Biomedical Engineering Reference
In-Depth Information
als for recording standard biological parts
(http://parts.mit.edu). The bottom-up trend
emerged because synthetic biology focuses
on creating genetic circuits. The operation
of these circuits is based on gene expression
regulation through transcription control that
primarily involving genes and proteins (Ben-
ner, 2005; Hasty, 2002). Therefore, these
biomolecules play the key role in device
design and construction. However, recent
studies on RNA's important cell regulating
functions are encouraging its use in the
design and construction of synthetic biology
devices (Isaacs, 2006; Rinaudo, 2007).
DNA nanodevices based on competitive
hybridization (Bath, 2007; Liedl, 2007;
Simmel, 2005).
Devices based on virus structure (Ball, 2005;
Cello, 2002).
New materials based on the properties of
nucleic acids and proteins (Ball, 2005).
The goals of synthetic biology are to generate
new scientific knowledge to explain biological
processes, find new biological circuit design
principles (now based on gene expression regula-
tion) and their application in other disciplines, like
systems biology, biomolecular computation, medi-
cine, pharmacology and bionanotechnology.
The top-down trend isolates or reduces parts
of the biological systems to a minimum. Its
objective is to be able to understand these
parts and use them to build more complex
synthetic biological systems. One example of
this tactic is projects aiming to discover the
least number of genes needed for bacteria to
survive (Glass, 2004; Hutchison, 1999). The
goal is to use these bacteria as a basic mould
to build new synthetic organisms by adding
the genes required to perform special func-
tions, such as generating alternative fuels, to
this skeleton genome. Another example is a
project on the synthesis of the artemisinin
antimalarial drug in engineered yeast (Ro,
2006).
Biomolecular computation: is a scientific
discipline that is concerned with processing in-
formation encoded in biological macromolecules
like DNA, RNA or proteins, although the most
important advances have been made using DNA
(Rodríguez-Patón, 1999). The first paper on in
vitro biomolecular computation focused on DNA
and solved what is known as the Hamiltonian
path problem (Adleman, 1994). The next paper
used a filtering model to solve the SAT (Boolean
satisfiability problem) (Lipton, 1995). These pa-
pers demonstrated that DNA computation was
massively parallel, offering a huge information
density as just one test tube could contain in the
order of 10 20 DNA strands to encode informa-
tion. Additionally, the operations are executed
in parallel on all the strands. These properties of
DNA computation have been exploited to develop
bioalgorithms for cryptography (Gehani, 1999),
memories (Baum, 1995) and autonomous mo-
lecular computing (Sakamoto, 2000; Stojanovic,
2003). But despite the advances, the scientific
community realized that DNA computers are
no competitor for electronic computers in terms
of computational problem solving speed. The
strength of biomolecular computing, and specifi-
cally DNA computation, is that it is perfect option
for processing and handling biological information
There are other ways of pigeonholing synthetic
biology projects, such as in vivo and in vitro
projects (Forster, 2007).
Another type of less common synthetic biol-
ogy devices whose operation is based on more
specific processes are:
Logic circuits of nucleic acids based on
competitive hybridization (Seelig, 2006;
Takahashi, 2005).
Biomolecular automata that are based on
competitive hybridization and on restric-
tion enzyme operation (Benenson, 2001;
Benenson, 2004).
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