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Mammals 2.0

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

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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

 Prevalence of chronic conditions as a function of age for the US population 2003

http://www.senescence.info/definitions.html

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Ageing (891 species) Homo sapiens

Heterocephalus glaber

Cebus capucinus

Balaena mysticetus

Jeanne Calment of France (1875–1997)

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Engineering aging Naked Mole ratHeterocephalus

glaber 25 yearsEusocial

(Queen & Workers)Congenital insensitivity to

pain (CIPA)

MouseMus

musculus2.5 years

Candidate gene examples: Trp53, Polg, Hr, Bmi1, Clk1, Sirt1, Ucp2, Hr,

Ercc2, Atm, Foxo1

genomics.senescence.info

Quantitative Trait Distributions

Starr &Taggart. 2003. The Unity and Diversity of Life. 10th Ed. P. 189

acsweb.fmarion.edu/Pryor/bellcurve.htm

Polygenic: A & B .. heterogeneous: A or

B ..

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0 10 20 30 40 50

Normal (m=20, s=4.47)

Poisson (m=20)

Binomial (N=2020, p=.01)

Binomial, Poisson, Normal

Expectation E (rth moment) of random variables X for any distribution f(X)

First moment= Mean variance 2 and standard deviation

E(Xr) = Xr f(X) E(X) 2E[(X-2]

Pearson correlation coefficient C= cov(X,Y) = X-X )Y-Y)]/(X Y)

Independent X,Y implies C perfect anti-correlation =-1.0but C0 does not imply independent X,Y. (e.g. Y=X2)

P = TDIST(C*sqrt((N-2)/(1-C2)) 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

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) pX qn-X np 2npq

(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

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 }

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 {-}