David Goodsell

GtL Workshop B: Experimental Technology Development and Integration Tue at 2 PM Co-Chairs – George Church, Harvard Medical School Ham Smith, Institute for Biological Energy Alternatives As we attempt to understand, protect, and/or engineer environmental microbial communities, we need to ask what sorts of data would most benefit our models and how to obtain these cost- effectively. For this session let us answer what small (or large) technological step are we taking toward these specific challenges The framework for the discussions will be the following questions:  What are the most useful technologies for our tasks/goals now and for the future? What are the major technological gaps that will need to be addressed to reach the GTL goals? To what extent will the technologies be developed by others?  How can technologies best be used to complement each other and strengthen the resulting research/insights? How do we promote the kind of synergistic interactions among the practitioners? We would like to invite you to bring one viewgraph to share with the participants on your views about technologies needed to meet these challenges.

GtL Workshop B: Experimental Technology Development and Integration Tue at 2 PM Specific challenges: (1) Microscopic methods capable of tracing the chain of a small genome? (2) Quantitation of “all” peptide states (either in single cells or populations)? (3) Sequencing at Mbp per $? (4) Automated designed genome engineering? Discussions leaders: (1) Joachim Frank (Wadsworth Center, NY Dept of Health) on Cryo-Electron Microscopy (1) Hoi-Ying Holman (Berkeley Lab) on FTIR imaging (1) Steve Colson (PNNL) on optical imaging (2) Bob Hettich or Greg Hurst (ORNL) and Dick Smith (PNNL) on Mass spectrometry, (3) George Church (HMS) Polony sequencing (4) Ham Smith (IBEA) Genome Synthesis We would like to invite you to bring one viewgraph to share with the participants on your views about technologies needed to meet these challenges.

DNA RNA Proteins Metabolites Replication rate Environment Biosystems Integrating Measures & Models Microbes Cancer & stem cells Darwinian optima In vitro replication Small multicellular organisms RNAi Insertions SNPs interactions

Improving Models & Measures Why model? “Killer Applications”: Share, Search, Merge, Check, Design

The issue is not speed, but integration. Cost per 99.99% bp : Including Reagents, Personnel, Equipment/5yr, Overhead/sq.m Sub-mm scale : 1  m = femtoliter ( ) Instruments $2-50K per CPU Why improve measurements? Human genomes (6 billion) 2 = bp Immune & cancer genome changes >10 10 bp per time point RNA ends & splicing: in situ bits/mm 3 Biodiversity: Environmental & lab evolution Compact storage 10 5 now to bits/ mm 3 eventually & How ? ($1K per genome, bits/$ )

Projected costs determine when biosystems data overdetermination is feasible. In 1984, pre-HGP (  X, pBR322, etc.) 0.1bp/$, would have been $30B per human genome. In 2002, (de novo full vs. resequencing ) ABI/Perlegen/Lynx: $300M vs. $3M 10 3 bp/$ (4 log improvement) Other data I/O (e.g. video) bits/$

Why single molecules? Integration from cells/genomes/RNAs to data Geometric constraints : Who’s “in cis” on a molecule, complex, or cell. e.g. DNA Haplotypes & RNA splice-forms

Polymerase colonies (Polonies) along a DNA or RNA molecule

A’ B B B B B B A Single Molecule From Library B B A’ 1st Round of PCR Primer is Extended by Polymerase B A’ B Polymerase colony (polony) PCR in a gel Primer A has 5’ immobilizing Acrydite Mitra & Church Nucleic Acids Res. 27: e34

Hybridize Universal Primer Add Red (Cy3) dTTP. Wash. Add Green (FITC) dCTP Wash; Scan BB’ 3’5’ A G T. T C BB’ 3’5’ G C G.. C Sequence polonies by sequential, fluorescent single-base extensions

$1K per diploid human sequence Input: Buccal cells, blood, or forensic samples. Output: Prioritized list of deviant bps (e.g. non-conservative). Raw data rate: 16 pixels/bp, 1Mpixel per 6sec/CPU = 24 CPU days. Amortization: 5 yr for $50K total = $200 per bp Overhead: $200 /sq ft/yr * 40 sq.ft (400 cu.ft) = $40 Reagents: At 20  m per (5  m) polony and 40 bp reads means cm 2 area, 800 ml of fluor dNTP, $100/mg = $40 5 ml PCR reactions = $200 Disposables: 500 slides = $50 Electricity: 2 kwatts 24hr*24days* 0.13$/kwatt-hr = $150 Labor for repair: 10% of instrument cost = $10 Labor for operation: Slide PCR, slide dips, scans, etc. = $20 R&D: Initially NIH grants (roughly 10%).

Inexpensive, off-the-shelf equipment MJR in situ Cycler $10K Automated slide fluidics $4K Microarray Scanner $26K+

Human Haplotype: CFTR gene 45 kbp Rob Mitra Vincent Butty Jay Shendure Ben Williams

Quantitative removal of Fluorophores Rob Mitra

Template ST30: 3' TCACGAGT Base added: (C) A G T (C) (A) G (T) C (A) (G) T C A 3' TCACGAGT AGTGCTCA Sequencing multiple polonies Rob Mitra

Mutiple Image Alignment Metric based on optimal coincidence of high intensity noise pixels over a matrix of local offsets (0.4 pixel precision) Shendure

Polony exclusion principle & Single pixel sequences Mitra & Shendure

Polony Flavors 1.Replica Plating of DNA images [Mitra et al. NAR 1999] 2.Long Range Haplotyping [Mitra et al. PNAS 2003] 3.Allelic mRNA Quantitation (HEP) [Mitra et al. 2003] 4.Alternative Splicing Combinatorics [Zhu et al. 2003] 5.Precise SNP-mutant & mRNA ratios [Merrill et al. 2003] 6.Fluor in situ Sequencing (FISSEQ 1) [Mitra et al. 2003] 7.Multiplex Genotyping (ApoE, Hyman, Shendure & Williams) 8.In situ / single-cell extensions of the above (Zhu & Williams)

Synthetic Mini-genomes 90kbp genome? All 3D structures known. Comprehensive functional data too. 100X faster replication (10 sec doubling) & selection to evolve widgets & systems? Utility of mirror-image & other unnatural polymers. Chassis & power supply

A 90 kbp mini-genome

The in vitro assembly (& 3D structure) of the prokaryotic ribosomes is known. (e.g. Nomura et al.; Noller et al.)

M DNA Template RNA Transcript All 30S-Ribosomal-protein DNAs & mRNAs synthesized in vitro Tian & Church

His-tagged ribosomal proteins synthesized in vitro RS-2,4,5,6,9,10,12,13,15,16,17,and 21 as original constructs. RS1 required deletion of a feedback motif in the mRNA. RS-3, 7, 8, 11, 14, 18, 19, 20 are still weakly expressed. Note that S1, S4, S7, S8, S20, L1, L4, L10 are known to repress their own translation (and are likely titrated by rRNA). Tian & Church

David Goodsell

Set of N coordinates x y z Matrix of distances SVD (singular value decomposition) Euclidean Metric pdb file (viewed with RasMol) Matlab visualization Representations of the Chromosome

Bidirectional replication Paired fork

Origin Blue: Left replicated segment (yelgr=high gene#) Red: Right (i.e. middle) segment Aqua: unduplicated segment of the circular genome Avoidance of entanglement throughout cell cycle