Transcript Synthetic Biology - COSMOS Cluster 2 Introduction

Synthetic Biology
(The Cell as a Nanosystem)
COSMOS Nanotechnology
UCSC Summer 2009
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’
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 transcomplementation and much more.
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
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
Bio-Nano Convergence
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
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
Affymetrix GeneChip™
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)
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
Viral Self-Assembly
http://www.virology.net/Big_Virology/BVunassignplant.html
Self-Assembled Algorithms
--------------------------1010110001011010
ATGCCAGTACTGG
TACGGTCATGACC
0101001110100101
---------------------------
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
Nano-Bio-Info-Tech
Nano
Bio
Digital cells
DNA computing
insilico biology
Concept by Robert Cormia
Info
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
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
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/
Digital Cell Basics
http://www.ee.princeton.edu/people/Weiss.php
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
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
Digital Cells – Bio Informatics
Modeling life as an information system
http://www.ee.princeton.edu/people/Weiss.php
Gene Regulatory Network
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/
Gene regulatory networks ‘interface’ with cellular processes
http://doegenomestolife.org/
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
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
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
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
Digital Cells Meet
Synthetic Biology
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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’
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
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.
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
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
Cell as a Nanosystem
• Bilayer outer lipid
membrane
• Energy apparatus
• Diffuse metabolome
• Proteome with
signaling network
• DNA / RNA operating
system, nucleosome
miRNA control units
Green Algae at Work Making H2
Algal cell suspension / cells
Thylakoid membrane 
These little critters are very happy just to be working!
Proposed Engineered H2 Bacterium
http://gcep.stanford.edu/pdfs/tr_hydrogen_prod_utilization.pdf
In Vitro Photo-Production of H2
Yellow arrow marks insertion of hydrogenase promoter.
Right side data cell optimized for continuous H2 production.
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
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?
Global Gene Expression
Gene expression tells you how the machine is working
Bioinformatics shows you where the control points are
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.
Synthetic Proteins
All proteins are ‘synthetic’ – peptides => polymers
Synthetic Proteins
• Synthesis
– New polymers
• Biochemistry
• Structural studies
– Structure / function
• Functional studies
– New properties
• New applications
– Cell structure adapts
well to environments
Nature as a NanoToolbox
http://www.cse.ucsc.edu/~hongwang/ATP_synthase.html
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
References
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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/