BIOINFORMATICS AND SYSTEM BIOLOGY (INTERNATIONAL PROGRAM)

Transcription

1 BIOINFORMATICS AND SYSTEM BIOLOGY (INTERNATIONAL PROGRAM) PROGRAM TITLE DEGREE TITLE Master of Science Program in Bioinformatics and System Biology (International Program) Master of Science (Bioinformatics and System Biology) M.Sc. (Bioinformatics and System Biology) PROGRAM STRUCTURE Total program credits 38 credits Plan A2 (Thesis) 1. English Course non credits 2. Seminar 2 credits 3. Compulsory Courses 15 credits 4. Elective Courses 9 credits 5. Thesis 12 credits Total program credits 38 credits Plan B (Independent Study) 1. English Course non credits 2. Seminar 2 credits 3. Compulsory Courses 15 credits 4. Elective Courses 9 credits 5. Independent Study 6 credits 6. Internship 6 credits 1. English Course non credits LNG 601 Foundation English for International Programs 3 (2 2 9) Remarks: Students must enroll LNG 601 and or exempt depend on the conditions of School of Liberal Arts 2. Remedial Courses non credits BIF 511 Programming Fundamentals 3 (2 2 9) 3. Compulsory Courses 15 credits 4. Elective Courses 9 credits 4.1 Bioscience BIF 614 Molecular Evolution 3 (3 0 9) BIF 632 Drug Design and Discovery 3 (3 0 9) BIF 634 Functional and Comparative Genomics 3 (3 0 9) BIF 652 Statistical Methods for Bioinformatics and System Biology 3 (3 0 9) BIF 662 Special Topics I 3 (3 0 9) BIF 664 Special Topics II 3 (3 0 9) BIF 666 Special Topics III 3 (3 0 9) BIF 674 Advanced Biotechnology 3 (3 0 9) BIF 712 Advanced Microbial Physiology 3 (3 0 9) BIF 772 Systems Biology and Metabolic Engineering 3 (3 0 9)

2 4.2 Computer Science BIF 611 Computer Architecture and Organization 3 (3 0 9) BIF 613 Operating Systems 3 (3 0 9) BIF 631 Database Systems for Bioinformatics 3 (3 0 9) BIF 641 Computer Systems Analysis and Design 3 (3 0 9) BIF 643 Software Engineering 3 (3 0 9) BIF 651 Artificial Intelligence 3 (3 0 9) BIF 653 Fuzzy Logic and Neural Networks 3 (3 0 9) BIF 661 Operations Research 3 (3 0 9) BIF 663 Simulation Techniques 3 (3 0 9) BIF 671 Computer Graphics 3 (3 0 9) BIF 677 Special Topic I 3 (3 0 9) BIF 679 Special Topic II 3 (3 0 9) 5. Seminar 2 credits 6. Thesis/Independent Study 6 and 12 credits BIF 696 Special Research Study 6 credits BIF 698 Thesis 12 credits 7. Internship 6 credits BIF 699 Internship 6 credits STUDY PLAN Plan A First Year Remedial courses: BIF 511 Programming Fundamentals 3 (2 2 9) First Semester Total 13 ( ) Total 13 ( ) Second Year First Semester BIF 698 Thesis 6 ( ) BIF 698 Thesis 6 ( ) Plan B First Year Remedial Courses: BIF 511 Programming Fundamentals 3 (2 2 9)

3 First Semester Total 13 ( ) Total 13 ( ) Second Year First Semester BIF 696 Special Research Study 6 ( ) BIF 699 Internship 6 ( ) Remark: *Students who must enroll remedial courses will be considered individually by the curriculum committee. ** Students can enroll elective courses in the curriculum or others with consent of advisors. Students who have bioscience background or other related fields should enroll courses in computer science at least 6 credits and students who have computer science background should enroll courses in bioscience at least 6 credits. ***Students can improve English language skills in class and make presentation in English and/or learn from extra program. Before graduate, students must pass the standard English test as equivalent of TOEFL, at least 500 score. COURSE DESCRIPTIONS LNG 550 Remedial English Course for Post Graduate Students 2 (1 2 6) This course aims to instill the background language and skills necessary for undertaking LNG 600 and to raise the students confidence in using English. There will be no predetermined focus of the course, but instead it will concentrate on those areas where the students are weakest and need most improvement. The classroom teaching and learning will be supported by self-directed learning to allow the students to improve their language and skills autonomously. LNG 600 Insessional English Course for Post Graduate Students 3 (2 2 9) Prerequisite: LNG 550 Remedial English Course for Post Graduate Students or Pass grade from placement procedure This course aims to develop English language skills relevant to mature students in Graduate Degree Programmes in Engineering, Science and Technology. It will be based on practical skills, but will not be yet another grammar course. Rather its focus will be on the real language demands, particularly in reading and writing, faced by students in the course of their studies. It is project-focussed and simulates the stages in preparing and presenting research, from finding references to writing a final draft. The course will equip students with language learning strategies to facilitate ongoing autonomous learning and will emphasise language use not usage, real communication not classroom practice. Microorganisms; prokaryote, eukaryotes, archeabacteria. Virus. Yeast and molds. Cell structure. Microbial metabolism, growth, and nutrition. Microbial genetics. Immunology. Biomolecules and metabolism. Energy metabolism.

