Synthetic biology builds on nanotechnology and biotechnology by adding information technology to model and modify biological systems at the genetic level. It aims to program cells by reengineering genomes and integrating biology with nanotechnology. Researchers can model gene networks, validate circuits, and alter genes to design new cellular functions. The next frontier is bringing such innovations to higher organisms using stem cells. The overall goal is to understand and reprogram biology as an information processing system at the molecular scale.

1. Synthetic Biology (The Cell as a Nanosystem) ARC Bioinformatics UC Davis Summer 2006

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. NanoBio Convergence 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. Molecular Self Assembly 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

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

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

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 Bio Info Self assembly Microarrays, BioMEMS Quantum computing nanoelectronic devices Digital cells DNA computing insilico biology 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 http://www.ee.princeton.edu/people/Weiss.php Modeling life as an information system

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

29. Information vs. 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 systems such 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 Meet Synthetic 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 Cell Technology 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 H 2 Algal cell suspension / cells Thylakoid membrane  These little critters are very happy just to be working!

40. Proposed Engineered H 2 Bacterium http://gcep.stanford.edu/pdfs/tr_hydrogen_prod_utilization.pdf

41. In Vitro Photo-Production of H 2 Yellow arrow marks insertion of hydrogenase promoter. Right side data cell optimized for continuous H 2 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/