Grip on complexity in chemical reaction networks

Photoinitiated Transient Self-Assembly in a Catalytically Driven Chemical Reaction Cycle

Chemically fueled chemical reaction networks (CRNs) are essential in controlling dissipative self-assembly. A key challenge in the field is to store chemical fuel-precursors or "pre-fuels" in the system that are converted into activating or deactivating fuels in a catalytically controlled CRN. In addition, real-time control over catalysis in a CRN by light is highly desirable, but so far not yet achieved. Here we show a catalytically driven CRN that is photoinitiated with 450 nm light, producing activated monomers that go on to perform transient self-assembly. Monomer activation proceeds via photoredox catalysis, converting the monomer alcohol groups into the corresponding aldehydes that self-assemble into large supramolecular fibers. Monomer deactivation is achieved by organometallic catalysis that relies on pre-fuel hydrolysis to release formate (i.e. the deactivating fuel). Additionally, irradiation with 305 nm light accelerates the release of formate by photo-uncaging the pre-fuel, leading to a factor of ca. 2 faster deactivation of the monomer. Overall, we show transient self-assembly upon visible light photoactivation, and tunable life-times by ultraviolet light.

The sugar cube: Network control and emergence in stereoediting reactions

Stereochemical editing strategies have recently enabled the transformation of readily accessible substrates into rare and valuable products. Typically, site selectivity is achieved by minimizing kinetic complexity by using protecting groups to suppress reactivity at undesired sites (substrate control) or by using catalysts with tailored shapes to drive reactivity at the desired site (catalyst control). We propose "network control," a contrasting paradigm that exploits hidden interactions between rate constants to greatly amplify modest intrinsic biases and enable precise multisite editing. When network control is applied to the photochemical isomerization of hexoses, six of the eight possible diastereomers can be selectively obtained. The amplification effect can be viewed as a mesoscale phenomenon between the limiting regimes of kinetic control in simple chemical systems and metabolic regulation in complex biological systems.

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.

Interlinking spatial dimensions and kinetic processes in dissipative materials to create synthetic systems with lifelike functionality

Biological systems spontaneously convert energy input into the actions necessary to survive. Motivated by the efficacy of these processes, researchers aim to forge materials systems that exhibit the self-sustained and autonomous functionality found in nature. Success in this effort will require synthetic analogues of the following: a metabolism to generate energy, a vasculature to transport energy and materials, a nervous system to transmit 'commands', a musculoskeletal system to translate commands into physical action, regulatory networks to monitor the entire enterprise, and a mechanism to convert 'nutrients' into growing materials. Design rules must interconnect the material's structural and kinetic properties over ranges of length (that can vary from the nano- to mesoscale) and timescales to enable local energy dissipations to power global functionality. Moreover, by harnessing dynamic interactions intrinsic to the material, the system itself can perform the work needed for its own functionality. Here, we assess the advances and challenges in dissipative materials design and at the same time aim to spur developments in next-generation functional, 'living' materials.

The Origin and Early Evolution of Life: Homochirality Emergence in Prebiotic Environments

Homochirality is one of the signatures of life. Numerous geological and prebiotic chemistry studies have proved that disordered soups containing small organic molecules, gases, liquids, and minerals (such as those containing phosphorous) yielded racemic mixtures of building blocks for biomolecule assembly. Polymers obtained from these bricks should have been enantiopure with functional properties similar to modern biomolecules or heterochiral with some functions such as catalyzing a chemical transformation unspecifically. Up until now, no clues have been found as to how symmetry breaking occurred. In this review, we highlight the principal achievements regarding the emergence of homochirality during the prebiotic synthesis of building blocks. Furthermore, we tried to focus on approaches based on prebiotic systems chemistry (bottom-up) and laboratory scales to simulate plausible prebiotic messy environments for the emergence of life. We aim with this review to assemble, even partially, the puzzle pieces of the origin of life regarding the relevant phenomenon of homochiral symmetry breaking.

