Scientists demonstrate chemical reservoir calculation using the formose reaction

Researchers from the Institute for Molecules and Materials at Radboud University have shown that a complex self-organizing chemical reaction network can perform various computational tasks, such as nonlinear classification and complex dynamical predictions.

The field of molecular computing interests researchers who want to harness the computational power of chemical and biological systems. In these systems, chemical reactions or molecular processes act as reservoir computers, transforming inputs into high-dimensional outputs.

The research, published in Naturewas led by Prof. Wilhelm Huck of Radboud University.

Researchers have exploited the potential of chemical and biological networks due to their complex computational capabilities. However, the implementation of molecular computing poses engineering and design challenges.

Rather than attempting to design molecular systems to perform specific computational tasks, Prof. Huck and his team investigate how naturally complex chemical systems can exhibit emergent computational properties.

“I am very interested in the chemical driving forces that led to the origin of life. In this context, we are looking for mechanisms by which chemical evolution can shape the properties of complex reaction mixtures. This research led us to consider how molecular systems can process information,” he explained to Phys.org.

The formose reaction

The formose reaction is a chemical reaction that synthesizes sugars from formaldehyde in the presence of a catalyst, calcium hydroxide. This reaction was chosen because of its unique properties.

Prof. Huck explained: “Although chemistry may seem complex to outsiders, most reaction sequences are quite linear. The formose reaction is the only example of a self-organizing reaction network with a highly nonlinear topology, with numerous positive and negative feedback loops.”

In other words, the reaction is not straightforward and produces multiple intermediate compounds that react further to form new compounds. These dynamic reactions can result in a diverse set of chemical species and are nonlinear in nature.

In addition, the network includes positive feedback loops that amplify response outcomes and negative feedback loops that moderate response outcomes.

The network is called ‘self-organizing’ because it develops naturally and responds to chemical input without the need for external intervention, resulting in a variety of outputs.

The computational capabilities are derived from the inherent properties of the network and are not explicitly programmed, making the computations very flexible.

Implementation of the reservoir computer

The researchers used a continuous stirred tank reactor (CSTR) to implement the formose reaction. The input concentrations of four reactants – formaldehyde, dihydroxyacetone, sodium hydroxide and calcium chloride – are controlled to modulate the behavior of the reaction network.

The output molecule is identified using a mass spectrometer, which allows them to track up to 106 molecules. This setup can be used to perform calculations, where the reactant concentrations are the input value for each function to be calculated.

But first the system must be trained to determine the outcome of this calculation. This is done using a series of weights.

“We need to find a set of weights that convert the traces in the mass spectrometer to the correct value of the calculation. This is a linear regression problem and is computationally simple. Once that is done, the reservoir computer calculates the output for this function for each new input,” explains Prof. Huck.

The weights are coefficients that determine the influence of each input on the output. This training step is essential because it allows the reservoir to learn and predict how changes in input will affect the output, so that it can predict the output for a new set of inputs.

Computing capabilities

The researchers used the reservoir computer to perform several tasks. The first was to perform nonlinear classification tasks. The reservoir computer could emulate all Boolean logic gates and even tackle more complex classifications, such as XOR, checkers, circles, and sine functions.

The team also showed that it could predict the behavior of a complex metabolic network model of E. coli, accurately capturing both linear and nonlinear responses to fluctuating inputs across different concentration ranges.

Furthermore, the system was able to predict future states of a chaotic system (the Lorenz attractor). The system accurately predicted two of the three input dimensions several hours into the future.

The research team also found that some chemicals in the system have short-term memory, allowing them to remember information about previous actions.

They also showed a proof-of-concept for a full chemical readout using colorimetric reactions, demonstrating how the state of the system could be interpreted without electronic measurement equipment.

In other words, the state of the system could be interpreted based on color changes due to chemical reactions, eliminating the need for electronic measuring instruments.

Origin of life, neuromorphic computing and more

This new approach to molecular computing could bridge the gap between artificial systems and the information processing capabilities of living cells.

It suggests a scalable and more flexible approach to molecular computing, opening up possibilities for creating autonomous chemical systems that can process information and respond to their environment without external electronic control.

Prof. Huck indicated that his team is interested in this field, saying, “Can we embed reservoir computing into chemical systems that sense their environment, process that information, and take appropriate action?

“For this, the reservoir would have to be coupled to other elements that can convert the output of the chemical brain into a mechanical response or into an interaction with living cells, for example.”

The research also has intriguing implications for the origins of life. The emerging computational properties of this relatively simple chemical system could provide insights into how early biological systems could have evolved information processing capabilities.

Prof. Huck indicated that this was his primary motivation for studying reservoir calculation.

The research team also sees potential in neuromorphic computing, which mimics the neural structure and functioning of the human brain, to improve computing power and efficiency.

“We are very interested in exploring the technological limits of the computational power of the formose reservoir computer—this is an ongoing research in collaboration with IBM Zurich. Reservoir computing is an example of neuromorphic computing, which has attracted interest because it is expected to consume less energy than conventional computers,” explained Prof. Huck.

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