1. 1 Mammals 2.0

2. Humanized mammals A 10 Mb-sized region of.. human chromosome 22 to be stably maintained in mouse embryonic stem (ES) cells, and in mice... functional expression of human genes from the HAC in mice. (Human immune functions) [Note: 300 of these = 1 genome] (Nature Biotech 2000 “Manipulation of human minichromosomes to carry greater than megabase-sized chromosome inserts”) Human liver enzymes for drug toxicity testing CYP2 D6, C9, C19, etc. Human / mouse neural chimeras: (Stem Cells. 2007 “Brain transplantation of immortalized human neural stem cells promotes functional recovery in mouse intracerebral hemorrhage stroke model”) Next step “personalized mammals” For transplants and drug testing

3. 3 Intra-species variation in muscle mass Myostatin nulls show enhanced muscle growth, decreased body fat & atherosclerosis (11-Jun-2009 Endocrine Society, Bhasin, BU) Flex Wheeler

4. Prevalence of chronic conditions as a function of age for the US population 2003 http://www.senescence.info/definitions.html

5. 5 Ageing (891 species) Homo sapiens Heterocephalus glaber Cebus capucinus Balaena mysticetus Jeanne Calment of France (1875–1997)

6. 6 Engineering aging Naked Mole rat Heterocephalus glaber 25 years Eusocial (Queen & Workers) Congenital insensitivity to pain (CIPA) Mouse Mus musculus 2.5 years Candidate gene examples: Trp53, Polg, Hr, Bmi1, Clk1, Sirt1, Ucp2, Hr, Ercc2, Atm, Foxo1 genomics.senescence.info

7. Quantitative Trait Distributions Starr &Taggart. 2003. The Unity and Diversity of Life. 10th Ed. P. 189 acsweb.fmarion.edu/Pryor/bellcurve.htm acsweb.fmarion.edu/Pryor/bellcurve.htm Polygenic: A & B.. heterogeneous: A or B..

8. Binomial, Poisson, Normal

9. Expectation E (rth moment) of random variables X for any distribution f(X) First moment= Mean  variance  2 and standard deviation  E(X r ) =  X r f(X)  E(X)  2  E[(X-  2 ] Pearson correlation coefficient C= cov(X,Y) =  X-  X )  Y-  Y )]/(  X  Y ) Independent X,Y implies C  perfect anti-correlation =-1.0 but C  0 does not imply independent X,Y. (e.g. Y=X 2 ) P = TDIST(C*sqrt((N-2)/(1-C 2 )) with dof= N-2 and two tails. where N is the sample size. Mean, variance, & linear correlation coefficient www.stat.unipg.it/IASC/Misc-stat-soft.html

10. p and q  p  q  q = 1 – p two types of object or event. Factorials 0! = 1 n! = n(n-1)! Combinatorics (C= # subsets of size X are possible from a set of total size of n) n! X!(n-X)!  C(n,X) B(X) = C(n, X) p X q n-X  np  2  npq (p+q) n =  B(X) = 1 Binomial frequency distribution as a function of X  {int  n} B(X: 350, n: 700, p: 0.1) = 1.53148×10 -157 = PDF[ BinomialDistribution[700, 0.1], 350] Mathematica ~= 0.00 = BINOMDIST(350,700,0.1,0) Excel

11. P(X) = P(X-1)  X =  x e -   X!  2  n large & p small  P(X)  B(X)  np For example, estimating the expected number of positives in a given sized library of cDNAs, genomic clones, combinatorial chemistry, etc. X= # of hits. Zero hit term = e -  Poisson frequency distribution as a function of X  {int  }

12. Z= (X-  Normalized (standardized) variables N(X) = exp(-  2 /2) / (2  ) 1/2 probability density function npq large  N(X)  B(X) Normal frequency distribution as a function of X  {-  }