The creation of adaptive matter is heavily inspired by biological systems. However, it remains challenging to design complex material responses that are governed by reaction networks, which lie at the heart of cellular complexity. The main reason for this slow progress is the lack of a general strategy to integrate reaction networks with materials. Herein we use a systematic approach to preprogram the response of a hydrogel to a trigger, in this case the enzyme trypsin, which activates a reaction network embedded within the hydrogel. A full characterization of all the kinetic rate constants in the system enabled the construction of a computational model, which predicted different hydrogel responses depending on the input concentration of the trigger. The results of the simulation are in good agreement with experimental findings. Our methodology can be used to design new, adaptive materials of which the properties are governed by reaction networks of arbitrary complexity.
Our knowledge of the properties and dynamics of complex molecular reaction networks, for example those found in living systems, considerably lags behind the understanding of elementary chemical reactions. In part, this is because chemical reactions networks are nonlinear systems that operate under conditions far from equilibrium. Of particular interest is the role of individual reaction rates on the stability of the network output. In this research we use a rational approach combined with computational methods, to produce complex behavior (in our case oscillations) and show that small changes in molecular structure are sufficient to impart large changes in network behavior.
Living
systems rely on complex networks of chemical reactions to control
the concentrations of molecules in space and time. Despite the enormous
complexity in biological networks, it is possible to identify network
motifs that lead to functional outputs such as bistability or oscillations.
One of the greatest challenges in chemistry is the creation of such
functionality from chemical reactions. A key limitation is our lack
of understanding of how molecular structure impacts on the dynamics
of chemical reaction networks, preventing the design of networks that
are robust (i.e., function in a large parameter space) and resilient
(i.e.,
reach their out-of-equilibrium function rapidly). Here we demonstrate
that reaction rates of individual reactions in the network can control
the dynamics by which the system reaches limit cycle oscillations,
thereby gaining information on the key parameters that govern the
dynamics of these networks. We envision that these principles will
be incorporated into the design of network motifs, enabling chemists
to develop “molecular software” to create functional
behavior in chemical systems.
The creation of adaptive matter is heavily inspired by biological systems. However, it remains challenging to design complex material responses that are governed by reaction networks, which lie at the heart of cellular complexity. The main reason for this slow progress is the lack of a general strategy to integrate reaction networks with materials. Herein we use a systematic approach to preprogram the response of a hydrogel to a trigger, in this case the enzyme trypsin, which activates a reaction network embedded within the hydrogel. A full characterization of all the kinetic rate constants in the system enabled the construction of a computational model, which predicted different hydrogel responses depending on the input concentration of the trigger. The results of the simulation are in good agreement with experimental findings. Our methodology can be used to design new, adaptive materials of which the properties are governed by reaction networks of arbitrary complexity.
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