Contents 1. Introduction 1.1. A workshop and this document 1.2. Framing origins of life science 1.2.1. What do we mean by the origins of life (OoL)? 1.2.2. Defining life 1.2.3. How should we characterize approaches to OoL science? 1.2.4. One path to life or many? 2. A Strategy for Origins of Life Research 2.1. Outcomes—key questions and investigations 2.1.1. Domain 1: Theory 2.1.2. Domain 2: Practice 2.1.3. Domain 3: Process 2.1.4. Domain 4: Future studies 2.2. EON Roadmap 2.3. Relationship to NASA Astrobiology Roadmap and Strategy documents and the European AstRoMap Appendix I Appendix II Supplementary Materials References

In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evolution of biochemical systems or molecular programs. Our method addresses the problem of meaningful crossovers between two chemical reaction networks of different topologies. It also builds on features such as speciation to speed up the search, to the point where it can deal with complete, realistic mathematical models of the biochemical processes. We demonstrate this framework by evolving credible biochemical answers to challenging autonomous molecular problems: in vitro batch oscillatory networks that match specific oscillation shapes. Our experimental results suggest that the search space is efficiently covered and that, by using crossover and preserving topological innovations, significant improvements in performance can be obtained for the automatic design of molecular programs.

In this paper, we apply the Polymerase-Exonuclease-Nickase Dynamic Network Assembly (PEN DNA) toolbox, a modular framework for molecular computing, to reservoir computing. While reservoir computing is traditionally implemented with recurrent neural networks, any system with similar recurrent properties, here chemical reaction networks (CRNs), can be used as a reservoir. We compared our approach to a previous CRN implementation of reservoir computing by Goudarzi et al. Our implementation yielded similar performance with respect to their benchmark tasks. We then took advantage of the modularity of the PEN DNA toolbox to investigate the impact of the CRN size on performance, both by hand and with an automated optimization process. In both cases, we were able to find systems with excellent performance while also being realistic with respect to in vitro implementation. Finally, we investigated the impact of constraining the weights of the output layer to be positive. This constraint guarantees that the system ...

Various artificial molecular devices, including some made of DNA or RNA, have been developed to date. The next step in this area of research is to develop an integrated system from such molecular devices. A molecular robot consists of sensors, computers, and actuators, all made of molecular devices, and reacts autonomously to its environment by observing the environment, making decisions with its computers, and performing actions upon the environment. Molecular computers should thus be the intelligent controllers of such molecular robots. Such controllers can naturally be regarded as hybrid systems because the environment, the robot, and the controller are all state transition systems having discrete and continuous states and transitions. For modeling and designing hybrid systems, formal frameworks, such as hybrid automata, are commonly used. In this perspective paper, we examine how molecular controllers can be modeled as hybrid automata and how they can be realized in a molecular robot. We first summarize the requirements for such molecular controllers and examine existing frameworks of DNA computing with respect to these requirements. We then show the possibility of combining existing frameworks of DNA computing to implement a sample hybrid controller for a molecular robot