pH-feedback systems to program autonomous self-assembly and material lifecycles

pH-responsive systems have gained importance for the development of smart materials and for biomedical applications because they can switch between different states by simple acid/base triggers. However, such equilibrium systems lack the autonomous behaviour that is so ubiquitous in living systems that self-regulate out of equilibrium. As a contribution to the emerging field of autonomous chemical systems, we have developed pH-feedback systems (pH-FS) based on the coupling of acid- and base-producing steps in chemical reaction networks. The resulting autonomous nonlinear pH curves can be coupled with a variety of pH-sensitive building blocks to program the lifecycles of the associated transient state at the level of self-assemblies and material systems. In this article, we discuss the different generations of such pH-feedback systems, the principles of their coupling to self-assemblies with lifecycles and highlight emerging concepts for the design of autonomous functional materials. The specificity, robustness, and flexible operation of such pH-FS can also be used to realize chemo-structural and chemo-mechanical feedbacks that extend the behaviour of such materials systems toward complex and functional life-like systems.

Chemical reaction networks and opportunities for machine learning

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

Perpetuating enzymatically induced spatiotemporal pH and catalytic heterogeneity of a hydrogel by nanoparticles

The attainment of spatiotemporally inhomogeneous chemical and physical properties within a system is gaining attention across disciplines due to the resemblance to environmental and biological heterogeneity. Notably, the origin of natural pH gradients and how they have been incorporated in cellular systems is one of the most important questions in understanding the prebiotic origin of life. Herein, we have demonstrated a spatiotemporal pH gradient formation pattern on a hydrogel surface by employing two different enzymatic reactions, namely, the reactions of glucose oxidase (pH decreasing) and urease (pH increasing). We found here a generic pattern of spatiotemporal change in pH and proton transfer catalytic activity that was completely altered in a cationic gold nanoparticle containing hydrogel. In the absence of nanoparticles, the gradually generated macroscopic pH gradient slowly diminished with time, whereas the presence of nanoparticles helped to perpetuate the generated gradient effect. This behavior is due to the differential responsiveness of the interface of the cationic nanoparticle in temporally changing surroundings with increasing or decreasing pH or ionic contents. Moreover, the catalytic proton transfer ability of the nanoparticle showed a concerted kinetic response following the spatiotemporal pH dynamics in the gel matrix. Notably, this nanoparticle-driven spatiotemporally resolved gel matrix will find applicability in the area of the membrane-free generation and control of spatially segregated chemistry at the macroscopic scale.

A Bayesian Approach to Extracting Kinetic Information from Artificial Enzymatic Networks

In order to create artificial enzymatic networks capable of increasingly complex behavior, an improved methodology in understanding and controlling the kinetics of these networks is needed. Here, we introduce a Bayesian analysis method allowing for the accurate inference of enzyme kinetic parameters and determination of most likely reaction mechanisms, by combining data from different experiments and network topologies in a single probabilistic analysis framework. This Bayesian approach explicitly allows us to continuously improve our parameter estimates and behavior predictions by iteratively adding new data to our models, while automatically taking into account uncertainties introduced by the experimental setups or the chemical processes in general. We demonstrate the potential of this approach by characterizing systems of enzymes compartmentalized in beads inside flow reactors. The methods we introduce here provide a new approach to the design of increasingly complex artificial enzymatic networks, making the design of such networks more efficient, and robust against the accumulation of experimental errors.

The finding and researching algorithm for potentially oscillating enzymatic systems

