Synthetic Biology(The Cell as a Nanosystem)

ARC BioinformaticsUC Davis Summer 2006

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 trans-complementation 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

--------------------------- 1010110001011010ATGCCAGTACTGGTACGGTCATGACC0101001110100101---------------------------

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

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Digital cellsDNA computinginsilico biology

Concept by Robert Cormia

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

http://www.ee.princeton.edu/people/Weiss.php

Modeling life as an information system

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/

http://doegenomestolife.org/

Gene regulatory networks ‘interface’ with cellular processes

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 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’

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 workingBioinformatics 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

• 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/