Scientists have developed a robotic artificial intelligence system to determine on its own the optimal conditions for growing the replacement retinal layers needed for certain treatments aimed at restoring vision. In the latest experiment, the system managed a trial and error process covering 200 million possible configurations and managed to dramatically improve the viability of cell cultures required for regenerative medicine therapy. This achievement is a good example of how the automated design and execution of scientific experiments can increase the efficiency and speed of research in fields such as biology.
Research in regenerative medicine often requires numerous experiments that are time-consuming and labor-intensive. In particular, creating specific tissues from stem cells (a process called induced cell differentiation) takes months of work, and the degree of success depends on a wide range of variables. Finding the optimal type, dose, and timing of reagents, as well as the optimal physical variables, such as cell transfer time or temperature, is difficult and requires an enormous amount of testing.
To make this process more efficient and practical, a research team led by Genki Kanda of the RIKEN Institute in Japan set out to develop a self-contained experimental system that can determine optimal conditions and grow functional retinal pigment layers from retinal cells. mother. Retinal pigment epithelial cells were chosen because the degeneration of these cells is a common age-related disorder that leaves people unable to see. More importantly, transplanted retinal pigment epithelial layers have already shown some clinical success.
For autonomous experiments to be successful, the robot must repeatedly perform the same series of precise movements and manipulations, and the artificial intelligence must be able to evaluate the results and formulate the next experiment. The new system meets these goals with a general-purpose humanoid robot called Maholo capable of high-precision biological experiments. Maholo is controlled by artificial intelligence software that uses a newly designed optimization algorithm to determine which parameters should be changed, and how they should be changed, in order to improve differentiation efficiency in the next round of experiments.
The AI-equipped robotic system tested 200 million possible configurations, finally managing to decisively improve the “recipes” used for the retinal regenerative medicine process it had been working on. (Photo: RIKEN)
What would have taken human researchers more than two and a half years, the robotic system with artificial intelligence only took 185 days, and this translated into going from an initial differentiation rate efficiency of 50 percent to one of 90 percent, thanks to the experimentation and improvement work carried out by the robot.
Kanda and his colleagues expose the technical details of their progress in the academic journal eLife, under the title “Robotic search for optimal cell culture in regenerative medicine.” (Font: NCYT de Amazings)
