1. Introduction to Synthetic Biology 423 2013 Herbert Sauro hsauro@u.washington.edu www.sys-bio.org

2. Gene and Genomes

3. Smallest Genome – was in 1999 One of the smallest Genomes: Mycoplasma genitalium (Small parasitic bacterium) } Single Gene 3

4. Smallest Genome Total genes: 521 Protein coding genes: 482 tRNA and rRNA: 39 This genome is of interest to synthetic biology because Craig Venter wants to use this organism as the basis for a minimal organism for genetic engineering. Venter’s group has removed roughly 101 genes and the organism is still viable, the idea then is to patent the minimal set of genes required for life. PNAS (2006) 103, 425--430 4

5. Gene Function The complexity of simplicity Scott N Peterson and Claire M Fraser Genome Biol. 2001;2(2):COMMENT 2002. Epub 2001 Feb 8. 5

6. But the real prize goes to…. 160-Kilobase Genome of the Bacterial Endosymbiont Carsonella Symbiont of sap sucking PSYLLIDS or ‘jumping plant lice’ ~182 genes The 160-Kilobase Genome of the Bacterial Endosymbiont Carsonella Atsushi Nakabachi, Atsushi Yamashita, Hidehiro Toh, Hajime Ishikawa, Helen E. Dunbar, Nancy A. Moran, and Masahira Hattori (13 October 2006) Science 314 (5797), 267. Endosymbiont : organism that lives in another cells. 6

7. Prokaryotic Cells: E. coli 2-3 um http://www.ucmp.berkeley.edu/bacteria/bacteriamm.html 1.Bacteria lack membrane bound nuclei 2. DNA is circular 3. No complex internal organelles 7

8. Prokaryotic Cells: E. coli http://atlas.arabslab.com 8

9. Comparison to Eukaryotic Cells http://www.cod.edu/people/faculty/fancher/ProkEuk.htm 9

10. E. coli Cytoplasm David S. Goodsell (Scripps) Average spacing between proteins: 7 nm/molecule Diameter of a protein: 5 nm 10

11. E. Coli Statistics David S. Goodsell (Scripps) Length: 2 to 3 um Diameter: 1 um Generation time: 20 to 30 mins Translation rate: 40 aa/sec Transcription rate: 70 nt/sec Number of ribosomes per cell : 18,000 Small Molecules/Ions per cell: Alanine: 350,000 Pyruvate: 370,000 ATP: 2,000,000 Ca ions: 2,300,000 Fe ions: 7,000,000 Data from: http://bionumbers.hms.harvard.eduhttp://bionumbers.hms.harvard.edu http://redpoll.pharmacy.ualberta.ca/CCDB/cgi-bin/STAT_NEW.cgi 11

12. E. Coli Statistics E coli has approximately 4300 protein coding genes. Protein abundance per cell: ATP Dependent helicase: 104 LacI repressor: 10 to 50 molecules LacZ (galactosidase) : 5000 CheA kinase (chemotaxis): 4,500 CheB (Feedback): 240 CheY (Motor signal): 8,200 Chemoreceptors: 15,000 Glycolysis Phosphofructokinase: 1,550 Pyruvate Kinase: 11,000 Enolase: 55,800 Phosphoglycerate kinase: 124,000 Krebs Cycle Malate Dehydrogenase: 3,390 Citrate Synthase: 1,360 Aconitase: 1630 Source: Protein abundance profiling of the Escherichia coli cytosol. BMC Genomics 2008, 9:102. Ishihama et al. 12

13. E. Coli Statistics E coli has approximately 4300 protein coding genes. Protein abundance per cell: ATP Dependent helicase: 104 LacI repressor: 10 to 50 molecules LacZ (galactosidase) : 5000 CheA kinase (chemotaxis): 4,500 CheB (Feedback): 240 CheY (Motor signal): 8,200 Chemoreceptors: 15,000 Glycolysis Phosphofructokinase: 1,550 Pyruvate Kinase: 11,000 Enolase: 55,800 Phosphoglycerate kinase: 124,000 Krebs Cycle Malate Dehydrogenase: 3,390 Citrate Synthase: 1,360 Aconitase: 1630 Source: Protein abundance profiling of the Escherichia coli cytosol. BMC Genomics 2008, 9:102. Ishihama et al. 13 Molecules Numbers in Prokaryotes: 1.Ions Millions 2.Small Molecules 10,000 – 100,000 3.Metabolic Enzymes 1000 – 10,000s 4.Signaling Proteins100 – 1000s 5.Transcription Factors 10s to 100s 6.DNA 1 – 10s

14. Circular Chromosome in E. coli Most Prokaryotic DNA is circular. Gene are located on both strands of the DNA. Genes on the outside are transcribed clockwise and those on the inside anticlockwise. E. coli’s genome is 4,639,221 base pairs Coding for 4472 genes, of which 4316 are genes that code for proteins. 14 Proteins4316 tRNAs89 rRNAs22 Other RNAs64

