Synthetic Biology (The Cell as a Nanosystem)

1. Synthetic Biology(The Cell as a Nanosystem) COSMOS Nanotechnology UCSC Summer 2009

3. Synthetic Biology • Nanotechnology is emulating biology • Molecular assemblers, molecular sensors • ‘Bots’ that deliver medicine to specific cells • Biotechnology is helping out • Genetic ‘reengineering’ of e-coli, phages • Nano-Bio or Bio-Nano? • Two very interesting approaches… • The answer might be ‘synthetic biology’

5. DNA 2.0 • DNA 2.0 Inc.is a leading provider for synthetic biology. With our gene synthesis process you can get synthetic DNA that conforms exactly to your needs, quickly and cost effectively. Applications of custom gene synthesis include codon optimization for increased protein expression, synthetic biology, gene variants, RNAi trans-complementation and much more.

6. Nano-Bio-Info-Tech (NBIT) • ‘Fusion’ or ‘convergence’ of • Nanotechnology • Biotechnology • Information technology • Focus of regional development • Nanobiotechnology (DNA microarrays) • Bioinformatics and Informatics • Add stem cell and genetic engineering

7. Some Definitions… • Bionanotechnology • Biology as seen through the eyes of nano • How do molecules work in biology? • How can we make biology work for us? • Applications • Self assembled protein metal complexes • DNA scaffolding for arrayed assembly • Phage injection of targeted viral DNA

8. Bio-Nano Convergence

9. Bio-Nano Machinery • Using protein / viral complexes and DNA to self-assemble devices, and novel function, into biomechanical systems Earth’s early nanostructures ~ 2 billion years ago

10. NanoBioConvergence • Nanotechnology used in biotech • DNA microarrays (GeneChip™) • SNP genotyping applications • Silicon microtechnology for the lab • Lab-On-A-Chip (LOC) • System-On-A-Chip • Biocompatible engineered surfaces • Better performance / durability in humans

12. Affymetrix GeneChip™

13. Nature’s Toolkit • Self Assembly • Viral caspids • Proteins • Genetic Algorithms • Information networks • DNA => miRNA => mRNA => Protein • Protein => miRNA = DNA (intron) / DNA (exon) • Energy networks (proteome / metabolome)

14. Figure1: 3D diagram of a lipid bilayer membrane - water molecules not represented for clarity http://www.shu.ac.uk/schools/research/mri/model/micelles/micelles.htm • Figure 2: Different lipid model • top : multi-particles lipid molecule • bottom: single-particle lipid molecule Molecular Self Assembly

15. Viral Self-Assembly http://www.virology.net/Big_Virology/BVunassignplant.html

16. --------------------------- 1010110001011010 ATGCCAGTACTGG TACGGTCATGACC 0101001110100101 --------------------------- Self-Assembled Algorithms

17. Bio-Nano-Info • Looking at bio through the eyes of nano • Physical properties of small / life systems • Looking at nano through the eyes of bio • Self-assembly of molecular nano structures • Interaction of information and molecules • Molecular assemblies as information and operating systems - nano execution of IT

18. Nano-Bio-Info-Tech Nano Quantum computing nanoelectronic devices Self assembly Microarrays, BioMEMS Bio Digital cells DNA computing insilico biology Info Concept by Robert Cormia

19. Bio-Informatics • Looking at life as an information system • DNA as a database • RNA as a decision network • Proteins and genes as runtime DLLs • Modeling gene regulatory networks • Simulating life as a computer program • Using silicon to validate biological models

20. Goal of Digital Cells • Simulate a Gene Regulatory Network • Goal of e-cell, CellML, and SBML projects • Test microarray data for biological model • Run expression data through GRN functions • Create biological cells with new functions • Splice in promoters to control expression • Create oscillating networks using operons

21. Digital Cell Components • Bio-logic gates • Inverters, oscillators • Creating genomic circuitry • Promoters, operons and genes • Multigenic oscillating solutions • Ron Weiss is the pioneer in the field • http://www.princeton.edu/~rweiss/

22. Digital Cell Basics http://www.ee.princeton.edu/people/Weiss.php

23. Digital Cell Circuit (1) INVERSE LOGIC. A digital inverter that consists of a gene encoding the instructions for protein B and containing a region (P) to which protein A binds. When A is absent (left)—a situation representing the input bit 0—the gene is active. and B is formed—corresponding to an output bit 1. When A is produced (right)—making the input bit 1—it binds to P and blocks the action of the gene—preventing B from being formed and making the output bit 0. Weiss http://www.ee.princeton.edu/people/Weiss.php

24. Digital Cell Circuit (2) In this biological AND gate, the input proteins X and Y bind to and deactivate different copies of the gene that encodes protein R. This protein, in turn, deactivates the gene for protein Z, the output protein. If X and Y are both present, making both input bits 1, then R is not built but Z is, making the output bit 1. In the absence of X or Y or both, at least one of the genes on the left actively builds R, which goes on to block the construction of Z, making the output bit 0. Weiss http://www.ee.princeton.edu/people/Weiss.php

