Biological modeling is not yet in the mainstream of biological research in the way that it is in other scientific and technical areas like physics and engineering, with exposure to the philosophy and practice of modeling amongst life scientists being generally limited to a small cadre of computational biologists. It is no surprise therefore, that the pervasive view of modeling amongst life scientists, tends to be limited to that notion that the utility of models lies purely in their predictive capabilities.
As intellectual frameworks in which data can be organized and even transformed into useful, actionable knowledge, models are much more than just tools for "forecasting the weather" as it were. Models are invaluable tools for reasoning about the underlying architecture of the complex systems from which biologists typically gather their data. As platforms for enhancing our cognitive grasp of these systems and for the communication of knowledge and ideas, models can also serve as vehicles for the kind meaningful collaboration that the size and complexity of living systems necessitates.
A few years back, I was working at Plectix BioSystems, a venture capital-funded startup developing a coud computing-based platform for modeling complex biological systems (whose doors were sadly shuttered by the economic crash in 2010). In the course of this work, I prepared the following video for Google's "blue sky" Sci Foo conference in which one of our founders was invited to present the work that we were doing. When I look at where we are today versus back then (in 2009), I feel that most of the road we wanted to travel, has yet to be travelled. Still, as the old chinese saying goes "may you be blessed to live in interesting times".
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