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Models as a way of reasoning about biology
The challenge of systems biology is not only experimental in kind. It also is the challenge of reasoning about facts that are rapidly evolving while remaining highly fragmented across research communities. I see a fundamental role for models as reasoning instruments in biology. Models, not databases as we know them today, will become the main vehicles for the computer-assisted storage, communication, and retrieval of biological knowledge. Computer scientists and I have joined forces with several other researchers to design a computational environment that represents biological knowledge, as it pertains to signaling, in an editable and executable fashion. This instrument will lend itself to the collaborative construction and critique of models.
The challenge of systems biology is not only experimental in kind. It also is the challenge of reasoning about facts that are rapidly evolving while remaining highly fragmented across research communities. I see a fundamental role for models as reasoning instruments in biology. Models, not databases as we know them today, will become the main vehicles for the computer-assisted storage, communication, and retrieval of biological knowledge. Computer scientists and I have joined forces with several other researchers to design a computational environment that represents biological knowledge, as it pertains to signaling, in an editable and executable fashion. This instrument will lend itself to the collaborative construction and critique of models.
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Jakob L. Andersen, Akbar Davoodi,Rolf Fagerberg,Christoph Flamm,Walter Fontana,Juri Kolčák, Christophe V. F. P. Laurent,Daniel Merkle,Nikolai Nøjgaard
arxiv(2024)
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JOURNAL OF THE AMERICAN CHEMICAL SOCIETYno. 1 (2023): 218-227
arXiv (Cornell University) (2022)
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