Many processes in living organisms are subject to periodic oscillations at different hierarchical levels of their organization: from molecular-genetic to population and ecological. Oscillatory processes are responsible for cell cycles in both prokaryotes and eukaryotes, for circadian rhythms, for synchronous coupling of respiration with cardiac contractions, etc. Fluctuations in the numbers of organisms in natural populations can be caused by the populations' own properties, their age structure, and ecological relationships with other species. Along with experimental approaches, mathematical and computer modeling is widely used to study oscillating biological systems. This paper presents classical mathematical models that describe oscillatory behavior in biological systems. Methods for the search for oscillatory molecular-genetic systems are presented by the example of their special case - oscillatory enzymatic systems. Factors influencing the cyclic dynamics in living systems, typical not only of the molecular-genetic level, but of higher levels of organization as well, are considered. Application of different ways to describe gene networks for modeling oscillatory molecular-genetic systems is considered, where the most important factor for the emergence of cyclic behavior is the presence of feedback. Techniques for finding potentially oscillatory enzymatic systems are presented. Using the method described in the article, we present and analyze, in a step-by-step manner, first the structural models (graphs) of gene networks and then the reconstruction of the mathematical models and computational experiments with them. Structural models are ideally suited for the tasks of an automatic search for potential oscillating contours (linked subgraphs), whose structure can correspond to the mathematical model of the molecular-genetic system that demonstrates oscillatory behavior in dynamics. At the same time, it is the numerical study of mathematical models for the selected contours that makes it possible to confirm the presence of stable limit cycles in them. As an example of application of the technology, a network of 300 metabolic reactions of the bacterium Escherichia coli was analyzed using mathematical and computer modeling tools. In particular, oscillatory behavior was shown for a loop whose reactions are part of the tryptophan biosynthesis pathway.

Self-reproducing catalytic micelles as nanoscopic protocell precursors

Protocells at life's origin are often conceived as bilayer-enclosed precursors of life, whose self-reproduction rests on the early advent of replicating catalytic biopolymers. This Perspective describes an alternative scenario, wherein reproducing nanoscopic lipid micelles with catalytic capabilities were forerunners of biopolymer-containing protocells. This postulate gains considerable support from experiments describing micellar catalysis and autocatalytic proliferation, and, more recently, from reports on cross-catalysis in mixed micelles that lead to life-like steady-state dynamics. Such results, along with evidence for micellar prebiotic compatibility, synergize with predictions of our chemically stringent computer-simulated model, illustrating how mutually catalytic lipid networks may enable micellar compositional reproduction that could underlie primal selection and evolution. Finally, we highlight studies on how endogenously catalysed lipid modifications could guide further protocellular complexification, including micelle to vesicle transition and monomer to biopolymer progression. These portrayals substantiate the possibility that protocellular evolution could have been seeded by pre-RNA lipid assemblies.

On the use of catalysis to bias reaction pathways in out-of-equilibrium systems

Catalysis is an essential function in living systems and provides a way to control complex reaction networks. In natural out-of-equilibrium chemical reaction networks (CRNs) driven by the consumption of chemical fuels, enzymes provide catalytic control over pathway kinetics, giving rise to complex functions. Catalytic regulation of man-made fuel-driven systems is far less common and mostly deals with enzyme catalysis instead of synthetic catalysts. Here, we show via simulations, illustrated by literature examples, how any catalyst can be incorporated in a non-equilibrium CRN and what their effect is on the behavior of the system. Alteration of the catalysts' concentrations in batch and flow gives rise to responses in maximum conversion, lifetime (i.e. product half-lives and t90 - time to recover 90% of the reactant) and steady states. In situ up or downregulation of catalysts' levels temporarily changes the product steady state, whereas feedback elements can give unusual concentration profiles as a function of time and self-regulation in a CRN. We show that simulations can be highly effective in predicting CRN behavior. In the future, shifting the focus from enzyme catalysis towards small molecule and metal catalysis in out-of-equilibrium systems can provide us with new reaction networks and enhance their application potential in synthetic materials, overall advancing the design of man-made responsive and interactive systems.

Controlling Mirror Symmetry Breaking and Network Formation in Liquid Crystalline Cubic, Isotropic Liquid and Crystalline Phases of Benzil-Based Polycatenars

Spontaneous development of chirality in systems composed of achiral molecules is important for new routes to asymmetric synthesis, chiral superstructures and materials, as well as for the understanding of the mechanisms of emergence of prebiotic chirality. Herein, it is shown that the 4,4'-diphenylbenzil unit is a universal transiently chiral bent building block for the design of multi-chained (polycatenar) rod-like molecules capable of forming a wide variety of helically twisted network structures in the liquid, the liquid crystalline (LC) and the crystalline state. Single polar substituents at the apex of tricatenar molecules support the formation of the achiral (racemic) cubic double network phase with Ia 3 ‾ d symmetry and relatively small twist along the networks. The combination of an alkyl chain with fluorine substitution leads to the homogeneously chiral triple network phase with I23 space group, and in addition, provides a mirror symmetry broken liquid. Replacing F by Cl or Br further increases the twist, leading to a short pitch double gyroid Ia 3 ‾ d phase, which is achiral again. The effects of the structural variations on the network structures, either leading to achiral phases or chiral conglomerates are analyzed.

