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Is Captain Kirk a natural blonde? Do X-ray crystallographers dream of electron clouds? Comparing model-based inferences in science with fiction
Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this concern, philosophers of science have started analyzing scientific representations in terms of fictionalization strategies. Many arguments center on a dyadic relation between the model and its target system, focusing on structural resemblances and “as if” scenarios. This chapter provides a different approach. It looks more closely at model building to analyze the interpretative strategies dealing with the representational limits of models. How do we interpret ambiguous elements in models? Moreover, how do we determine the validity of model-based inferences to information that is not an explicit part of a representational structure? I argue that the problem of ambiguous inference emerges from two features of representations, namely their hybridity and incompleteness. To distinguish between fictional and non-fictional elements in scientific models my suggestion is to look at the integrative strategies that link a particular model to other methods in an ongoing research context. To exemplify this idea, I examine protein modeling through X-ray crystallography as a pivotal method in biochemistry
Analysis of Three-Dimensional Protein Images
A fundamental goal of research in molecular biology is to understand protein
structure. Protein crystallography is currently the most successful method for
determining the three-dimensional (3D) conformation of a protein, yet it
remains labor intensive and relies on an expert's ability to derive and
evaluate a protein scene model. In this paper, the problem of protein structure
determination is formulated as an exercise in scene analysis. A computational
methodology is presented in which a 3D image of a protein is segmented into a
graph of critical points. Bayesian and certainty factor approaches are
described and used to analyze critical point graphs and identify meaningful
substructures, such as alpha-helices and beta-sheets. Results of applying the
methodologies to protein images at low and medium resolution are reported. The
research is related to approaches to representation, segmentation and
classification in vision, as well as to top-down approaches to protein
structure prediction.Comment: See http://www.jair.org/ for any accompanying file
The Role of Data in Model Building and Prediction: A Survey Through Examples
The goal of Science is to understand phenomena and systems in order to
predict their development and gain control over them. In the scientific process
of knowledge elaboration, a crucial role is played by models which, in the
language of quantitative sciences, mean abstract mathematical or algorithmical
representations. This short review discusses a few key examples from Physics,
taken from dynamical systems theory, biophysics, and statistical mechanics,
representing three paradigmatic procedures to build models and predictions from
available data. In the case of dynamical systems we show how predictions can be
obtained in a virtually model-free framework using the methods of analogues,
and we briefly discuss other approaches based on machine learning methods. In
cases where the complexity of systems is challenging, like in biophysics, we
stress the necessity to include part of the empirical knowledge in the models
to gain the minimal amount of realism. Finally, we consider many body systems
where many (temporal or spatial) scales are at play and show how to derive from
data a dimensional reduction in terms of a Langevin dynamics for their slow
components
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