.0 ECTS Sonsoles Martín Santamaría 0.75 ECTS Juan Alberto Medina Auñón 2.0 ECTS Juan Julián Merelo Guevós 1.5 ECTS Modesto Orozco 0.75 ECTS Paola Otero Gómez 2.0 ECTS Abraham Otero Quintana 4.0 ECTS Mª Isabel Panadero Antón 1.75 ECTS Alberto Pascual Montano 5.0 ECTS Beatriz de Pascual Teresa 0.75 ECTS Florencio Pazos Cabaleiro 3.0 ECTS Carmen Pérez García 1.5 ECTS Juan Poyatos Adeva 1.25 ECTS Ana María Ramos González 1.5 ECTS Antonio Rausell 1.0 ECTS Pablo Redondo Martín 0.75 ECTS Eva Ruiz Casares 2.5 ECTS Alejandro Sánchez Suárez 5.0 ECTS Carlos Oscar Sánchez Sorzano 6.5 ECTS Cruz Santos Tejedor 0.5 ECTS Oswaldo Trelles Salazar 5.0 ECTS María Teresa de Troya Franco 2.5 ECTS Javier Velázquez Muriel 0.75 ECTS Igor Zwir 3.5 ECTS

26 6.- MATERIAL RESOURCES Available infrastructures and equipments: Lecture rooms with the necessary equipment in order to the lectures be given and the student s discussions be hold. Web facilities will be also available. Students applying for the entire master will get their own one laptop. Laboratories for the practices. A library containing books and journals about topics related to the master Predictions, if anyone, for getting better infrastructures and equipments: At present, all the necessary facilities are avaliabe.

27 ATTACHED DOCUMENT I PROGRAM STRUCTURE TITLE: Master on Computational Biotechnology LEARNING HOURS MODULE SUBJECT LENGTH 1 TYPE 2 ESPECIALITY ECTS PERSONAL CLASS PRACTICE WORK 1A Adaptation 1 1S1 C A Adaptation 2 1S1 C B Adaptation 1 1S1 C B Adaptation 2 1S1 C Statistical inference, regression and 1S1 C experiment design 2 Multivariate analysis 1S1 C Bayesian networks 1S2 C Neural networks 1S2 C Molecular biology and recombinant 1S2 C DNA technology 3 Sequencing, genotyping and 1S1 C Indicate number of months and in which semester the subject will be given (example: 3 months in the first semester = 3S1) 2 Compulsary (C), Optional (OP)

28 transcriptomics 3 Proteomics 1S1 C Metabolomics 1S2 C Classification and clustering 1S3 C Dynamic Models 1S2 C Associative rules, logic networks and 1S2 C grammars 5 Biotechnology 1S2 C Synthetic and system biology 1S3 C Structural biology and protein 1S3 C engineering 6 DNA, protein and structure 1S3 C analysis 6 Interaction networks and 1S3 C array analysis 6 Bioinformatic databases and literature 1S3 C analysis Master Thesis 2S3 C TOTAL

29 TITLE: Master on Computational Biotechnology ATTACHED DOCUMENT II TEACHERS AND RESEARCHERS NAME AND SURNAME UNIVERSITY/INSTITUTION/COMPANY CATEGORY/CHARGE SUBJECTS ECTS Federico Abascal DNA, protein and structure 1 Centro Nacional Biotecnología (CSIC) Postdoc fellowship Sebastián de Erice analysis Juan José Álvarez Millán Univ. San Pablo CEU Adjunct Professor Metabolomics Encarnación Amusquivar Arias Univ. San Pablo CEU Assistant Professor Adaptation 2 (Biochemistry) 1.25 Riánsares Arriazu Adaptation 1 (Cell Biology and 4 Univ. San Pablo CEU Assistant Professor Navarro Genetics) Coral Barbas Arribas Univ. San Pablo CEU Associate Profesor Metabolomics 1.0 María Concepción Bielza 6 Univ. Politécnica de Madrid Lozoya Associate Profesor Bayesian networks Jesús Blázquez Gómez Centro Nacional Biotecnología (CSIC) Researcher Synthetic and system biology Carlos Bocos de Prada Univ. San Pablo CEU Adjunct Professor Molecular biology and recombinant DNA technology Sequencing, genotyping and 2.75 transcriptomics Biotechnology 9 Pedro Carmona Sáez Centro Nacional Biotecnología (CSIC) Predoc fellowship Interaction networks and array analysis 1.5 Mónica Chagoyen Bioinformatic databases and 10 Univ. Complutense de Madrid Adjunct Profesor Quiles literature analysis Ricardo Díaz Martín Univ. San Pablo CEU Adjunct Professor Biotechnology Ana Dopazo Centro Nacional Investigaciones Sequencing, genotyping and Researcher Cardiovasculares transcriptomics 1.0 Antonia García 13 Fernández Univ. San Pablo CEU Adjunct Professor Metabolomics Concepción Gil García Univ. Complutense de Madrid Associate Profesor Proteomics Celia Gutiérrez Cosío Univ. San Pablo CEU Assistant Professor Adaptation 2 (Abstract data 2.0

