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A Complementary Account of Scientific Modelling: Modelling Mechanisms in Cancer Immunology

Zach, Martin (2022) A Complementary Account of Scientific Modelling: Modelling Mechanisms in Cancer Immunology. [Preprint]

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Abstract

According to a widely held view, scientific modelling consists in entertaining a set of model descriptions that specify a model. Rather than studying the phenomenon of interest directly, scientists investigate the phenomenon indirectly via a model in the hope of learning about some of the phenomenon’s features. I call this view the description-driven modelling (DDM) account. I argue that although an accurate description of much of scientific research, the DDM account is found wanting as regards the mechanistic modelling found in many branches of biology. By analysing research practices in cancer immunology concerning the development of mechanistic models of the process of cancer metastasis, this paper presents and argues for a complementary account of scientific modelling, herein called the experimentation-driven modelling (EDM) account. In EDM, scientists investigate a set of experimental systems and then integrate the results obtained from experiments into a mechanistic model. While EDM shares some key features with DDM, the two are epistemically very different approaches to research.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Zach, Martinzach@flu.cas.cz0000-0001-7181-0391
Additional Information: Forthcoming in the British Journal for the Philosophy of Science
Keywords: cancer; experimentation; immunology; scientific modeling; mechanisms
Subjects: General Issues > Data
Specific Sciences > Biology
General Issues > Confirmation/Induction
General Issues > Experimentation
General Issues > Explanation
General Issues > Models and Idealization
Depositing User: Dr. Martin Zach
Date Deposited: 21 Jun 2022 17:41
Last Modified: 21 Jun 2022 17:41
Item ID: 20777
Subjects: General Issues > Data
Specific Sciences > Biology
General Issues > Confirmation/Induction
General Issues > Experimentation
General Issues > Explanation
General Issues > Models and Idealization
Date: 2022
URI: https://philsci-archive.pitt.edu/id/eprint/20777

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