Summary: Syllabus Introduction To Systems Biology

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• 0 Preface

• What led to the devolopment of subdisciplines as bioinformatics and systems biology?

  The introduction of key measuring techniques, such as:
  

  • genome sequencing
  • measurement of mRNA
  • measurement of protein and metabolite concentration
  • fluorescence microscopy

  --> molecular understanding of the cell.

• 1 What is systems biology?

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• Especially in biology, many disciplines exist, such as genetics, molecular biology, biophysics, biochemistry, microbiolgy, cell biology, bioinformatics and systems biology. How do they differ? And why do we need them?

  • They differ because they use different methods and ask different questions
    
  • Together they achieve understranding of biological systems: they are reliant on one another and breaktroughs in one discipline influence developments in others.

• Why is systems biology an interdisciplinary field?

  Approaches from other discilpines are very useful for understanding molecular networks. Main cross-feeding disciplines: mathematics, computer science, control engineering and physics.

• Name 7 approaches that characterize the major part of systems biology research.

  • Dynamics of molecular networks
    
  • Quantitative measurement
  • Understanding the design of molecular networks (functions")
  • Mathematical modelling
  • Theory development
  • Evolutionary aspects
  • Molecular-system constraints, limits and trade offs.
    

• Define natural selection (from a systems approach).

  From a systems approach, proteins or protein complexes can also be susceptible to natural selection.
  
  = pressure force that provides advantage to species to survive and propogate.

• Define fitness. Name a molecular example.

  Suitability of species to the system. 
  
  Example = codon bias:
  

  • differences in the frequency of occurrence of synonymous codons in coding DNA 
  • some codons prefer an amino acid: some are encoded by only one codon, others by 3/4... E.g. Ribosome binding could change --> one codon could be more fit than the other 
  • --> influences the performance of the system

• What is the main reason for using a systems approach?

  A systems approach allows you to analyze a system beyond 1 or 2 of the involved proteins: you can study networks.

• 2 Top-down systems biology

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• Why is distance not a fixed concept?

  Think of a data cloud that is U shaped, with sample 1 at the top left of the U shape and sample 2 on the top right. You could say: the distance is a straight line (shortest path) between the two samples = Euclidian distance. Or you could say that the data suggests otherwise; the path is visible in the data cloud and is thus U -shaped. 
  
  --> you have to think about what concept of distance is used!

• Below you see an example of two aligned protein sequence fragments, and we are interested in their evolutionary distance. LIALDWFNGNRSLVALDWFSGNRS Can you think of reasons why just using the number of differences as a distance measure is probably not very relevant or accurate?

  Some mutations are more likely to occur than others. These more likely mutation events require less time to aquire than mutations that occur less likely. For example, mutation of isoleucine to valine (both hydrophobic amino acids) or vice versa is more likely to occur than amutation from an aromatic amino acid like tryptophane to a charged amino acid like glutamate.

• A program like BLASTp uses the BLOSUM62 scoring table to infer something about evolutionary distance between proteins ( or organisms from which these proteins originate). Which effects does BLOSUM62 account for compared to just counting numbers of differences?

  The BLOSUM68 matrix gives a higher score to a mutation that is unlikely to occur than to a mutation that is likely to occur. If we use these scores to measure distance we compensate for the effect described above.