Basic Statistics Standards in Scienti c Communities I Module 3 Lecture 3 20 109 Spring 2010 Lecture 2 review What properties of hydrogels are advantageous for soft TE What is meant by bioactivity and how can it be introduced What are the two major matrix components of cartilage and how do they support tissue function 2 Topics for Lecture 3 Module 3 so far and Day 3 plan Introduction to statistics con dence intervals t test Standards in scienti c communities general engineering principles standards in synthetic biology standards in data sharing 3 Module progress week 1 Day 1 culture design What did you test BD All rights reserved This content is excluded from our Creative Commons license For more information see http ocw mit edu fairuse Day 2 culture initiation Cells receiving fresh media every 2 days 4 Module day 3 test cell viability Green stain SYTO10 viability Red stain ethidium cytotoxicity Assay readout uorescence Working principle Relative cell permeability 5 Statistics review basics Essential concepts standard deviation s mean sample size n degrees of freedom DOF Normal Gaussian distribution 1 s includes of the data x axis y axis Con dence intervals CI principle 60 sample measured mean 95 CI calculated to be 3 Thus 95 likely that the range 60 3 contains the population true mean exact de nition is subtle 90 CI a where a 3 a 3 a 3 Consider betting example What about n Calculating con dence intervals CI t is tabulated by DOF vs CI DOF n 1 In Excel us TINV function input p value 100 CI 100 Introduction to t test Every statistical test has assumptions asks a speci c question requires human interpretation Some t test assumptions normal distribution cf Mann Whitney test equal variances type 2 in Excel type 3 unequal Question Calculating t test signi cance DOF ttable If tcalc ttable difference is signi cant In Excel us TTEST function Excel returns p value con dence level CL 1 tailed vs 2 tailed test Assignment for report Get live cell count and or live cell percent values for both culture conditions Calculate 95 CI for both means Plot means on bar graph with CI error bars Apply t test to the means For multiple comparisons ANOVA is better Comparing many means requires correction Remember p 0 05 means 1 in 20 false positives Interlude intersection of science and commerce 1 HeLa cells http www colbertnation com the colbert report videos 267542 march 16 2010 rebecca skloot 00 30 3 00 2 Patenting genes Judge invalidates human gene patent NY Times March 2010 Metastasizing patent claims on BRCA1 Genomics May 2010 Thinking critically about module goals Purpose of experiment Local Global All well and good but Can we move beyond empiricism tissue engineering E g broadly useful biomaterials goal control degradability over wide range a lot of chemical calculations later we estimated that the anhydride bond would be the right one Robert Langer MRS Bulletin 31 2006 13 Engineering principles after D Endy D Endy Nature 438 449 2005 Is biology too complex to engineer or does it simply require key foundational technologies Systematic vs ad hoc approach Abstraction software function libraries copy editor vs editor Decoupling architecture vs construction design vs fabrication Standardization screw threads train tracks internet protocols what would we standardize to engineer biology Public domain image Wikimedia Commons 14 Application to synthetic biology Systems D Endy Nature 438 449 2005 Synthetic biology in brief programming cells DNA to perform desired tasks Can I have three inverters artimisinin synthesis in bacteria genetic circuits Devices I need a few DNA binding proteins Abstraction DNA parts devices systems materials processing to avoid unruly structures Decoupling Parts Get me this DNA DNA design vs fabrication rapid large scale Standardization Registry of Standard Biological Parts standard junctions off the shelf RBS etc DNA Image by MIT OpenCourseWare See D Endy Nature 433 449 15 Data standards what and why Brooksbank Quackenbush OMICS 10 94 2006 High throughput methods are data rich Standards for collection and or sharing Reasons shared language human and computer compare experiments across labs avoid reinventing the wheel integration of information across levels Examples MIAME for microarrays Gene Ontology protein functions Who drives standards http www geneontology org scientists funding agencies journals industry Screenshot image captured April 2010 Courtesy of the Gene Ontology Used with permission 16 Lecture 3 conclusions Con dence intervals and t tests are two useful statistical concepts Standardizing data sharing and collection is of interest in several BE disciplines Microarray data See D Endy Nature 438 449 standardized biological parts Next time discussion of standards in TE more about cell viability and microscopy Registry of Standard Biological Parts License CC BY SA This content is excluded from our Creative Commons license For more information see http ocw mit edu fairuse 17 MIT OpenCourseWare http ocw mit edu 20 109 Laboratory Fundamentals in Biological Engineering Spring 2010 For information about citing these materials or our Terms of Use visit http ocw mit edu terms