4 BIF 511 Programming Fundamentals 3 (2 2 9) General concepts of computer programming, statement, variable, constant, operator, expression, data types, array data structure, program structure: sequence, selection, and repetition; program module; user defined procedure/function; parameter passing, introduction to object oriented programming concept, file operations: sequential file operation, random access file operation; laboratory work: basic programming by using one of programming languages, debugging, testing, and correcting program to solve bioinformatics and systems biology problems and/or others related. Gene organization in prokaryotes and eukaryotes. Molecular mechanisms of protein synthesis. Regulation of gene expression in prokaryote. Transcription and post-transcription process in eukaryotes. Genome dynamics. Gene manipulation. Data structures: arrays, stacks, queues, trees, hash tables, and heaps. Sorting algorithms, searching algorithms, and graph and tree algorithms. Complexities of algorithms. algorithm design techniques. Solving computational problems: divide-and-conquer, dynamic programming, and greedy algorithms. Data structures and algorithms used in solving bioinformatics and system biology problems. Programming practices in implementing various algorithms. BIF 611 Computer Architecture and Organization 3 (3 0 9) Computer systems: process, memory, and I/O modules, plus the interconnections among these major components. Central processing unit: control unit, registers, ALU, and the instruction unit; architectural issues: instruction set design; organizational issues: pipelining, parallel organization. Study of genome, transcriptome, and proteome. Basis of molecular evolution and their applications. Cellular signaling. New or advanced topics in molecular biology and biochemistry. BIF 613 Operating Systems 3 (3 0 9) Operating system architecture, goals, and structure, process management, memory management, secondary storage management, computer security, and an introduction to distributed operating systems. BIF 614 Molecular Evolution 3 (3 0 9) Prerequisite: BIF 612 Molecular evolution and development. Phylogenetic principles. Phylogenetic reconstruction by distance, parsimony, and likelihood method. Molecular clock and speciation. Introduction to the theory and methods of DNA and protein sequence analysis. Methods of sequence alignments including dynamic programming and statistical methods. Methods of phylogenetic analysis, and database similarity searching. Intensive overview of techniques with both theoretical background and hands-on experiences. Biological databases and related computer program will be introduced. Other topics covered are study of genome sequencing and analysis that includes databases, homology and non-homology based annotation, genome sequencing and assembly, RNA sequence and structure analysis, phylogenetic analysis. Study of functional genomics using SNP array, transcriptome and data analysis using R, protein modeling and proteome, etc. For genome scale modeling, pathways construction, regulatory pathway construction and data integration will be discussed. Project will be assigned and student presentation is a part of evaluation. BIF 631 Database Systems for Bioinformatics 3 (3 0 9) File system, database system, database system components and architecture. Data modeling, database design, conceptual, physical and normalization. ER-Model, database languages, SQL and QBE. Introduction to OODB and distributed database. Database systems used in Bioinformatics.

5 BIF 632 Drug Design and Discovery 3 (3 0 9) Prerequisite: BIF 512 Techniques in computer-aided drug design and discovery using computer and information technologies in areas such as searching and analysis of structure and function analysis of biological macromolecules. Analysis of structure function and structure activities. Relationships of physiologically active compounds. Ligand designing and simulation of their interaction with biological macromolecules. Predictions of pharmacological properties of new substances. Molecular graphics and de novo drug design. Introduction to probability theory. Introduction to data mining and knowledge discovery in databases (KDD). Process of Data Mining. Data preparation. Model evaluation. Association rules. Nearest neighbor classification. Naive Bayes classification. Decision tree classification. Neural networks classification. Clustering. Data mining applications in Bioinformatics. BIF 634 Functional and Comparative Genomics 3 (3 0 9) Study of biological processes through genome-wide expression and regulation in organisms. DNA microarrays analysis. Protein-protein interaction and signal transduction. Gene identification and clustering genes into functional groups. Building networks and pathways of interacting genes and gene products. Perspectives on comparative genomics. Genome and sequence comparisons to understand the human genetics and evolution of organisms and genomic responses to the challenges of evolutionary niches. BIF 641 Computer Systems Analysis and Design 3 (3 0 9) System components. SDLC. Analysis methodologies and CASE tools. Technical, operational, and economical feasibility studies. DFD, ERD, input design, output design, database design, documentation and presentation. BIF 643 Software Engineering 3 (3 0 9) Software life cycle. Requirement analysis. Architectural design. Software development for reuse and testing. Software project management. Complexity measurement. CASE tools. BIF 651 Artificial Intelligence 3 (3 0 9) Problems in the domain of AI. Knowledge representation. Means-ends analysis. Uninformed searches. Heuristic searches. Adversarial searches. Game playing. Expert systems. Uncertainty management in expert systems. Fuzzy set theory. Genetic algorithm. Genetic programming. Artificial neural networks. AI applications for Bioinformatics. BIF 652 Statistical Methods for Bioinformatics and Systems Biology 3 (3 0 9) Standard and advanced statistical methods and algorithms used for analysis of the data from high-throughput experiments in genome biology. Probability and statistical theory. Scoring and statistical significance. Data integration. Models and emergent properties finding. The course is project-based, providing hands-on experiences with analysis of genome-wide biological data, group discussion and communication on results. Critical reviews and assessment of the current statistical techniques from literature. BIF 653 Fuzzy Logic and Neural Networks 3 (3 0 9) Prerequisite: BIF 651 Fuzzy sets, fuzzy logic inference operations, neural computing elements, basic learning algorithms, biological neural concepts, neural network taxonomies, applications of neural network in Bioinformatics. BIF 661 Operations Research 3 (3 0 9) Deterministic models: linear programming (dual prices, inventory analysis), networks (Floyd's shortest path, relationships between networks and flows, transportation, and transshipment models), goal programming, integer programming, deterministic dynamic programming, and deterministic inventory models; probabilistic models: forecasting models, discrete simulation techniques and queuing models. BIF 662 Special Topics I 3 (3 0 9) New or advanced topics in Bioinformatics and systems biology. The contents will be specified at the time the course is offered.

