## 5 EvoBot Applications
### Computer Vision Based Droplet Experiments  
Researchers at the University of Trento study chemotactic behaviour of moving droplets. In order to automate these types of experiments, we extended the static liquid handling system described above such that it could take input from the camera module. This allows us to place the chemical attractant at fixed relative positions with respect to the moving droplet. We have shown that using EvoBot instead of manual liquid handling improves the quality of the resulting experimental data [21] and that the chemotactic droplets can be used to transport live cells under sterile conditions [22]. We have furthermore optimised the chemotactic behaviour investigated above using a combination of EvoBot and machine learning. The aim is to evolve the droplet system towards desired outcomes (chemotaxis) by running an optimization experiment, where two different continuous variables or parameters are adjusted. An evolutionary algorithm was chosen as the optimization method, the Covariance Matrix Adaptation Evolution Strategy (CMAES), which produces good results with a limited number of evaluations [23]. The DEAP library, [24], provides an implementation of the CMAES algorithm in Python, which made it straight forward to use it with our Python-based API. The parameters to optimize are the molarity and the pH of the aqueous decanoate solutions, which are represented in or algorithm as a vector of two real number {a,b} \(\forall\) a,b \(\in\) [0,1] that represents the pH and the molarity of the system(pH \(\in\) [7,12.3]; molarity \(\in\) [5,20] mM). The droplet composition and volume (30 \(\mu\)L of 1-decanol droplet) and the salt gradient created (300 \(\mu\)L of 3.5 M NaCl) are fixed. In order to create the desired pH and molarity, seven vessels were placed on the experimental layer of EvoBot with solutions of 20 mM and pH ranging from 7 to 13, Figure 9a. A simple routine was developed to create a specific recipe by mixing two correlative pH solutions and water. In order to facilitate a fast setup, a custom experimental layer was developed with holes for the vessels and a template that helps to place 8 Petri dishes at specific locations. Thus, a simple routine was written, which chooses a clean Petri dish, dispenses the water and two solutions of decanoate, and mixes the liquid in order to achieve the desired concentration and pH. Then, the robot places a 1-decanol droplet and the salt, and tracks the droplet for 1 min, which can be seen in Figure 9b and Video S5. After all the Petri dishes have been used, the robot waits until a user replaces them and refills the vessels with reagents. Figure 9: Studying chemotaxis in decanol droplets. (**a**) The setup of the robot with 3 syringe modules, one dispensing module, 8 Petri dishes for experiments, vessels with 1-decanol (with red colour), salt and decanoate at different pH concentrations. (**b**) The image captured from the camera placed below the experimental layer. At some specific pH and concentrations of decanoate, the salt gradient induces a chemotactic behaviour in the 1-decanol droplet. The path of the droplet is analysed in real time and stored (blue line), and the edge of the Petri dish is detected (red circle). (**c**) Each experiment is scored using a fitness function. The initial position of the droplet for the fitness calculation, when it leaves the safe area (green circle), is the indicated by droplet 0. After eight frames, the droplet has moved to position 1. The vector dist 0 is projected on the axis droplet-salt, and the partial fitness, f0, is calculated. The same procedure is repeated every eight frames. The total fitness is the sum of the partial fitness divided by the number of frames. (**d**) The robot optimized the pH and the concentration of decanoate using an evolutionary algorithm, in order to maximize the chemotaxis of the droplet. The blue and red lines represent respectively the best individual and the median of the population for each generation.