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Synthetic Biology (The Cell as a Nanosystem)

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Synthetic Biology (The Cell as a Nanosystem) ARC Bioinformatics UC Davis Summer 2006 Synthetic Biology Nanotechnology is emulating biology Molecular assemblers ... – PowerPoint PPT presentation

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Title: Synthetic Biology (The Cell as a Nanosystem)


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

2
(No Transcript)
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

4
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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

Earths 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

11
(No Transcript)
12
Affymetrix GeneChip
13
Natures Toolkit
  • Self Assembly
  • Viral caspids
  • Proteins
  • Genetic Algorithms
  • Information networks
  • DNA gt miRNA gt mRNA gt Protein
  • Protein gt miRNA DNA (intron) / DNA (exon)
  • Energy networks (proteome / metabolome)

14
Molecular Self Assembly
15
Viral Self-Assembly
http//www.virology.net/Big_Virology/BVunassignpla
nt.html
16
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
Info
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 0the gene is active.
and B is formedcorresponding to an output bit 1.
When A is produced (right)making the input bit
1it binds to P and blocks the action of the
genepreventing B from being formed and making
the output bit 0. Weiss http//www.ee.princeton.ed
u/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
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 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 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 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_uti
lization.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 gt
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.htm
l
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/
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