Biophysics 101 Lectures

From FreeBio

Contents

September

Week 1 : class organization

20-Sep-2005 & 22-Sep-2005 schedule

Week 2 : choose projects; exercise - a quantitative definition of life

27-Sep-2005 & 29-Sep-2005 schedule

October

Week 3 : new energy sources and personalized medicine

4-Oct-2005 schedule

Week 4 : biosphere facts

11-Oct-2005 & 13-Oct-2005 schedule

Week 5 : project literature review

18-Oct-2005 & 20-Oct-2005 schedule

  • 18-Oct no new slides
  • 20-Oct no new slides

Week 6 : metabolic networks and learning perl

25-Oct-2005 & 27-Oct-2005 schedule

References:

  • Crawford,et al. (2005) Annu.Rev.Genomics Hum. Genet. 6:287-312 The Patterns of Natural Variation In Human Genes.
  • Edwards, et al 2002. Genome-scale metabolic model of Helicobacter pylori 26695. J Bacteriol. 184(16):4582-93.
  • Segre, et al, 2002 Analysis of optimality in natural and perturbed metabolic networks. PNAS 99: 15112-7. (Minimization Of Metabolic Adjustment ) http://arep.med.harvard.edu/moma/
  • Emmerling, et. al. 2002. Metabolic Flux Responses to Pyruvate Kinase Knockout in Escherichia Coli. Journal of Bacteriology. 184(1):152-164.
  • Desai RP, Nielsen LK, Papoutsakis ET. Stoichiometric modeling of Clostridium acetobutylicum fermentations with non-linear constraints. J Biotechnol. 1999 May 28;71(1-3):191-205.
  • Ibarra et al. Nature. 2002 Nov 14;420(6912):186-9. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

November

Week 7 : midterm evaluation

1-Nov-2005 & 3-Nov-2005 schedule

  • 1-Nov no new slides
  • 3-Nov no new slides

1-Nov

  • The personalized medicine people met last Saturday (29-Oct) and made much progress, including a tenative outline. They planned to make great use of the Weston and Hood paper[1]. Interest was expressed in Guilt-by-Association genes, which are genes that are co-expressed with some other gene of interest. They can help give an indication of what unknown genes do. G-b-A is one of the many approaches to annotating a genome, and is thus best thought of as something our project would use rather than do.
  • We discussed how the class would manage its compuational tasks. The TFs suggested that the database stuff might be above our programming skills, and that we should get started learning PERL soon. The TFs would maintain the databases of genomes, and students would design the tools to get and manipulate the information they desired.
  • Big demonstration on PERL. The book on the web (which I'm not supposed to link to) should be consulted. The best options for running PERL on our machines seemed come down to getting Cygwin, a UNIX-like environment for windows, or making a live-CD with Ubuntu.

Week 8: 101 uses for 1000 genomes

8-Nov-2005 & 10-Nov-2005 schedule

Ind. Eng. Chem. Res. 2005, 44, 6154-6163 Vunjak-Novakovic D, Kim Y, Wu X, Berzin I, & Merchuk JC. Air-Lift Bioreactors for Algal Growth on Flue Gas: Mathematical Modeling and Pilot-Plant Studies. (Species: Dunaliella parva & tertiolecta)

Biotechnol Bioeng. 2005 Sep 5;91(5):643-8. Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO. In silico design and adaptive evolution of Escherichia coli for production of lactic acid.

J Mol Evol. 2005 Aug;61(2):171-80 Rozen DE, Schneider D, Lenski RE Long-term experimental evolution in Escherichia coli. XIII. Phylogenetic history of a balanced polymorphism.


From Morten's thorough summary of the Nov. 8th lecture...

  • If we had a 1000 diploid human genome sequences, what would we do?
  1. Establish the normal genome sequence - that is which nucleotide is most common at each position. How does this compare with the present Human Genome draft.
  2. Variability map for the human genome - which nucleotide positions are most variable. Creating a variability map for the human genome in which each position is assigned a value representing how much variability there is in nucleotide choice on that position across the 1000 genomes.
    1. How does well characterized 'objects' like: extrons, introns, known transcription factor binding sites, siRNA, and 'junk' DNA etc. map onto the variability map. For instance are extrons primarily in regions of low variability whereas introns are in regions of high variability?
    2. How are DNA segments that are related to human disease mapped onto variability map.
  3. Conservation of amino acid sequence
    1. For all protein coding DNA frames how are amino acid sequences conserved.
      1. Characterize different types of changes of amino acids (i.e. from charged to neutral, large to small etc.)
      2. Are there particular changes (ex.: charged to neutral) that are more frequent than others.
      3. Which amino acids are most conserved
    2. For all disease related protein coding sequences - how well are the amino acids conserved.
    3. How does the frequency of amino acid changing mutations compare between all protein coding sequences compared to those linked to disease? Maybe disease related protein coding sequences are the ones that have high frequency of amino acid changing mutations.
  • Personalized medicine

That is given a personal genome what should we look for.

