Synthetic Biology Network Analysis and Design - PowerPoint PPT Presentation

synthetic biology network analysis and design n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Synthetic Biology Network Analysis and Design PowerPoint Presentation
Download Presentation
Synthetic Biology Network Analysis and Design

play fullscreen
1 / 99
Synthetic Biology Network Analysis and Design
164 Views
Download Presentation
dezso
Download Presentation

Synthetic Biology Network Analysis and Design

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Synthetic Biology Network Analysis and Design Robert Entus, Ph.D.

  2. “Synthesis drives discovery and paradigm change in ways not possible through analysis” -Steve Brenner

  3. What is Synthetic Biology? There are two fundamental research areas that fall under the term: • Retooling biological interactions on the molecular scale by developing synthetic analogs to biological molecules, e.g. novel bases that allows Watson-Crick base pairing in DNA to have more than four bases. • Using existing biological components to the increase the understanding of cellular biology and the requirements for adding functionality. Combines knowledge from many disciplines, such as molecular biology, computational sciences, and engineering.

  4. What is Synthetic Biology? There are two fundamental research areas that fall under the term: • Retooling biological interactions on the molecular scale by developing synthetic analogs to biological molecules, e.g. novel bases that allows Watson-Crick base pairing in DNA to have more than four bases. • Using existing biological components to the increase the understanding of cellular biology and the requirements for adding functionality. Combines knowledge from many disciplines, such as molecular biology, computational sciences, and engineering.

  5. Advantages of Synthetic Biology? • What can SB do for us? • Small gene networks are introduced into hosts, such as E. coli to study existing networks and motifs by altering individual elements. • develop novel and increasingly complex gene networks in single cell and multi cellular systems • development of sophisticated behaviors such as bistable switches, oscillators, biosensors, drug synthesis, and programmable pattern formation.

  6. Advantages of Synthetic Biology? • What can SB do for us? • Small gene networks are introduced into hosts, such as E. coli to study existing networks and motifs by altering individual elements. • develop novel and increasingly complex gene networks in single cell and multi cellular systems • development of sophisticated behaviors such as bistable switches, oscillators, biosensors, drug synthesis, and programmable pattern formation.

  7. Advantages of Synthetic Biology? • What can SB do for us? • Small gene networks are introduced into hosts, such as E. coli to study existing networks and motifs by altering individual elements. • develop novel and increasingly complex gene networks in single cell and multi cellular systems • development of sophisticated behaviors such as bistable switches, oscillators, biosensors, drug synthesis, and programmable pattern formation.

  8. Advantages of Synthetic Biology? • What can SB do for us? • Small gene networks are introduced into hosts, such as E. coli to study existing networks and motifs by altering individual elements. • develop novel and increasingly complex gene networks in single cell and multi cellular systems • development of sophisticated behaviors such as bistable switches, oscillators, biosensors, drug synthesis, and programmable pattern formation.

  9. Advantages of Synthetic Biology? • What can SB do for us? • Small gene networks are introduced into hosts, such as E. coli to study existing networks and motifs by altering individual elements. • develop novel and increasingly complex gene networks in single cell and multi cellular systems • development of sophisticated behaviors such as bistable switches, oscillators, biosensors, drug synthesis, and programmable pattern formation.

  10. Network Simplification Without simplification understanding network interactions becomes very difficult Example: Given b = 3.6 what does a =? a4 + 4a3b + 6a2b2 + 4ab3 + b4 = 1296

  11. Network Simplification Without simplification understanding network interactions becomes very difficult Example: Given b = 3.6 what does a =? a4 + 4a3b + 6a2b2 + 4ab3 + b4 = 1296 With simplification the solution becomes trivial Solution: (a + b)4 = 64 a + 3.6 = 6 a = 2.4

  12. Network For Feedback Amplifier Arabinose AraC cI cI O1 araI O2 GFP GFP metJ OR1 OR2 MetJ

  13. How To Create Synthetic Network • Define experimental conditions. What host will you be using, E.coli, yeast, or mammalian, how will you measure the output, etc. • Develop a working model of the network that contains necessary and appropriate components, specifically a measurable input and output. • No matter how useful a protein appears to be, in your design, if its production kills the host, it is not a good choice. • Construct the network with experimental conditions in mind. Will you want to switch hosts later on down the line?

