Systems Biology and Medicine

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Systems Biology and Medicine

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study

    Biology, Pre-Medicine

  • Language Level

    Taught In English

  • Course Level Recommendations

    Upper

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

    6
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    4
  • Overview

    TARGET AUDIENCE
    Biomedical Sciences majors

    COURSE OBJECTIVE
     Course aims:
    • Profound insight in the network/multifactorial aspects of disease, health and function, connecting pathology with the molecular world that causes or cures the disease
    • Profound insight in how an integration of molecular, physiological, and computational (ICT) techniques can help understand disease, design therapies and improve biotechnology
    • Profound insight in how functional genomics, systems biology and the integration of environmental data can bring about truly individualized, personalized or cohort-based medicine, and precision biotechnology
    • Efficient introduction to >10 (aspects of) subdisciplines of biomedical research, by use of corresponding data and by discussing the strengths and limitations thereof
    • Insight in the foundations and paradigms of medical, biological and exact sciences and in their interactions culminating in systems biology
    • Capability of using diverse quantitative methods so as to infer relevant conclusions through the analysis of data
    • Acquaintance with a number of relevant computer programs and precise experimental methodologies
    • Ability to formulate testable hypotheses, to use modelling when doing this, as well as to amend the hypotheses critically, all based on a thorough analysis of experimental data in the context of existing scientific knowledge
    • Ability to engage in a critical assessment of the utility and reliability of data and models
    • Ability to analyze critically the state of affairs of the life and biomedical sciences as well as of bioengineering.

    Training line 'Scientific thinking and research’:
    The students will be requested to analyze a number of diseases from the network perspective. Using computer tools and through Jamboree-type discussions students will research literature data. All aspects of this training line will surface in this course with the exception of the learning of laboratory abilities. Training line ‘Bioinformatics’: Retrieval and use of BIG DATA, as well as advanced data mining and analysis will be practiced. Training line ‘Mathematical models’: Virtually all aspects will be addressed; the course will assume that most of these have been met with previously, but ample time and assistance will be given to the students to recapitulate them.

    COURSE CONTENT
    Most diseases are caused by the malfunctioning of networks of our body, more so than by the failure of a single molecule. Likewise, most biotechnological processes fail to be robust or optimal because of networks running awry. It is only in the present century that genomics, functional genomics and systems or network biology have developed sufficiently to bring about a breakthrough in the understanding and therapy of disease. The present course familiarizes its participants with the biological networks that determine the functioning of the human and associated organisms. This extends from intracellular molecular networks to the networking of the human with the microbes in the intestines and on the skin. Metabolic as much as signaling and gene-expression networks are involved. The course also teaches the student how to approach these networks using simple bioinformatics and modelling techniques, downloading and then analyzing data through the wwweb, and arguing in terms of recently discovered principles that determine network functioning. Furthermore the course will provide the students with new insights in (a) a number of important multifactorial diseases such as cancer and obesity/metabolic syndrome/diabetes mellitus, (b) inborn errors of metabolism, (c) infectious diseases and (d) aging diseases such as Parkinson’s, Alzheimer’s and Huntington’s. The course will highlight a number of new methods through which new therapies may be designed, some of which make the use of experimental animals unnecessary. The course will pay considerable attention to personalized medicine and nutrition and to the use of the genome-wide metabolic map therein. The hitherto persistent separation between ‘Nature’ (the genes) and ‘Nurture’ (nutrition, lifestyle and environment) will be removed, as will be the barrier between traditional and modern medicine. The student himself will be enabled to (i) figure out where in a network the best targets are for medicinal drugs or other agents to improve network function, (ii) show why it should be a good idea to target multiple points at the same time and how to determine which, (iii) demonstrate how disease probability can be predicted somewhat from an individual’s genome sequence, (iv) show how this might help physicians to come with individual advice with respect to medicine and nutrition, and (v) experience how functional genomic, physiological, and dispositional information may be integrated.

    TYPE OF ASSESSMENT
    Exam with essay questions (50%) Assignment with report and resulting computer programs (50%): Interpretation of data with data analysis and the construction of a model. Both parts should qualify 5.5/10.

    ENTRY REQUIREMENTS
    Completed first one and a half semester BSc Biomedical Sciences VUA or UvA (including Pathology and statistics) or equivalent.

    RECOMMENDED BACKGROUND KNOWLEDGE
    Interest in application of science in medicine or biotechnology. Interest in those sciences themselves. Interest in the roles of networks.
     

     

Course Disclaimer

Courses and course hours of instruction are subject to change.

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