  1. Determine deviations from the normal sequence.
  2. Categorize the variations
    1. Nucleotide changes in non protein coding regions of genome
      1. Nucleotide changes in known regulatory regions of genome (Transcription factor binding sites, etc.)
      2. Nucleotide changes in 'junk' dna
      3. Known disease causing nucleotide changes (mutations on transcription factor binding sites)- is the mutation dominant or recessive
    2. Amino acid changes in coding regions of genome
      1. Different classes of amino acid changes (charged to non charged)
      2. Changes of amino acid sequence in proteins linked to disease
      3. Known disease causing amino acid mutations - is the mutation dominant or recessive

And some Relevant References:

  • familytreeDNA.com
  • hapmap.org
  • Debbie Nickerson's research-I don't know if a certain papers are more important for us than others, but this website contains a list of select publications, most of which appeared relevant.

Week 9

15-Nov-2005 & 17-Nov-2005 schedule

  • Matthew's minutes for Nov. 15:
    • Introductory comments
      • Shawn: The wishy/washy time will be replaced with short, 10-minute PowerPoint presentations each class meeting. They will report on tasks that are more standardized. Preparing to teach is a good way to learn. They will start next Tuesday, and more details will follow. Reminder that low-level contributions to project are code, and high-level contributions (what we're presenting in class) are English descriptions of code. We will be divided into pairs to complete programming tasks.
      • Lax: One of the goals is to get us all up to speed on basic terminology and concepts.
      • Sasha: Final papers will be due some time in January, but we need to have hit substantial milestones by the end of December.
      • Jeff: We need to address cross-platform issues with PPT presentations in class.
    • Individual updates
      • Morten: Read through papers on alkane-producing bacteria. One group found a bacterium that secretes alkanes into the medium. Goal is to identify the cellular machinery that does the excreting. Hydrocarbons are long (~20 atoms) and are not very volatile. Papers were light on biochemistry details.
      • Matthew: Began to write code for compression/decompression of DNA sequence. Had difficulty with Perl pack and unpack functions. Sasha pointed out that task was to use file I/O, not screen I/O.
      • Chiki: Reviewed paper on future implications of the HapMap. HapMap is a study of SNPs in 269 individuals worldwide. Papers suggestes using Medical Resquencing (MRS) targeted to 20,000 coding regions.
      • Mark: Revied HapMap paper. Also reviewed ENCODE, the goal of which is to annotate functional regions of the genome.
      • Jeffrey: Using ActivePerl, and downloaded MSI to use with it. Strongly suggests installing MSI for easy uninstall. Wrote a completely random DNA generator. Reviewed NYT article of a new acne drug that requires an enzyme test for side effects. Morten pointed out that determining a dose requires sequencing a patient's p450 genes, which are the genes that metabolize the drug. Jeffrey also reviewed "Funding Fairness," which points out the inherent incentive problems in government funding of scientific reserach. The 1980 Bayh-Doyle act, which allowed organizations to retain ownership of government-sponsored projects and keep the results private, is outdated, and the NIH today requires grant recipients to share their results. After a brief discussion, we determined that the government is (and should be) trying to maximize the cost-effectiveness of medicine. Sasha pointed out that there is a real business problem of funding personalized medicine. He says we need a model that provides economic or academic incentives to develop personalized medicine.


  • Notes for Nov 17
    • Discussion include of NYTimes article on "Boutique Doctors" lexis-nexis pdf

Week 10

22-Nov-2005 schedule

Week 11

29-Nov-2005 & 1-Dec-2005 schedule

December

Week 12

6-Dec-2005 & 8-Dec-2005 schedule

Week 13 : presentations

13-Dec-2005 & 15-Dec-2005 schedule

Week 14 : wrap-up

20-Dec-2005 schedule