The productivity challenge for life scientists is the design, execution, and analysis of high value experiments that discover or validate drugs and therapies. Compared to the electronics industry that spends $100,000 on software/engineer/year for productivity, an average life sciences company spends less than $5000 on software/scientist/year to achieve productivity. Scientists are left with a landscape of manual or hardwired informatics for integrating the lifecycle of experimentation. With the impressive advances in lab automation, miniaturization, and systems biology information, a new bottleneck to lab productivity is the informatics to leverage experiment data. Traditional informatics solutions are expensive to develop and deploy limiting the use and potential impact that software can have on productivity. Experiment design automation is a new form of software that breaks through the existing bottleneck using model-driven informatics and a combination of experimental and computational biology. The result is improved speed and quality of experiments, accelerated research, and lower operational costs.
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