Memory, switches, and an OR-port through bistability in chemically fueled crystals

The ability to store information in chemical reaction networks is essential for the complex behavior we associate with life. In biology, cellular memory is regulated through transcriptional states that are bistable, i.e., a state that can either be on or off and can be flipped from one to another through a transient signal. Such memory circuits have been realized synthetically through the rewiring of genetic systems in vivo or through the rational design of reaction networks based on DNA and highly evolved enzymes in vitro. Completely bottom-up analogs based on small molecules are rare and hard to design and thus represent a challenge for systems chemistry. In this work, we show that bistability can be designed from a simple non-equilibrium reaction cycle that is coupled to crystallization. The crystals exert the necessary feedback on the reaction cycle required for the bistability resulting in an on-state with assemblies and an off-state without. Each state represents volatile memory that can be stored in continuously stirred tank reactors indefinitely even though molecules are turned over on a minute-timescale. We showcase the system’s abilities by creating a matrix display that can store images and by creating an OR-gate by coupling several switches together.

In biology, information is stored and processed using highly evolved molecules in bistable states. Here, the authors demonstrate bistability in a synthetic system without the need for evolved biomolecules or autocatalytic networks.

Switchable Biocatalytic Reactions Controlled by Interfacial pH Changes Produced by Orthogonal Biocatalytic Processes

Enzymes immobilized on a nano-structured surface were used to switch the activity of one enzyme by a local pH change produced by another enzyme. Immobilized amyloglucosidase (AMG) and trypsin were studied as examples of the pH-dependent switchable "target enzymes." The reactions catalyzed by co-immobilized urease or esterase were increasing or decreasing the local pH, respectively, thus operating as "actuator enzymes." Both kinds of the enzymes, producing local pH changes and changing biocatalytic activity with the pH variation, were orthogonal in terms of the biocatalytic reactions; however, their operation was coupled with the local pH produced near the surface with the immobilized enzymes. The "target enzymes" (AMG and trypsin) were changed reversibly between the active and inactive states by applying input signals (urea or ester, substrates for the urease or esterase operating as the "actuator enzymes") and washing them out with a new portion of the background solution. The developed approach can potentially lead to switchable operation of several enzymes, while some of them are inhibited when the others are activated upon receiving external signals processed by the "actuator enzymes." More complex systems with branched biocatalytic cascades can be controlled by orthogonal biocatalytic reactions activating selected pathways and changing the final output.

Hydrogels as dynamic memory with forgetting ability

The memory of our brain, stored in soft matter, is dynamic, and it forgets spontaneously to filter unimportant information. By contrast, the existing manmade memory, made from hard materials, is static, and it does not forget without external stimuli. Here we propose a principle for developing dynamic memory from soft hydrogels with temperature-sensitive dynamic bonds. The memorizing-forgetting behavior is achieved based on fast water uptake and slow water release upon thermal stimulus, as well as thermal-history-dependent transparency change of these gels. The forgetting time is proportional to the thermal learning time, in analogy to the behavior of brain. The memory is stable against temperature fluctuation and large stretching; moreover, the forgetting process is programmable. This principle may inspire future research on dynamic memory based on the nonequilibrium process of soft matter.

Viewpoint: Pavlovian Materials-Functional Biomimetics Inspired by Classical Conditioning

Herein, it is discussed whether the complex biological concepts of (associative) learning can inspire responsive artificial materials. It is argued that classical conditioning, being one of the most elementary forms of learning, inspires algorithmic realizations in synthetic materials, to allow stimuli-responsive materials that learn to respond to a new stimulus, to which they are originally insensitive. Two synthetic model systems coined as "Pavlovian materials" are described, whose stimuli-responsiveness algorithmically mimics programmable associative learning, inspired by classical conditioning. The concepts minimally need a stimulus-triggerable memory, in addition to two stimuli, i.e., the unconditioned and the originally neutral stimuli. Importantly, the concept differs conceptually from the classic stimuli-responsive and shape-memory materials, as, upon association, Pavlovian materials obtain a given response using a new stimulus (the originally neutral one); i.e., the system evolves to a new state. This also enables the functionality to be described by a logic diagram. Ample room for generalization to different stimuli and memory combinations is foreseen, and opportunities to develop future adaptive materials with ever-more intelligent functions are expected.

Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators

Responsive and shape-memory materials allow stimuli-driven switching between fixed states. However, their behavior remains unchanged under repeated stimuli exposure, i.e., their properties do not evolve. By contrast, biological materials allow learning in response to past experiences. Classical conditioning is an elementary form of associative learning, which inspires us to explore simplified routes even for inanimate materials to respond to new, initially neutral stimuli. Here, we demonstrate that soft actuators composed of thermoresponsive liquid crystal networks "learn" to respond to light upon a conditioning process where light is associated with heating. We apply the concept to soft microrobotics, demonstrating a locomotive system that "learns to walk" under periodic light stimulus, and gripping devices able to "recognize" irradiation colors. We anticipate that actuators that algorithmically emulate elementary aspects of associative learning and whose sensitivity to new stimuli can be conditioned depending on past experiences may provide new routes toward adaptive, autonomous soft microrobotics.

