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Systems Biology of Diseases and Drug Treatment

The interplay between genetics and environment decide between health and disease. The systems paradigm is an approach to integrate different types of information into a comprehensive concept for a deep understanding of diseases.
Veranstaltungstyp Vorlesung und Übung
Dozent Prof HW Mewes
Zeit Freitag, 08:30–11:00
Turnus wöchentlich vom 20.10.2017 bis zum 09.02.2018
Raum D 102, Richard-Wagner Str. 10, München
Unterrichtssprache Deutsch
Punkte 6 ECTS

Zeitlicher Ablauf: Vorlesung und Übung finden Montags 8:30 bis 10:00 Uhr und Freitags 8:30 bis 11:00 Uhr in Raum D 102 (Richard-Wagner-Str. 10) statt.

Die 1. Vorlesung findet am 20.10.2017 statt. Anmeldung zum Modul über TUMonline!

Inhalt

Soon, the number of sequenced genomes will be larger than 100.000. Will this information have an impact on the future of medicine? What are promises and consequences of a "look-ahead" type of preventive medicine? The very rapidly increasing amount of information overwhelms our capacity for a mechanistic understanding of disease mechanisms. Medical doctors have to translate the information from diagnosis into the best therapy to cure the patient. Unfortunately neither causative - based on the genetics of rare diseases - nor preventive medicine - based on risk assessment for common diseases - is practiced. Traditionally medical practice is driven by personal experience of the doctor, however the analysis of complex information as well as the understanding of disease aetiology reaches far beyond the textbook driven experience. Medical practice today is dominated by intuitive diagnosis supported by imaging, bioanalytics, and individual experience.  The molecular analysis of diseases, however, is based on complex data such as genetics as well as different types of -omics data. The digitalisation of medicine together with a plethora of new insight into the molecular biology of rare and common diseases will have a profound impact on health care and prevention.

While bioinformatics deals with management of data and the transformation from data into information, systems biology is the magic paradigm to translate any biological process into an appropriate model. This is much easier said than done, but as a paradigm, systems biology is an attractive approach to gain insight into the complex reality of living organisms. If the devil is in the detail, how much detail is needed to improve diagnostics and therapy if a doctor has 8 minutes to spend for the patient?

Human well being is a primary wish for all of us despite the fact that there is no lucky outcome ("the game of life is hard to play, you will loose it anyway"; from MASH). Human culture from its beginning, aimed to avoid pain and cure diseases. Medicine developed to the largest economic sector in modern societies. Life expectation constantly increases as a consequence of improved living conditions and medical care. As a consequence of a dramatically changed life-style, the profile of the most frequent diseases has substantially changed. Examples are the increase in lung cancer and obstructive diseases of the lung as a result of smoking in the 20th century and the world-wide epidemic growth of diabetes type II.

Since the blueprint of the human genome was known, an absolute amazing world of molecular information related to human diseases was gathered and published. Insight to molecular mechanisms such as epigenetics and the action of non-coding RNAs were gained based on molecular information, explored by bioinformatics methods. Medical diagnosis based on sequence information entered the clinic. To understand the genotype/phenotype relation, in other words, the impact of genetics and environment on the onset and progress of diseases is the "Grand challenge" in life science research from the model organism up to the clinic.

The availability of highly complex data together with a wide spectrum of diagnostic technologies will fundamentally change the way doctors will treat patients in the future. The interpretation of complex data by the medical personnel will be impossible, but how can the information be used for the benefit of the patient. What will be the role of the data explorer, the bioinformatician or systems biologist? Is a reliable prediction of the outcome of a treatment possible in the near future based on the genotype of the patient?

The lecture will try to introduce into the reality and science fiction of data driven medicine. It will start with some basic considerations on data, causality, complexity and try to give a realistic picture of personalized medicine and prevention. As examples of recent insight based on genetic/molecular evidence in the areas of metabolic diseases, cancer, neurodegenerative and psychiatric diseases will be presented.

 

Topics:

  1. General introduction to the causal interplay of genetics and environment
  2. The classification paradigm of traditional medicine and the paradigm of individualized/systemic medicine
  3. Variation in the human genome (1.000 genomes and many more)
  4. Heritable and rare diseases and their genetic background
  5. Ageing
  6. Metabolic diseases and diabetes
  7. Cancer as the result of somatic mutations
  8. The mechanisms and challenges of drug action / pharmacogenetics
  9. Neurobiology and psychiatric diseases


Guest lectures:

There will be 2-3 guest lectures held by renowned colleagues on animal disease models, genetics in psychiatry, and systems biology of mental diseases.

The successful participation will require active contributions by the students concerning reading key publications, presenting specialized topics, and writing summaries as contributions to the lecture blog. A number of research subjects will be presented by leading experts in genetics, high throughput sequencing, and other cutting-edge technologies.

 

Goals: After the successful participation, students will understand key methods to investigate rare and complex diseases. They will have a better insight into disease mechanisms, in particular in the areas of metabolic, neoplastic and mental diseases.

 

Voraussetzungen

Bachelor Bioinformatik; Grundlagen der molekularen Biologie; Grundlagen der Bioinformatik (z.B. Vorlesung Weiterführende Bioinformatik)

Zielgruppe

Masterstudenten der Bioinformatik, Doktoranden der Biowissenschaften und der Medizin