30 types) 16 Nuno Henriques Gil Univ. San Pablo CEU Associate Profesor Adaptation 1 (Cell Biology and Genetics) 1.25 Molecular biology and Gonzalo Herradón Gil recombinant DNA technology 17 Univ. San Pablo CEU Assistant Professor Gallardo Sequencing, genotyping and 2 transcriptomics 18 Maite Iglesias Badiola Univ. Francisco de Vitoria Associate Profesor Biotechnology Pedro Larrañaga Múgica Univ. Del País Vasco Professor Bayesian networks Classification and clustering Sonia López Giral Univ. San Pablo CEU Assistant Professor Adaptation 1 (Cell Biology and Genetics) Víctor de Lorenzo Centro Nacional Biotecnología (CSIC) Researcher Synthetic and system biology Roberto Marabini Ruiz Univ. Autónoma de Madrid Ramón y Cajal Contract Adaptation 1 (Databases) 2 Sonsoles Martín Ramón y Cajal Structural biology and protein 23 Univ. San Pablo CEU Santamaría Contract engineering 0.75 Proteomics Juan Alberto Medina 24 Centro Nacional Biotecnología (CSIC) Predoc fellowship Bioinformatic databases and Auñón literature analysis 2 Juan Julián Merelo 25 Guevós Univ. De Granada Associate Profesor Neural networks Modesto Orozco Univ. De Barcelona Professor Structural biology and protein engineering Paola Otero Gómez Univ. San Pablo CEU Adjunct Professor Adaptation 2 (Biochemistry) Molecular biology and recombinant DNA technology Abraham Otero Quintana Mª Isabel Panadero Antón Univ. San Pablo CEU Univ. San Pablo CEU Assistant Professor Assistant Professor Adaptation 1 (Programming) Adaptation 2 (Analysis and design of algorithms) Molecular biology and recombinant DNA technology Biotechnology 30 Alberto Pascual Univ. Complutense de Madrid Ramón y Cajal Neural networks

31 Montano Contract Interaction networks and array analysis Classification and clustering Beatriz de Pascual Structural biology and protein 31 Univ. San Pablo CEU Associate Profesor Teresa engineering 0.75 Florencio Pazos DNA, protein and structure 32 Centro Nacional Biotecnología (CSIC) Researcher Cabaleiro analysis Carmen Pérez García Univ. San Pablo CEU Adjunct Professor Proteomics Juan Poyatos Adeva Centro Nacional Investigaciones Ramón y Cajal Oncológicas Contract Synthetic and system biology Antonio Rausell Centro Nacional Investigaciones Oncológicas (CNIO) Predoc fellowship Multivariate analysis 1.0 Ana María Ramos Adaptation 2 (General 36 Univ. San Pablo CEU Associate Profesor González Chemistry) Pablo Redondo Martín Univ. San Pablo CEU Adjunct Professor Biotechnology Eva Ruiz Casares Univ. San Pablo CEU Assistant Professor Adaptation 1 (Cell Biology and Genetics) Molecular biology and recombinant DNA technology Sequencing, genotyping and transcriptomics Carlos Óscar Sánchez Sorzano Univ. San Pablo CEU Adjunct Professor Adaptation 1 (Statistics) Multivariate analysis Classification and clustering Dynamic Models Statistical inference, regression and experiment design Alejandro Sánchez 40 Suárez Univ. De Barcelona Associate Profesor 5 41 Cruz Santos Tejedor Univ. Francisco de Vitoria Adjunct Professor Biotechnology 0.5 Associative rules, logic networks 42 Oswaldo Trelles Salazar Univ. De Málaga Associate Profesor and grammars DNA, protein and structure 5 analysis 43 María Teresa de Troya Franco Univ. San Pablo CEU Adjunct Professor Adaptation 1 (Microbiology) 2,5 6.5

32 44 Javier Velázquez Muriel Univ. California - San Francisco Postdoc fellowship 45 Igor Zwir Howard Hughes Medical Institute Research specialist Structural biology and protein engineering Dynamic Models Interaction networks and array analysis