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Invited Talk: – 5 Invited Talk: – 5 Presentation Transcript

  • Bioinformatics: A New Vision for Academics 2 nd International Conference on Frontiers of Information Technology (FIT) 2006 Islamabad Pakistan December 20 - 21, 2006 Mudasser F. Wyne, Ph.D. Computer Science University of Michigan - Flint Computer Science Department Email: mfwyne@umflint.edu
  • Bioinformatics??
    • Classically known
      • Computational biology
    • Definition
      • The storage and analysis of biological data using certain algorithms and computer software
      • The application of tools of computation and analysis to the capture and interpretation of biological data
    • Emphasis
      • Use of computer and statistical methods to understand biological data
  • Bioinformatics
    • A multifaceted discipline
      • Biology
        • Genetics and microbiology
      • Biochemistry
      • Mathematics
      • Statistics
      • Computer science
        • AI, Database, Algorithms, Pattern recognition
  • Bioinformatics
    • Bioinformatics is a new and rapidly evolving discipline that has emerged from the fields of experimental molecular biology and biochemistry, and from the artificial intelligence, database, pattern recognition, and algorithms disciplines of computer science.
  • Why Bioinformatics?
    • Gene information at NCBI doubles every 14 months
    • Computer scientists are needed to
      • Analyze
      • Index
      • Represent
      • Model
      • Display
      • Process
      • Mine
      • Search
  • Why a new Program?
    • Non of the traditional programs serve the needs of the new demand
    • Fundamental challenge
      • Life science curricula have historically shared very little common ground with computer and IT curricula
      • Traditional science programs in Computer Science and Biochemistry
    • Information systems are the lifeblood of the new century
      • A positive feedback cycle where the need for new genomics and biomedical discoveries
      • Push the development of information systems
  • Why a new Program?
    • Largely because of the inherently interdisciplinary nature of bioinformatics research, academia has been slow to respond to strong industry and government demands for trained scientists to develop and apply novel bioinformatics techniques to the rapidly growing freely available repositories of genetic data.
  • Another Reason
    • Decrease in enrollment in computer science programs
      • USA
        • From 2000 the drop is around 35 to 50%
        • Labor statistics forecast job growth 20% to 50% by 2012
      • UK
        • From 2000 the drop is around 20% to 35%
        • Electronics and Electrical engineering enrollment increased
      • Europe
        • Enrollments decreasing and jobs are rising
  • Interdisciplinary or Multidisciplinary
    • Multidisciplinary
      • Involvement of several different professional areas
      • Not necessarily in an integrated manner
    • Interdisciplinary
      • Integration of multidisciplinary knowledge across a central program theme or focus
      • Creates knowledge that is more holistic than knowledge built in discipline specific studies
  • Why Not Graduate Degree?
    • Entrance barriers for these programs are high,
      • Significant amount of prerequisite knowledge in the fields of biochemistry and computer science
    • B.Sc. Computer Science
      • Remedial courses in chemistry, biochemistry, molecular biology and genetics
    • B.Sc. Biology
      • Remedial courses in programming, data structure, database and artificial intelligence
  • Why Not Graduate Degree?
    • I will present an undergraduate-level bioinformatics curriculum in computer science designed for the baccalaureate student.
    • This program is designed to be tailored easily to the needs and resources of a variety of institutions.
  • Variations
    • A challenge to discover
      • Mostly offered as subprograms or specializations of existing programs
    • Several terms
      • Bioinformatics
      • Medical informatics,
      • Informatics,
      • Health informatics,
      • Computational biology
    • Most programs have started in just the last 3 years and have not been entered into a program directory.
