Simulations are a powerful and cost-effective way to demonstrate how efficiently ProtoLife's Predictive Design Technology (PDT) finds optimal targets in complex search spaces. The following tests were run on simulated but realistic experimental search spaces, representing combinations of a certain number of experimental parameters (e.g., materials or compounds) chosen from a library of 100, with each parameter in the combination taking any of 20 different values.1 The response surfaces for these systems contained several local optima with a range of values measured in arbitrary units from 0 to 8, with added experimental noise. These simulations benchmark PDT against established optimization methods in large and highly synergistic experimental search spaces, and demonstrate PDT's superiority at identifying the optimal targets while saving time and resources.
A simulated system with pairwise combinations was defined on an experimental space with a total of 105 experiments. Exhaustive exploration of such a space in the laboratory would require a vast effort, and screening even a fraction of it could be very expensive (e.g., if the experimental parameters were costly pharmaceuticals or other chemicals).
On this system, standard Design of Experiments (DoE) techniques were obviously more efficient than exhaustive exploration, but substantially less efficient than PDT.
PDT found all of the optimal targets after exploring only 3% of the experimental space, and was more than twice as efficient in doing so than a standard genetic algorithm (GA).
This simulated system involved all combinations of up to five experimental parameters, for a huge experimental space of over 1011 experiments. This system is more realistic than the one described above, because unpredictable higher-order synergies often occur in real-world systems: e.g., chemical systems for protein crystallization, protein synthesis, combination drug discovery, formulation of small molecule drugs, complex formulations (such as siRNA), heterogeneous catalyst discovery, and plastic formulations.
Because of such synergies, standard DoE cannot be used effectively to find optimal targets; DoE software such as JMP performs only slightly better than random search in a complex space of these dimensions.
PDT found all of the optimal targets in this space after exploring only 4,000 experiments, and was over 3 times more efficient than a standard GA.
1 Cawse, J, Gazzola, G, and Packard, N. (2011). Efficient discovery and optimization of complex high-throughput experiments. Catalysis today, 159(1): 55-63.