Viewpoint: Homeostasis as Inspiration-Toward Interactive Materials

Homeostatic systems combine an ability to maintain integrity over time with an incredible capacity for interactive behavior. Fundamental to such systems are building blocks of "mini-homeostasis": feedback loops in which one component responds to a stimulus and another opposes the response, pushing the module to restore its original configuration. Particularly when they cross time and length scales, perturbation of these loops by external changes can generate diverse and complex phenomena. Here, it is proposed that by recognizing and implementing mini-homeostatic modules-often composed of very different physical and chemical processes-into synthetic materials, numerous interactive behaviors can be obtained, opening avenues for designing multifunctional materials. How a variety of controlled, nontrivial material responses can be evoked from even simple versions of such synthetic feedback modules is illustrated. Moreover, random events causing seemingly random responses give insights into how one can further explore, understand and control the full interaction space. Ultimately, material fabrication and exploration of interactivity become inseparable in the rational design of such materials. Homeostasis provides a lens through which one can learn how to combine and perturb coupled processes across time and length scales to conjure up exciting behaviors for new materials that are both robust and interactive.

Vision Statement: Interactive Materials-Drivers of Future Robotic Systems

A robot senses its environment, processes the sensory information, acts in response to these inputs, and possibly communicates with the outside world. Robots generally achieve these tasks with electronics-based hardware or by receiving inputs from some external hardware. In contrast, simple microorganisms can autonomously perceive, act, and communicate via purely physicochemical processes in soft material systems. A key property of biological systems is that they are built from energy-consuming "active" units. Exciting developments in material science show that even very simple artificial active building blocks can show surprisingly rich emergent behaviors. Active nonequilibrium systems are therefore predicted to play an essential role in realizing interactive materials. A major challenge is to find robust ways to couple and integrate the energy-consuming building blocks to the mechanical structure of the material. However, success in this endeavor will lead to a new generation of sophisticated micro and soft-robotic systems that can operate autonomously.

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.

Exploiting complexity to implement function in chemical systems

This feature article reflects a personal overview of the importance of complexity as an additional parameter to be considered in chemical research, being illustrated with selected examples in molecular recognition and catalysis.

Engineering multilayer chemical networks

Dynamic multilevel systems emerged in the last few years as new platforms to study thermodynamic systems. In this work, unprecedented fully communicated three-level systems are studied. First, different conditions were screened to selectively activate thiol/dithioacetal, thiol/thioester, and thiol/disulfide exchanges, individually or in pairs. Some of those conditions were applied, sequentially, to build multilayer dynamic systems wherein information, in the form of relative amounts of building blocks, can be directionally transmitted between different exchange pools. As far as we know, this is the first report of one synthetic dynamic chemical system where relationships between layers can be changed through network operations.

Spatiotemporal control of DNA-based chemical reaction network via electrochemical activation in microfluidics

In recent years, DNA computing frameworks have been developed to create dynamical systems which can be used for information processing. These emerging synthetic biochemistry tools can be leveraged to gain a better understanding of fundamental biology but can also be implemented in biosensors and unconventional computing. Most of the efforts so far have focused on changing the topologies of DNA molecular networks or scaling them up. Several issues have thus received little attention and remain to be solved to turn them into real life technologies. In particular, the ability to easily interact in real-time with them is a key requirement. The previous attempts to achieve this aim have used microfluidic approaches, such as valves, which are cumbersome. We show that electrochemical triggering using DNA-grafted micro-fabricated gold electrodes can be used to give instructions to these molecular systems. We demonstrate how this approach can be used to release at specific times and locations DNA- based instructions. In particular, we trigger reaction-diffusion autocatalytic fronts in microfluidic channels. While limited by the stability of the Au-S bond, this easy to implement, versatile and scalable technique can be used in any biology laboratory to provide new ways to interact with any DNA-based computing framework.