MIT-OCW Health Sciences & Technology 508/510 Harvard Biophysics 101 EconomicsEconomics, Public Policy, Business, Health PolicyPublic PolicyBusinessHealth.

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MIT-OCW Health Sciences & Technology 508/510 Harvard Biophysics 101 EconomicsEconomics, Public Policy, Business, Health PolicyPublic PolicyBusinessHealth Policy For more info see: 10 AM Tue 27-Sep 2005 Fairchild room 177 Genomics, Computing, Economics & Society

Class outline (1) Topic priorities for homework since last class (2) Quantitative exercise (3) Project level presentation & discussion (4) Sub-project reports & discussion (5) Discuss communication/presentation tools (6) Topic priorities for homework for next class

(1) Topic priorities for homework since last class (a) Your notes at top level and detailed level (b) Followup on the discussion on Thu: What is life? Definitions of random and complex Statistical complexity, replicated complexity Compression algorithms Examples of test cases. (c) Exponential growth xls example

Test cases for bio-complexity Snowflakes Mule Fire Brain-dead cloned beings, parts recreating whole- cells ecosystem - green animals symbionts Plant clippings (soil-dead) symmetry of plants & animals, Fibonacci gas vs crystals complexity function of size Economic systems Cellular Automata, Univ-Turing machines Logistical map Autonomous agents Quantum, crypto randomness, incompressible Chemical vs structural complexity Ideas - Language - memes viruses, DNA computer viruses religion & science memes Collections of ideas & cultural artefacts (books) Static vs dynamic

Meta-definition issues for bio-complexity Static vs dynamic Environmental conditions Density 3 or 4 D Hidden simple processes random seed vs pi functional vs imperative languages (Walter) In/out complexity Stan Miller & origin of life Adjacent possible (Kaufman) Rate of complexity change (4th law?) anthropocentrism biocentrism

Simulations. Permutation statistics. What are random numbers good for?

perl -e "print rand(1);" excel: = RAND() f77: write(*,'(f29.15)') rand(1) Mathematica: Random[Real, {0,1}] Where do random numbers come from? X  {0,1}

Monte Carlo. Uniformly distributed random variates X i = remainder(aX i-1 / m) For example, a= 7 5 m= Given two X j X k such uniform random variates, Normally distributed random variates can be made (with  X  X  X i = sqrt(-2log(X j )) cos(2  X k ) (NR, Press et al. p )NR, Where do random numbers come from really?

Class outline (1) Topic priorities for homework since last class (2) Quantitative exercise (3) Project level presentation & discussion (4) Sub-project reports & discussion (5) Discuss communication/presentation tools (6) Topic priorities for homework for next class

Exponent.xls try r= 0.9, 1.01, 1.1, 1.5, 3, , 4, 4.03 try y(i) =r*y(i-1) (i.e. A3=r*A2 etc.) A3 =MAX(r*A2*(1-A2),0)

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