15. Circular Chromosome in E. coli 88% of the E. coli genome codes for proteins, the rest includes RNA coding, promoter, terminators etc. In contrast, the Human genome: 3,000,000,000 base pairs and about 25,000 genes. Only 2% of the Human genome codes for proteins. The rest is……RNA regulatory network? Human genes are also segmented into Exon and Introns, with alternative splicing, significantly increasing the actual number of protein 15

16. EcoCyc: http://ecocyc.org/ 16

17. E. coli Gene Structure Page 134 Start codon Stop codon (TAG, TAA, TGA)

18. RNA Polymerase Binds to Promoters http://mgl.scripps.edu/people/goodsell/pdb/pdb40/pdb40_1.html mRNA Changes in the promoter sequence can change the efficiency of RNA polymerase binding to the DNA. The promoter is therefore a site which can be engineered. Changes in the promoter sequence can change the efficiency of RNA polymerase binding to the DNA. The promoter is therefore a site which can be engineered.

19. Strong and Weak Promoters TTGATA -- 16 -- TATAAT TTGACA -- 17 -- TATAAT Strong Promoter. The recA promoter is a strong promoter. CTGACG -- 18 -- TACTGT TTGACA -- 17 -- TATAAT Weak Promoter. The araBAD promoter is a weak promoter. It differs from the averaged promoter sequence by one nucleotide and on base pair in the spacer region. Most common Promoter (Consensus sequence) The strength of a promote is one of the factors which determines the rate of transcription.

20. RNA Polymerase Stops at a Terminator Changes in the terminator sequence can change the efficiency of RNA polymerase stopping. If the gene is part of an operon, terminators can modulate relative expression levels of the different genes in the operon. The terminator is therefore a site which can be engineered. Changes in the terminator sequence can change the efficiency of RNA polymerase stopping. If the gene is part of an operon, terminators can modulate relative expression levels of the different genes in the operon. The terminator is therefore a site which can be engineered.

21. Operon Structure Gene AGene BGene C TerminatorPromoter 100%60%30%

22. Operators – Regulating Expression

23. Gene Regulation lac Operon Promoter Operator Metabolic Enzyme (output) Sugar in MediumRelative β- galactosidase Glucose1 Glucose + lactose50 Lactose2500 lacZ codes for β-galactosidase. lacY codes for β-galactoside permease.

24. Gene Regulation lac Operon Lac repressor Promoter Operator Metabolic Enzyme (output)

25. Gene Regulation lac Operon

26. LacI Repressor lacI is a tetramer (x4)

27. LacI binding to Promoter

28. Ribosome Binding Sites

29. In summary: GeneTerminatorRBSPromoter Operators Start Codon Stop Codon 5’-UTR

30. This course is about networks: The Science and Engineering of Biological Networks

31. The world is full of networks WWW SocialRoad Electronic

32. Biological Networks

33. Metabolic Networks Metabolic About 1000-1400 genes that code for metabolic enzymes in E. coli (out of a total of about 4300 genes)

34. Protein-Protein Networks Protein Signaling Network

35. Protein-Protein Networks Protein Signaling Network: CellDesigner Kohn MIMS 20% of the human protein-coding genes encode components of signaling pathways, including transmembrane proteins, guanine- nucleotide binding proteins (G proteins), kinases, phosphatases and proteases.

36. Protein-Protein Networks C

37. Genetic Networks Gene Regulatory Networks: BioTapestry

38. Genetic Networks Gene Regulatory Networks: BioTapestry : Ventral Neural Tube in Vertebrate Embryo

39. Genetic Units Understanding the Dynamic Behavior of Genetic Regulatory Networks by Functional Decomposition. William Longabaugh and Hamid Bolouri Curr Genomics. Author manuscript; available in PMC 2007 December 12. Published in final edited form as: Curr Genomics. 2006 November; 7(6): 333–341.

40. Hybrid Network: Cell Cycle Control is Bacteria

41. Two Kinds of Representations 1.Non-Stoichiometry – or ball and stick networks No stoichiometry, kinetics or mass conservation 2. Stoichiometry – reaction maps ?? – Stuff that people make up, whose knows what they really mean Cytoscape: Ball and Stick Stoichiometric

42. Network Classification NetworksStoichiometricElementaryNon-Elementary Non- Stoichiometric Probabilistic Ball and Stick (Data dependent)

43. Systems and Synthetic Biology Systematic Biology Synthetic Biology Network Physiology Systems Biology Synthetic Biology Top Down Bottom Up

44. Top Down and Bottom Up Top Down “-omics” Whole cell System Statistical Correlations Model High-throughput Data Yeast Protein-Protein Interaction Map

45. Top Down and Bottom Up Top Down “-omics” Whole cell System Statistical Correlations Model High-throughput Data Networks/Pathways System Mechanistic, biophysical Model Quantitative, single-cell Data Bottom Up ”mechanistic”