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the coupling from phase-attractive to phase-repulsive and vice versa. Since the
multistability and multirhythmicity described here are the result of phase-repulsive
interaction, time delay can probably induce such eects also in systems with phase-
attractive coupling. Even more interesting would be to investigate the combined
eect of delay, intrinsic noise, and cell-cell coupling. Recently it was shown that time
delay in gene expression can induce oscillations even when system's deterministic
counterpart exhibits no oscillations [Bratsun et al. (2005)].
An important aspect of synthetic biology is the design of smart biological devices
or new intelligent drugs, through the development of in vivo digital circuits [Weiss
et al. (2001)]. If living cells can be made to function as computers, one could
envisage, for instance, the development of fully programmable microbial robots
that are able to communicate with each other, with their environment and with
human operators. These devices could then be used, e.g., for detection of hazardous
substances or even to direct the growth of new tissue. In that direction, pioneering
experimental studies have shown the feasibility of programmed pattern formation
[Basu et al. (2005)], and the possibility of implementing logical gates and simple
devices within cells [Hasty et al. (2002)]. We identify three perspective directions
of this research. First is the construction of new biological devices capable to solve
or compute certain problems [see e.g. Haynes et al. (2008)]. A second direction
would be the identication of new dynamical regimes with extended functionality
using standard genetic parts, as we have discussed here. Finally, it should be
possible to add more levels of control, e.g. spatiotemporal control [Basu et al. (2004)]
or temporal light-dependent control via encapsulation [Antipov and Sukhorukov
(2004)] for precise regulation of synthetic genetic oscillators.
Finally it is worth noting that the investigation of synthetic genetic oscillators
can prot greatly from techniques and methods transferred from other elds of sci-
ence. Two areas are particularly relevant in this context: neural and electronic
networks. Both neural and genetic networks make use of feedback and coupling
mechanisms, and are signicantly noisy [Swain and Longtin (2006)]. However, neu-
ral networks have attracted in recent years much more attention than genetic net-
works from scientists working in nonlinear dynamics. Neuroscientists have access
to relatively long and clean time series of neural activity; such type of data are
only now beginning to appear for genetic systems. This outlines a promising fu-
ture to the combination of eorts in these two elds. On the other hand, direct
analogies can be drawn between synthetic biology and established techniques in
electrical engineering [Hasty et al. (2002)]. As a test bed of complicated experi-
ments in the implementation of complex gene networks, electronic circuits provide
much easier possibilities to investigate complex networks with similar topology and
demonstrating complex dynamical phenomena [Buldu et al. (2005)].
