Advances in digitalisation bring a large number of possibilities to create, collect, store, and share data. This has a significant impact in biotechnology were the complexity of living organisms can be studied deeper and analysed by computers and experiments can be carried out by robotic stations fast and in a high degree of parallelization. Nevertheless, speaking of automation, control, and digitalization, the bioindustry is clearly behind other fields of engineering. The complexity of biological systems hampers an efficient use of computer aided tools. The substantial problem is that it is not possible to create a reliable mathematical model (digital twin) even of simple biological systems. Changes in protein expression, metabolic activity, mutations, etc. cannot be foreseen, described, nor fully understood. This means that the internet of things in biotechnology will be always coupled with experimental activities to validate and re-adjust models to living systems.
These experiments need to be designed intelligently and carried out in miniaturized parallel Liquid Handling Stations (LHS). Existing engineering tools need to be further developed and adapted to fill this gap between the digital space and the experimental system. Methods for Design of Experiments (DoE) have been developed and widely applied to this end. Still, if we consider systems where the time dependent response is important (e.g. most engineering applications) the experimental design has to consider the nonlinear dynamics of the process. If this is the case, more advanced tools are required, namely methods for Optimal Experimental Design (OED). These methods have to be further developed aiming to i) design optimal dynamic experiments in very short time, ii) communicate with the robotic facility that preforms the experiment, iii) learn from the data as it is being generated, iv) re-fit the parameters of the model to the data, and v) re-design the optimal experimental strategy.
All these methods need to be integrated in a framework to create an intelligent laboratory for rapid characterization of biosystems including: easy interaction of all devices, experimental design programs tailored for robotic LHS, online optimization algorithms that enable an optimal operation of the robots, and advanced control methods to assure the correct operation of the facilities.