History Dependence in a Chemical Reaction Network Enables Dynamic Switching

Identify structures underlying out-of-equilibrium reaction networks with random graph analysis

Network measures have proven very successful in identifying structural patterns in complex systems (e.g., a living cell, a neural network, the Internet). How such measures can be applied to understand the rational and experimental design of chemical reaction networks (CRNs) is unknown. Here, we develop a procedure to model CRNs as a mathematical graph on which network measures and a random graph analysis can be applied. We used an enzymatic CRN (for which a mass-action model was previously developed) to show that the procedure provides insights into its network structure and properties. Temporal analyses, in particular, revealed when feedback interactions emerge in such a network, indicating that CRNs comprise various reactions that are being added and removed over time. We envision that the procedure, including the temporal network analysis method, could be broadly applied in chemistry to characterize the network properties of many other CRNs, promising data-driven analysis of future molecular systems of ever greater complexity.

Exploring the programmability of autocatalytic chemical reaction networks

Networks of chemical reactions exhibit emergent properties under out-of-equilibrium conditions. Recent advances in systems chemistry demonstrate that networks with sufficient chemical complexity can be harnessed to emulate properties important for neuromorphic computing. In all examples, autocatalysis appears an essential element for facilitating the nonlinear integration of the input and self-regulatory abilities in the output. How this chemical analogue of a positive feedback mechanism can be controlled in a programmable manner is, however, unexplored. Here, we develop a strategy that uses metal ions (Ca2+, La3+, and Nd3+) to control the rate of a trypsin-catalysed autocatalytic reaction network. We demonstrate that this type of control allows for tuning the kinetics in the network, thereby changing the nature of the positive feedback. The simulations and experiments reveal that an input with one or more metal ions allow for temporal and history-dependent outputs that can be mapped onto a variety of mathematical functions.

Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems

The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.