  14. How To Create Synthetic Network • Define experimental conditions. What host will you be using, E.coli, yeast, or mammalian, how will you measure the output, etc. • Develop a working model of the network that contains necessary and appropriate components, specifically a measurable input and output. • No matter how useful a protein appears to be, in your design, if its production kills the host, it is not a good choice. • Construct the network with experimental conditions in mind. Will you want to switch hosts later on down the line?

  15. How To Create Synthetic Network • Define experimental conditions. What host will you be using, E.coli, yeast, or mammalian, how will you measure the output, etc. • Develop a working model of the network that contains necessary and appropriate components, specifically a measurable input and output. • No matter how useful a protein appears to be, in your design, if its production kills the host, it is not a good choice. • Construct the network with experimental conditions in mind. Will you want to switch hosts later on down the line?

  16. General Experimental Setup • Synthetic Networks were designed and assembled on plasmids capable of expression in E.coli. • Cells expressing the network of interest were grown overnight, diluted 1:300 into LB with the appropriate supplements and grown to an OD600=0.6.

  17. General Experimental Setup • Synthetic Networks were designed and assembled on plasmids capable of expression in E.coli. • Cells expressing the network of interest were grown overnight, diluted 1:300 into LB with the appropriate supplements and grown to an OD600=0.6.

  18. General Experimental Setup Clear Bottom 96 Well Plate Victor3 Plate Reader • The cells were washed and resuspended in M9 Medium and transferred to a 96 well plate and covered with mineral oil. • Fluorescent (ex. 395, em. 515), and OD600 measurements were taken every 8 minutes with orbital shaking between measurements.

  19. General Experimental Setup Clear Bottom 96 Well Plate Victor3 Plate Reader • The cells were washed and resuspended in M9 Medium and transferred to a 96 well plate and covered with mineral oil. • Fluorescent (ex. 395, em. 515), and OD600 measurements were taken every 8 minutes with orbital shaking between measurements.

  20. Plate Based System • Measure samples through out entire experiment. No need to use additional protocols to determine network state. • Robust experimental conditions possible in a single run. • High throughput experimentation possible • Small footprint • Low experimental costs PNAS (2003) 100:7702

  21. Plate Based System • Measure samples through out entire experiment. No need to use additional protocols to determine network state. • Robust experimental conditions possible in a single run. • High throughput experimentation possible • Small footprint • Low experimental costs PNAS (2003) 100:7702

  22. Plate Based System • Measure samples through out entire experiment. No need to use additional protocols to determine network state. • Robust experimental conditions possible in a single run. • High throughput experimentation possible • Small footprint • Low experimental costs PNAS (2003) 100:7702

  23. Plate Based System • Measure samples through out entire experiment. No need to use additional protocols to determine network state. • Robust experimental conditions possible in a single run. • High throughput experimentation possible • Small footprint • Low experimental costs PNAS (2003) 100:7702

  24. Plate Based System • Measure samples through out entire experiment. No need to use additional protocols to determine network state. • Robust experimental conditions possible in a single run. • High throughput experimentation possible • Small footprint • Low experimental costs PNAS (2003) 100:7702

  25. Working Model Arabinose AraC cI cI O1 araI O2 GFP GFP metJ OR1 OR2 MetJ

  26. AraC AraC binds araI and O2, bending the DNA so that the two DNA bound AraC contact each other cI O1 araI O2

  27. Arabinose AraC cI cI O1 araI O2 Arabinose causes a conformation change allowing AraC to bind araI and O1 allowing the transcription

  28. Arabinose AraC cI cI O1 araI O2 the operator sites OR1 and OR2 cooperatively bind cI, which in turn acts as a transcriptional activator by recruiting RNA polymerase. GFP OR1 OR2

  29. Arabinose AraC cI cI O1 araI O2 GFP The operator sites OR1 and OR2 cooperatively bind cI, which in turn acts as a transcriptional activator by recruiting RNA polymerase. GFP OR1 OR2 GFP is produced as the measurable output.

  30. Tandem “met box” sequences provide a repressor binding site AGgatTtT AGcCGTCc AGAtGTtT AcACaTCc 50% 75% 75% 63% Arabinose AraC cI cI O1 araI O2 GFP GFP metJ OR1 OR2 AGgatTtT AGcCGTCc AGAtGTtT AcACaTCc

  31. Arabinose AraC cI cI O1 araI O2 GFP GFP metJ OR1 OR2 AGgatTtT AGcCGTCc AGAtGTtT AcACaTCc MetJ metJ is transcribed from the same mRNA as GFP.