25. Digital Cells – Bio Informatics Modeling life as an information system http://www.ee.princeton.edu/people/Weiss.php

26. Gene Regulatory Network

27. Basic GRN Circuit Flow Gross anatomy of a minimal gene regulatory network (GRN) embedded in a regulatory network. A regulatory network can be viewed as a cellular input-output device.http://doegenomestolife.org/

28. Gene regulatory networks ‘interface’ with cellular processes http://doegenomestolife.org/

29. Informationvs.Processing Just as in a computer, data bits and processing bits are made from the same material, 0 or 1, or A, T, C, G, or U in biology

30. Nature as a Computer • Biological systems like DNA and RNA especially appear to be more than networks of information. • RNA itself can be seen as a molecular decision network

31. E-Cell • E-Cell System is an object-oriented software suite for modeling, simulation, and analysis of large scale complex systemssuch as biological cells. Version 3 allows many components driven by multiple algorithms with different timescales to coexist

32. Computer Modeling Metabolic Pathways • BioCyc – collection of organism specific metabolic pathway databases • cellML is an XML based format for exchanging biological data from genes to proteins to metabolism

33. Digital Cells MeetSynthetic Biology • Model the circuit • Validate the circuit • Tinker with the circuit • Then… • Alter the gene to build a new protein • SNPs will give you a ‘first approach’ • See if the new protein is ‘well tolerated’

34. Gene Therapy • Gene therapy using an Adenovirus vector. A new gene is inserted into an adenovirus vector, which is used to introduce the modified DNA into a human cell. If the treatment is successful, the new gene will make a functional protein. http://en.wikipedia.org/wiki/Gene_therapy

35. DNA Vaccines • The ultimate method to train the immune system against a multitude of threats • Inject a known sequence of DNA • Trick the cell into expressing it, then seeing it as an antigen to ward against. • Used to fight cancer.

36. Animal Model Systems • Mice make perfect models – as they are: • Cheap (reasonably) • Fast / easy growing • Very ‘inbred’ • Mouse DNA arrays and the mouse genome are fairly well known, characterized

37. Stem CellTechnology Once you have an ‘altered genome’ ready to test beyond a simple one cell environment, you leverage the ability of stem cells to ‘mass produce’ your synthetic biology solution

38. Cell as a Nanosystem • Bilayer outer lipid membrane • Energy apparatus • Diffuse metabolome • Proteome with signaling network • DNA / RNA operating system, nucleosome miRNA control units

39. Green Algae at Work Making H2 Algal cell suspension / cells Thylakoid membrane  These little critters are very happy just to be working!

40. Proposed Engineered H2 Bacterium http://gcep.stanford.edu/pdfs/tr_hydrogen_prod_utilization.pdf

41. In Vitro Photo-Production of H2 Yellow arrow marks insertion of hydrogenase promoter. Right side data cell optimized for continuous H2 production.

42. Synthetic Biology Roadmap • Understanding of gene elements and transcriptional control at miRNA level • Ability to model protein structure, and surface potential / folding / function • Ability to create functional operons and regulated / feedback transcriptional control • Stem cell and gene therapy synergism

43. Role of Bioinformatics • Where are genes? • What are the regulatory inputs? • What are the proteins? • Where are post translational modifications? • What are the pathways? • What are the protein – RNA interactions? • Can we ‘modulate’ the operon networks to include precision feedback control?

44. Global Gene Expression Gene expression tells you how the machine is working Bioinformatics shows you where the control points are

45. Reprogramming the Cell • The cell is a molecular system where all parts also participate in an information system. • We model that system, and then attempt to alter the ‘internal influences’ to create different functional outputs.

46. Synthetic Proteins All proteins are ‘synthetic’ – peptides => polymers

47. Synthetic Proteins • Synthesis • New polymers • Biochemistry • Structural studies • Structure / function • Functional studies • New properties • New applications • Cell structure adapts well to environments

48. Nature as a NanoToolbox http://www.cse.ucsc.edu/~hongwang/ATP_synthase.html

49. Summary • Nano-Bio-Info Technology • Builds on nanotech and biotech • Adds information tech to model systems • Synthetic biology • Building informatics into modified genomes • Integrating biology and nanotechnology, working with life as an information system • Stem cell work will be the next frontier • Bringing innovation to life in higher organisms

50. References • http://www.ee.princeton.edu/people/Weiss.php • http://www.dbi.udel.edu/ • http://biospice.lbl.gov/ • http://www.systems-biology.org/ • http://www.e-cell.org/ • http://sbml.org/ • http://biocyc.org/ • http://www.sbi.uni-rostock.de/teaching/research/ • http://www.ipt.arc.nasa.gov/