Page 125 - Read Online

Zhong et al. Chem Synth 2023;3:27 https://dx.doi.org/10.20517/cs.2023.15 Page 19 of 25

Figure 11. (A) A general scheme for protocellular communication of DNA-encapsulating protocells; (B) The collective signaling
behaviors involved in protocellular communication; (C) The preparation of the protocellular system. This figure is quoted with
permission from Joesaar et al. [155]

networks provide interactive platforms to program biological response patterns by affecting information
transformation processes, such as feedback, feedforward, communication, adaptation, bistability, and
oscillation mechanisms. The networks discussed here show the recent advances in using nucleic acids as
building blocks to synthesize bioinspired networks that mimic natural processes. We summarized the
chemical principles and the dynamic behaviors of fundamental dynamic networks, such as feedback,
feedforward, communicating, and network adaption. Then we categorized artificial transient-signal
conversion networks from the perspective of fuel types. Numerous dissipative far-from-equilibrium
networks are proposed as signaling transducers that convert environmental inputs into temporal output
signals. In addition, we discuss the recent advances in complex signaling dynamic behaviors by integrating
different functional networks. Furthermore, nucleic acid-based dynamic networks linking with downstream
functional systems are introduced to highlight the network-guided emerging properties and functions from
nanostructure and pattern dynamics to information processes in protocells.

Despite the considerable progress achieved with nucleic acid-based dynamic networks, the complexity and
functionality of natural networks have surpassed that of artificial networks. Therefore, the construction of
nucleic acid-based dynamic networks with enhanced complexity and functionality remains challenging due
to the limitations of existing approaches. Living systems operate under far-from-equilibrium conditions
with complex spatiotemporal behaviors, and forming such far-from-equilibrium systems follows a
[82]
spatiotemporal evolutionary pathway under environmental stress . This approach allows the system to
self-regulate the interactions and reactions of its components and adapt itself to be in tune with the ever-
changing environment [157-160] . To address this challenge, an evolutionary approach is highly desired.
Integrating numerous known far-from-equilibrium networks and adapting to environmental changes
through the training-learning process holds great promise for triggering the evolutionary process of the
artificial network. With this understanding, it is possible to imagine spatiotemporal networks with life-like
complexity and capabilities.