Leroy Cronin

Lindsay A. Rutter, Abhishek Sharma, Ian Seet et al.

Molecular assembly offers a promising path to detect life beyond Earth, while minimizing assumptions based on terrestrial life. As mass spectrometers will be central to upcoming Solar System missions, predicting molecular assembly from their data without needing to elucidate unknown structures will be essential for unbiased life detection. An ideal agnostic biosignature must be interpretable and experimentally measurable. Here, we show that molecular assembly, a recently developed approach to measure objects that have been produced by evolution, satisfies both criteria. First, it is interpretable for life detection, as it reflects the assembly of molecules with their bonds as building blocks, in contrast to approaches that discount construction history. Second, it can be determined without structural elucidation, as it can be physically measured by mass spectrometry, a property that distinguishes it from other approaches that use structure-based information measures for molecular complexity. Whilst molecular assembly is directly measurable using mass spectrometry data, there are limits imposed by mission constraints. To address this, we developed a machine learning model that predicts molecular assembly with high accuracy, reducing error by three-fold compared to baseline models. Simulated data shows that even small instrumental inconsistencies can double model error, emphasizing the need for standardization. These results suggest that standardized mass spectrometry databases could enable accurate molecular assembly prediction, without structural elucidation, providing a proof-of-concept for future astrobiology missions.

Daniel Gahler, Dean Thomas, Slawomir Lach et al.

The most fundamental abstraction underlying all modern computers is the Turing Machine, that is if any modern computer can simulate a Turing Machine, an equivalence which is called Turing completeness, it is theoretically possible to achieve any task that can be algorithmically described by executing a series of discrete unit operations. In chemistry, the ability to program chemical processes is demanding because it is hard to ensure that the process can be understood at a high level of abstraction, and then reduced to practice. Herein we exploit the concept of Turing completeness applied to robotic platforms for chemistry that can be used to synthesise complex molecules through unit operations that execute chemical processes using a chemically-aware programming language, XDL. We leverage the concept of computability by computers to synthesizability of chemical compounds by automated synthesis machines. The results of an interactive demonstration of Turing completeness using the colour gamut and conditional logic are presented and examples of chemical use-cases are discussed. Over 16.7 million combinations of Red, Green, Blue (RGB) colour space were binned into 5 discrete values and measured over 10 regions of interest (ROIs), affording 78 million possible states per step and served as a proxy for conceptual, chemical space exploration. This formal description establishes a formal framework in future chemical programming languages to ensure complex logic operations are expressed and executed correctly, with the possibility of error correction, in the automated and autonomous pursuit of increasingly complex molecules.

Stuart M. Marshall, Douglas Moore, Alastair R. G. Murray et al.

We have developed the concept of pathway assembly to explore the amount of extrinsic information required to build an object. To quantify this information in an agnostic way, we present a method to determine the amount of pathway assembly information contained within such an object by deconstructing the object into its irreducible parts, and then evaluating the minimum number of steps to reconstruct the object along any pathway. The mathematical formalisation of this approach uses an assembly space. By finding the minimal number of steps contained in the route by which the objects can be assembled within that space, we can compare how much information (I) is gained from knowing this pathway assembly index (PA) according to I_PA=log (|N|)/(|N_PA |) where, for an end product with PA=x, N is the set of objects possible that can be created from the same irreducible parts within x steps regardless of PA, and NPA is the subset of those objects with the precise pathway assembly index PA=x. Applying this formalism to objects formed in 1D, 2D and 3D space allows us to identify objects in the world or wider Universe that have high assembly numbers. We propose that objects with PA greater than a threshold are important because these are uniquely identifiable as those that must have been produced by biological or technological processes, rather than the assembly occurring via unbiased random processes alone. We think this approach is needed to help identify the new physical and chemical laws needed to understand what life is, by quantifying what life does.

Jonathan Grizou, Laurie J. Points, Abhishek Sharma et al.

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the state a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water droplets, we are able to observe an order of magnitude more variety of droplet behaviours than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the discovery of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplets motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how target free search can significantly increase the rate of unpredictable observations leading to new discoveries with potential applications in formulation chemistry.

Juan Manuel Parrilla Gutierrez, Trevor Hinkley, James Taylor et al.

This manual outlines a fully automated liquid handling robot to enable physically-embodied evolution within a chemical oil-droplet system. The robot is based upon the REPRAP3D printer system and makes the droplets by mixing chemicals and then placing them in a petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software. This manual accompanies the open access publication published in Nature Communications DOI: 10.1038/ncomms6571.