  • Web Search
  • School Statistics 25 9 TOTAL 2 Schools with an undergraduate course in Bioinformatics 1 1 Schools with Graduate tracks in Bioinformatics 1 1 Schools with Doctorial degrees in Bioinformatics 9 3 Schools with Masters degrees in Bioinformatics 1 Schools with undergraduate tracks in 14 1 Schools with undergraduate degrees in Bioinformatics National Michigan
  • Structural Learning Model
    • Pre-Structural Level
      • Learner is distracted by an irrelevant aspect belonging to a previous mode
    • Uni-Structural Level
      • Learner focuses on a relevant discipline
    • Multi-Structural Level
      • The learner acquires knowledge is several disciplines
      • But does not integrate them
    • Relational Level
      • Learner integrates knowledge from several discilpines around a central theme
  • Program Flow -1 Bioethics (3) Biostatistics (3) Bioinformatics (3) Adv. bioinformatics (3) Paper/ Project (3) Genomics (3-4) Genetics (4) Cell & Molecular Biology (4) General Biology II (4) General Biology I (4) MTH121 (4) MTH122 (4) MTH321 (3) DS & AA (3) Object Oriented Prog. (4) Organic Chemistry II (4) Organic Chemistry I (4) General Chemistry I (4) General Chemistry II (3) Prog. (4) Physics II (4) Physics I (4) Database Design (3) AI (3) Data Mining (3)
  • Program Flow -2 Bioethics (3) Biostats (3) Statistics (3) Bioinformatics (3) Adv. Bioinfor. (3) Paper/ Project (3) Genomics (3-4) Cell & Molecular Biology (4) Genetics (4) General Biology II (4) General Biology I (4) MTH121 (4) MTH122 (4) MTH321 (3) DS & AA (3) OO Prog. (4) BioChemistry (3) Organic Chemistry II (4) Organic Chemistry I (4) General Chemistry I (4) General Chemistry II (3) Prog. (4) Database Design (3) AI (3) Data Mining (3) VB (3) ICS (2) SW (1)
  • Sample Course Contents
    • Bioinformatics
      • Applications of computer-based tools to the representation, annotation, and analysis of DNA and protein sequences.
      • Computer lab-oriented exercises employing software packages used to evaluate structural, functional, and evolutionary characteristics of animal and microbial genomes.
    • Topics
      • Fundamentals of Gene Structure and Function, Molecular Biology Databases, Information Retrieval, Database Searches, Molecular Sequence Analysis Tools, Gene Identification and Protein Structure-Function Predictions. Computational analysis of DNA data; Introduction to bioinformatics database using Pearl and SQL; configuration of UNIX workstations for bioinformatics analyses.
  • Sample Course Contents
    • Adv. Bioinformatics
      • This course presents an algorithmic focus to problems in computational biology. Problems and solutions covered in this course include Gene Hunting, Sequence Comparison, Multiple Alignment, Gene Prediction, Trees and Sequences, Databases and Rapid sequence analysis.
    • Also known as:
      • Bioinformatics, Computational Bioinformatics, Principles of Bioinformatics II , Advanced Bioinformatics Computing,
  • Sample Course Contents
    • Bioethics
      • Discussions of major ethical questions having arisen from research in genetics, medicine, and industries supported by this knowledge.
    • Topics
      • General scientific ethics, biological research ethics, governmental and research policy issues.
      • Course work I primarily reading and writing
  • Credit Hours
    • Credit Hours
    • Maths: 11
    • Biology: 19 +3 (Biostatistics) + 3 (Bioinformatics)
    • Chemistry: 18
    • CS: 23 + 3 (Project)
    • Philosophy 3 (Bioethics)
    • Total hours: 83
  • Accreditation
    • This model program can be modified to meet CSAB requirements
      • ABET
      • Computer science topics at least 40 semester hours
      • Mathematics and science at least 30 semester hours
      • Humanities, social sciences, arts and other disciplines that serve to broaden the background of the student at least 30 semester hours.
  • QUERIES
  • 2nd International Workshop on Web-Based Learning: Innovation and Challenges  (WeLearn-2007) Amman, Jordan, 17–20 April 2007
    • Aims and Scope
    • The focus of this workshop is on web based issues in both teaching and learning. We would also like to stimulate interest in this area, expose natural collaboration among the participants, inform the larger research community of the interest and importance of this topic and create a permanent forum for evaluating innovations and relevant research activities.
    • Paper submission deadline: January 20, 2007.
    • Send you submissions to [email_address] .