Programmable responsive hydrogels inspired by classical conditioning algorithm

Living systems have inspired research on non-biological dynamic materials and systems chemistry to mimic specific complex biological functions. Upon pursuing ever more complex life-inspired non-biological systems, mimicking even the most elementary aspects of learning is a grand challenge. We demonstrate a programmable hydrogel-based model system, whose behaviour is inspired by associative learning, i.e., conditioning, which is among the simplest forms of learning. Algorithmically, associative learning minimally requires responsivity to two different stimuli and a memory element. Herein, nanoparticles form the memory element, where a photoacid-driven pH-change leads to their chain-like assembly with a modified spectral behaviour. On associating selected light irradiation with heating, the gel starts to melt upon the irradiation, originally a neutral stimulus. A logic diagram describes such an evolution of the material response. Coupled chemical reactions drive the system out-of-equilibrium, allowing forgetting and memory recovery. The findings encourage to search non-biological materials towards associative and dynamic properties.

Living systems inspired research on systems chemistry to mimic specific complex biological functions, but mimicking even the most elementary aspects of learning is a grand challenge. Here the authors demonstrate a programmable hydrogel-based model system, whose behaviour is inspired by associative learning.

Design of Flow Systems for Improved Networking and Reduced Noise in Biomolecular Signal Processing in Biocomputing and Biosensing Applications

We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s) as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed.

Enzyme-based logic systems interfaced with signal-responsive materials and electrodes

Enzyme-based biocomputing systems were interfaced with signal-responsive membranes and electrodes resulting in bioelectronic devices switchable by logically processed biomolecular signals. "Smart" membranes, electrodes, biofuel cells, memristors and substance-releasing systems were activated by various combinations of biomolecular signals in the pre-programmed way implemented in biocatalytic cascades mimicking logic networks.

Molecules with a sense of logic: a progress report

Ones and zeros can be handled by molecules through the input-control of their signaling features. The progress in this exciting field during the last five years is covered in this tutorial review.

Majority and minority gates realized in enzyme-biocatalyzed systems integrated with logic networks and interfaced with bioelectronic systems

Biocatalytic reactions operating in parallel and resulting in reduction of NAD(+) or oxidation of NADH were used to mimic 3-input majority and minority logic gates, respectively. The substrates corresponding to the enzyme reactions were used as the input signals. When the input signals were applied at their high concentrations, defined as logic 1 input values, the corresponding biocatalytic reactions were activated, resulting in changes of the NADH concentration defined as the output signal. The NADH concentration changes were dependent on the number of parallel reactions activated by the input signals. The absence of the substrates, meaning their logic 0 input values, kept the reactions mute with no changes in the NADH concentration. In the system mimicking the majority function, the enzyme-biocatalyzed reactions resulted in a higher production of NADH when more than one input signal was applied at the logic 1 value. Another system mimicking the minority function consumed more NADH, thus leaving a smaller residual output signal, when more than one input signal was applied at the logic 1 value. The performance of the majority gate was improved by processing the output signal through a filter system in which another biocatalytic reaction consumed a fraction of the output signal, thus reducing its physical value to zero when the logic 0 value was obtained. The majority gate was integrated with a preceding AND logic gate to illustrate the possibility of complex networks. The output signal, NADH, was also used to activate a process mimicking drug release, thus illustrating the use of the majority gate in decision-making biomedical systems. The 3-input majority gate was also used as a switchable AND/OR gate when one of the input signals was reserved as a command signal, switching the logic operation for processing of the other two inputs. Overall, the designed majority and minority logic gates demonstrate novel functions of biomolecular information processing systems.

Robust ZnO nanoparticle embedded memory device using vancomycin conjugate and its biorecognition for electrical charging node

Conjugation of antibiotic vancomycin (VAN) on nanoparticles (NPs) has recently initiated novel works in the nanobiotechnology field. In this study, a bioelectronic structure using VAN conjugated zinc oxide (ZnO) NPs as charge storing elements on metal-pentacene-insulator-silicon (MPIS) device is demonstrated. Highly specific molecular recognition between the VAN and membrane protein unit mimicked from VAN-resistant bacteria is employed as the formation mechanism of self-assembly monolayers (SAMs) of ZnO NPs. The insulator surface is modified with the VAN cognate peptide of L-Ala-D-Glu-L-Lys-D-Ala-D-Ala by chemical activator coupling. Hysteretic behaviors in capacitance versus voltage (C-V) curves are obtained for the charged ZnO NPs exhibiting flatband voltage shifts, which demonstrate the charge storage on the VAN conjugated ZnO NPs. The potential perspective of this study will be a tangible progress of biomolecular electronics implemented by the interface between biomolecules and electronics.