Automation Science

Automated Science is the process of conducting scientific research with minimum human involvement. In other words, the objective of automation science is to develop self-driving instruments which can decide the best feasible solutions based on available data.

Genomic sequencing and drug screening are two key uses of automation science in biomedical research. Thanks to the availability of highly automated equipment, we can now automate the experiments. However, we still rely on human scientists to set the parameters of experiments. Because of advances in artificial intelligence and machine learning, it is now feasible to create an intelligent system that can design complex experiments with a better likelihood of success.

One such example of automated science in biomedical research was a pioneering study that was based on extensive prior knowledge about yeast genetics and biochemistry. Its goal was to efficiently identify which genes encode particular enzymatic activities required for yeast growth. A team led by Ross King constructed a “Robot Scientist” named ADAM for this task. They first constructed specialized hardware that could carry out a very simple type of experiment: measuring the growth of strains of yeast containing deletions in particular genes in the presence or absence of various metabolites. They combined this with software to select experiments using logic programming. This program maintains a set of competing hypotheses consistent with previous observations and prior knowledge and then selects an experiment that is expected to invalidate as many of these hypotheses as possible. The chosen experiment is then executed in an automated fashion and the new observations are used to select the next experiment. ADAM correctly determined the functional roles of several genes in an automated fashion using fewer experiments than alternative techniques for selecting experiments (ex. selecting the least expensive one).

Another example to consider was a study done by Armaghan et al. 2016. The goal was to learn the effects of different drugs upon the distribution of different proteins within mammalian cells. A combination of liquid handling robots and an automated microscope were used to execute experiments in which one drug was added to one cell line expressing a fluorescently tagged protein. The output was a set of microscope images, and the choice of experiments was decided on the fly by constructing a predictive model for experiments that had not been done yet and using an active machine learning strategy to choose experiments for which the confidence of predictions was low. It was the first use of computationally-driven experimentation in which the set of possible outcomes was not known in advance. The results showed that a model that was 92% accurate could be learned while doing only 29% of the possible experiments.

Thus, the need for a new type of automated science stems from the realization that for many disciplines, like biology, there is no set of rules or laws that can be learned and from which everything else can be predicted. This fundamentally changes the paradigm of scientific research from a hypothesis-driven search for such laws to a data-driven construction of empirical models. Because many systems have too many variables and are too complex for humans to be able to think about, we need automated ways of constructing empirical models.