Towards Algorithms for Autonomous Experimentation

摘要

1 Introduction Modelling biological systems, is impaired by the cost of experimentally obtaining the data required to build the models. The resources available are typically very limited compared to large experimental parameter spaces, so have to be used efficiently. Similarly, in engineering with biological systems such as is found in synthetic biology and molecular computation, models of the phenomena to be harnessed are required. These models can only be obtained experimentally. As a consequence, for engineering with biological systems to become more prevalent, tools and techniques are required to assist in the experimentation performed to develop these models. Here we focus on one such tool, a computational system capable of autonomously investigating an experimental parameter space to identify phenomena that exist within. We call this computational system an autonomous experimentation system. Autonomous experimentation systems try to capture the efficiency of experimentalists, who are able to successfully navigate a seemingly boundless space of potential experiments. Autonomous experimentation systems develop hypotheses, plan experiments and perform experiments, in a closed loop manner without human interaction. As a foundation for our approach to autonomous experimentation, we draw on ideas from the philosophy of science. 2 Experimentation Experimentation should work to disprove hypotheses [1]. A hypothesis gives a possible explanation for some observed phenomena. Hypothesis lead experimentation can benefit from considering many hypotheses simultaneously, so as to allow for different explanations for a particular phenomenon to be developed without prejudice [2, 1]. While human scientists are limited in the number of different working hypotheses that they can realistically contemplate and visualise at any one time, a computer has no such limitation and could compare many thousand different hypotheses simultaneously. The hypotheses that are developed through experimentation, need not be mechanistic in nature. The investigation of the relationship between cause and effect can be performed experimentally, without developing mechanistic hypotheses. It is known for instance that children display an ability to associate cause with effect from an early age through their early play [3]. The use of cause and effect experimentation in scientific, medical and engineering work, has allowed for developments in these areas without an understanding for why the cause and effect are related. For example, a cure for scurvy was produced long before anyone understood why the cure worked [4]. 3 From Experimentation to Autonomous Experimentation Computational systems capable of scientific discovery are interlinked with artificial intelligence systems. Computational scientific discovery puts into practice artificial intelligence methods and brings real world benefits with it. It is important to note that neither artificial intelligence or autonomous experimentation systems will be able to match the abilities of human creativity. The KEKADA system [5], was an early computational experimentation system that was able to develop hypotheses and plan experiments to investigate an experimental parameter space. The KEKADA system took the approach of performing a broad search of the experimental parameter space through experimentation until a surprising phenomena was found, at which point the system performs more focussed experiments on the surprising phenomena. The hypotheses developed were mechanistic in nature and the system was able to rediscover known phenomena, such as determining the mechanism for how urea is synthesised in the body, and determining the structure of common alcohol, which also showed the systems generality in its hypotheses [5]. The KEKADA systems limitations came to the fore when comparing it to scientists, where scientists have the advantage of being able to employ additional heuristics and so able to solve a significantly large