The Best Model of a Cat Is Several Cats

© 2015 Elsevier Ltd. Modern biotechnology is emerging at the intersection of engineering, biology, physics, and computer science. As such it carries with it history from several disparate fields of research including a strong tradition in deductive reasoning primarily derived from discovery focused molecular biology and physics. Engineering biological systems is a complex undertaking requiring a broader set of epistemic tools and methods than what is usually applied in today's discovery based research. Inductive reasoning as commonly used in computer science has proven to be a very efficient approach to build knowledge about complex megadimensional datasets, including synthetic biology applications. The authors conclude that the multi-heuristic nature of modern biotechnology makes it an engineering field primed for inductive reasoning to complement the dominating deductive tradition. Complexity modeling. Biotechnology engineers living systems that are the product of evolution and can be discretized and organized following different organizational layers, each one with its own mechanisms and contexts. Bioengineering practitioners must be aware of the rich and complex relationships among these levels of complexity. Reproducibility. Most of biotechnology cannot be reproduced for practical, temporal, and economic reasons. Despite this, designed bioengineered entities must work consistently and robustly when released as products. Multi-heuristics. Several heuristics are implemented in parallel in modern biotechnology. Each one follows a reasoning procedure that provides possible solutions and logical explanations. They are not contradictory, just useful for pragmatic reasons. The challenge is to build engineering knowledge for a biological field.