Figure 3: This diagram illustrates the “branch migration” DNA reaction, in which one complementary strand is paired with— and replaces the existing partner of—another strand.

ATGCCGATTGCATTG
ATTGCATTG
ATTGCATTG
TACGGCTAACGTAAC
ATGCCGATTGCAT TG
TACGGCTAACGTAAC

 

molecules modified with additional chemical groups.

Moreover, DNA can be used to actuate DNA structures. DNA tweezers developed by Yurke are one typical example, where the single strand called fuel triggers the conformational change of DNA tweezers by branch migration [ 9]. Along this line, various kinds of motors and walkers made of DNA molecules have been developed [ 5].

INFORMATION TECHNOLOGY

Imagining the use of computers is easy in the context of construction of automobiles or other computers. However, imagining the use of computers to make robots made of cells or DNA is much more difficult.

IT is used heavily in general biologi-
cal research. Methods for string search
and string comparison are commonly
used for searching for unknown chro-
mosome sequences and for comparing
gene sequences among species. Such
methods are becoming increasingly im-
portant due to the progress in sequenc-
ing technology, while gene sequencing
itself requires methods for assembling
fragments of gene sequences, which is
also a kind of string processing.

Figure 4: Starting with the AND gate and reading clockwise, we see the input and output of each step in the process of computing a Boolean AND via the branch migration reaction.

Input 1

waste
AND gate
Input 2

Output
waste

are too detailed as models and not appropriate for biological systems because the parameters in the differential equations are often unknown. In addition, stochastic aspects are often important inside a cell because the copy number of a particular molecular species is very small. The copy number of each chromosome is only one. Consequently, more abstract discrete models are also used for modeling biological systems, and stochastic simulation is used to analyze such models.

Unlike systems biology, synthetic biology tries to construct artificial systems. Using genetic components found in natural systems is not necessary for this. We are free to engineer whatever components are required for easy use. The MIT researchers who initiated the study of synthetic biology take a rather radical approach to introducing engineering disciplines into biology. In particular, they emphasize standardization and abstraction [ 2]. More specifically, they are compiling a registry of genetic components or parts, similar to a transistor data book, and are trying to standardize the registry by adding various quantitative data to the component descriptions. Such standardized components can be used without knowledge of their internal implementation.

However, because biological components extracted from natural systems usually interact with one another in an unexpected manner, identifying all possible interactions among them is not possible. Even if they are standardized or abstracted, some unexpected interactions still remain, which makes the straightforward composition of components difficult. Moreover, biological components are usually fragile and exhibit stochastic behaviors, which can be regarded as errors from the artificial systems perspective. Therefore,