  32. MetJ provides negative feedback in the system by preventing cI production. Arabinose AraC cI cI O1 araI O2 GFP GFP metJ OR1 OR2 MetJ

  33. MetJ induced negative feedback can be modulated in two different areas. Arabinose AraC cI cI O1 araI O2 GFP GFP metJ OR1 OR2 AGgatTtT AGcCGTCc AGAtGTtT AcACaTCc Each 8 base sequence can be altered independently. The Ribosome Binding Site controlling MetJ translation.

  34. Network Construction • Building synthetic networks requires an understanding of fundamental molecular biology procedures: • Vector selection • Restriction enzymes to produce compatible ends • Gene cloning (Polymerase Chain Reaction) • Ligation reactions

  35. Vector Selection The network will be built on a single circular plasmid that contains all of the network components The completed network will look like this. Not this.

  36. Restriction Enzymes • Restriction enzymes cut the DNA at specific palindromic sites that range from 4 to 8 bases. • Many restriction enzymes leave “sticky” overhangs that allow directed cloning. • Several restriction enzymes can be used in the same reaction, as long as their buffer requirements are compatible. • The ability to cut a restriction site is dependent on many factors: the methylation state, secondary structures, and proximity to the end. The activity of most enzymes decreases dramatically when you get closer than six bases from the end. Crystal Structure of Eco RI bound to DNA substrate. Recognition Sequence.

  37. Restriction Enzymes • Restriction enzymes cut the DNA at specific palindromic sites that range from 4 to 8 bases. • Many restriction enzymes leave “sticky” overhangs that allow directed cloning. • Several restriction enzymes can be used in the same reaction, as long as their buffer requirements are compatible. • The ability to cut a restriction site is dependent on many factors: the methylation state, secondary structures, and proximity to the end. The activity of most enzymes decreases dramatically when you get closer than six bases from the end. Crystal Structure of Eco RI bound to DNA substrate. Recognition Sequence.

  38. Restriction Enzymes • Restriction enzymes cut the DNA at specific palindromic sites that range from 4 to 8 bases. • Many restriction enzymes leave “sticky” overhangs that allow directed cloning. • Several restriction enzymes can be used in the same reaction, as long as their buffer requirements are compatible. • The ability to cut a restriction site is dependent on many factors: the methylation state, secondary structures, and proximity to the end. The activity of most enzymes decreases dramatically when you get closer than six bases from the end. Crystal Structure of Eco RI bound to DNA substrate. Recognition Sequence.

  39. Restriction Enzymes • Restriction enzymes cut the DNA at specific palindromic sites that range from 4 to 8 bases. • Many restriction enzymes leave “sticky” overhangs that allow directed cloning. • Several restriction enzymes can be used in the same reaction, as long as their buffer requirements are compatible. • The ability to cut a restriction site is dependent on many factors: the methylation state, secondary structures, and proximity to the end. The activity of most enzymes decreases dramatically when you get closer than six bases from the end. Crystal Structure of Eco RI bound to DNA substrate. Recognition Sequence.

  40. Restriction Enzymes Sal I digestion Nde I digestion - GTCGAC - - GTCGAC - -CATATG- -GTATAC- - G TCGAC - - GTCGA C - -CA TATG- -GTAT AC- Two common restriction sites that allows directed cloning. Although many enzymes are available, Nde I is often used (even though it has minor problems) due to fact that the site incorporates a terminal ATG that provides the start codon in protein synthesis.

  41. Common Restriction Enzymes

  42. Polymerase Chain Reaction

  43. Polymerase Chain Reaction

  44. Ligation Reaction • Catalyzes the formation of a phosphodiester bond between juxtaposed 5' phosphate and 3' hydroxyl termini in duplex DNA or RNA. • Ligases can join blunt end and cohesive end termini as well as repair single stranded nicks in duplex DNA, RNA or DNA/RNA hybrids.

  45. Network Assembly

  46. NdeI restricition site (CATATG) Sal I digestion Sal I restriction site (GTCGAC) - GTCGAC - - GTCGAC - lac promoter - G TCGAC - - GTCGA C - pBR322 plasmid with lac promoter Double digest with Nde I and Sal I Nde I digestion -CATATG- -GTATAC- lac promoter NdeI overhang -CA TATG- -GTAT AC- Sal I overhang

  47. 6 bp extension NdeI restricition site (CATATG) GFP gene 6 bp extension Sal I restriction site (GTCGAC) PCR Amplification GFP gene with new restriction sites Double digest with Nde I and Sal I