6 BIF 663 sluminhlea niitalumis 3 (3 0 9) Defining and modeling simulation. Values of simulation models. Simulation techniques: problem formulation, data collection and analysis, developing simulation models, random number generation, model verification and validation, model experimentation and optimization, implementing simulation results, simulation techniques for Bioinformatics. BIF 664 Special Topics II 3 (3 0 9) New or advanced topics in Bioinformatics and systems biology. The contents will be specified at the time the course is offered. BIF 666 Special Topics III 3 (3 0 9) New or advanced topics in Bioinformatics and systems biology. The contents will be specified at the time the course is offered. BIF 671 Computer Graphics 3 (3 0 9) Hardware and software components; fundamental algorithms for two-dimensional graphics; methodologies for producing basic picture components and techniques for adjusting size, color and other attributes; two-dimensional geometric transformations and viewing algorithms; representations of three-dimensional objects; computer graphics applications in visualization of Bioinformatics data. BIF 674 Advanced Biotechnology 3 (3 0 9) Prerequisite: BIT 376Techniques in Fermentation Process or Consent of the instructor Recent advances in biotechnology with a focus on the development and operation of modern fermentation processes. Strain improvements. Monitoring and control of key environmental parameters. Downstream processing for recovery of fermentation products. Development of biosensors for fermentation monitoring. Applications of biotechnology in the food, agriculture, and medical industries. BIF 677 Special Topics I 3 (3 0 9) New or advanced topics in information technology. The contents will be specified at the time this course is offered. BIF 679 Special Topics II 3 (3 0 9) New or advanced topics in information technology. The contents will be specified at the time this course is offered. Application of knowledge and skills in Bioinformatics to solve problems in the field of biological science and related areas. Review, discussion, invention, analysis, and synthesis of principles and concepts, current problems and literature in bioinformatics and systems biology. Presentations and participation in discussion of others in class. Review, discussion, invention, analysis, and synthesis of principles and concepts, current problems and literature in bioinformatics and systems biology. Presentations and participation in discussion of others in class. BIF 696 Special Research Study 6 credits Application of knowledge and skills in Bioinformatics and system biology to solve problems in the field of biological science and related areas. BIF 698 Thesis 12 credits Analysis and development of an appropriate mathematical, statistical, computing method and use of knowledge and skills in bioinformatics and systems biology for solving biological sciences and related area problems.

7 BIF 699 Internship 6 credits Application of knowledge and skills in bioinformatics and systems biology to analyze and develop an appropriate method for solving biological sciences problems from various bioinformatics laboratories. BIF 712 Advanced Microbial Physiology 3 (3 0 9) Prerequisite: BIF 510, BIF 612 Current and future status in microbial physiology research. BIF 772 Systems Biology and Metabolic Engineering 3 (3 0 9) Principles and methodology of systems biology and metabolic engineering. Studies of biological systems by systematically perturbing them biologically, genetically, or chemically. Monitoring gene, protein, and informational pathway responses; integrating these data; and ultimately, formulating mathematical models that describe the structure of the system and its response to individual perturbations. Introduction of metabolic engineering. Metabolic network reconstruction and analysis. Mathematical and experimental techniques for the quantitative description, modeling, control, prediction of biological processes, and design of metabolic pathways. Applications in strain improvements of biotechnological and agricultural importance, drug discovery, disease gene identification, diagnostic and prognosis.