Biomolecular Computing Devices in Synthetic Biology - Advancing Artificial Intelligence Through Biological Process Applications - page 254

and can be applied to solve problems within the
field of biomedicine (Benenson, 2004; Condon,
2004; Rianudo, 2007; Shapiro, 2006).
Another important branch of biomolecular
computing is the so-called Membrane Comput-
ing or P Systems (Paun, 2000; Paun, 2002). P
Systems are distributed computational models
inspired in the living cell. In last years this abstract
and theoretical model has been used like a new
framework for developing computational models
of complex biological processes and systems
(Ciobanu, 2006).
Originally, biomolecular computation targeted
mathematical and computational problem solving
(Rodríguez-Patón, 1999). However, more recent
papers on biomolecular automata for intelligent
drugs design (Benenson, 2004) or gene networks
for boolean logic gates (Weiss, 2003) are broad-
ening horizons towards the application of these
systems in other disciplines like medicine, phar-
macology and bionanotechnology. These are the
newest and most promising 'killer applications'
of DNA computing.

they performed as part of the rest of the
biological system. These separable parts
are called modules. (Alon, 2007; Hartwell,
1999).

• Robustness: this is the persistency of the

biological system's properties in face of the
perturbations and under different noisy con-
ditions. Feedback control and redundancy
play an exceedingly important role in system
robustness (Mcdams, 1999; Kitano, 2004;
Stelling, 2004).

The goals of systems biology are to generate
new scientific knowledge to explain the biologi-
cal processes.

Relationship between systems biology and
synthetic biology: In the coming years we are
very likely to see a special relationship grow up
between systems biology and synthetic biology.
Systems biology is an ideal tool for helping syn-
thetic biology to model the design and construction
of biomolecular devices. In return, synthetic biol-
ogy can lead systems biology to a better under-
standing of the dynamic behaviour of biological
systems by creating biologically-inspired devices
to improve the models. This device modelling ( in
info or in silico ) and construction ( in vitro or in
vivo ) cycle will boost and further refine systems
biology models and the synthetic biology devices
(Church, 2005; Di Ventura, 2006).

Systems biology: is a scientific discipline
examining biological processes, systems and its
complex interactions using robust and precise
mathematical and physical models. The goal
of these models is to describe, understand and
predict the dynamic behaviour of these biologi-
cal processes and systems (Kitano, 2002). Key
concepts in systems biology are:

Some prominent centres:
In USA: • Complexity: the properties and dynamic

behaviour of biological systems are hard
to understand because of the huge number
of molecules and interactions between
molecules in different networks (genetic,
metabolic, etc.) and on different time scales.
(Amaral, 2004; Csete, 2004; Goldenfeld,
1999).
• Modularity: biological systems have the
property of containing separable parts that
are, even so, able to fulfil the function that

• Adam Arkin's group at California Univer-

sity, Berkeley: Arkin Laboratory , (http://ge-
nomics.lbl.gov/).

• George M. Church's group at Harvard Uni-

versity: Center for Computational Genetics ,
(http://arep.med.harvard.edu/).

• James J. Collins's group at Boston Uni-

versity: Applied Biodynamics Laboratory ,
(http://www.bu.edu/abl/).