Abstract
In this chapter we explore basic mathematical and other constraints which limit the often novel uses of computation employed in modern computational system biology. These constraints generate substantial obstacles for one goal prominent in the field; namely, the goal of producing models valid for predictive uses in clinical and other contexts. However on closer examination many applications of computation and simulation in the field have more pragmatic or investigative goals in mind, suggesting an important role for rationalizing uses of computation in systems biology and elsewhere as investigative tools. We discuss the concept of an “investigative tool”, and what insights it might offer our understanding of modern computational strategies and the bases for them.
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Notes
- 1.
This research was funded by the US National Science Foundation which requires following human subjects’ regulations. Identities are concealed for this reason.
- 2.
I use “parameter space” here to refer to the space that plots parameters values against performance or fitness (measured by a set of performance conditions like fit to the data).
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Acknowledgments
The research for this paper was supported by an US National Science Foundation (DRL097394084), as well as by a Postdoctoral Fellowship at this Academy of Finland Centre of Excellence in the Philosophy of the Social Sciences and a position at the University of Twente. I would like to thank the editors of the volume in particular for their helpful advice in the development of this paper.
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MacLeod, M. (2017). Systems Biology in the Light of Uncertainty: The Limits of Computation. In: Lenhard, J., Carrier, M. (eds) Mathematics as a Tool. Boston Studies in the Philosophy and History of Science, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-54469-4_7
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