3D-VISUALIZATION OF MACROMOLECULES IN BIOINFORMATICS:
DOI: 10.23951/2312-7899-2021-4-12-35
Bioinformatics scientists often describe their own scientific activities as the practice of working with large amounts of data using computing devices. An essential part of their self-identification is also the development of ways to visually represent the results of this work. Some of these methods are aimed at building convenient representations of data and demonstrating patterns present in them (graphics, diagrams, graphs). Others are ways of visualizing objects that are not directly accessible to human perception (microphotography, X-ray). Both the construction of visualizations and (especially) the creation of new computer visualization methods are considered in bioinformatics as significant scientific achievements. Representations of the three-dimensional structure of protein molecules play a special role in the inquiries of bioinformatics scientists. 3D-visualization of a macromolecule, on the one hand, is, like a graph, a representation of the results of computer processing of data arrays obtained by material methods – spatiotemporal coordinates of structural elements of the molecule. On the other hand, like microphotography, these 3D structures should serve as accurate representations of specific scientific objects. This leads to the parallel existence of two contradictory epistemic regimes: creative arbitrariness in making convenient, communicatively successful models, is combined with commitment to the object “as it really is”. The paradox is reinforced by the fact that the scientific study of objects in question (determining the properties of the structure, its functions, comparison with other structures) by means of computers does not require visualization at all. Its obviously high value for bioinformatics does not look justified if we take into account the prominent artificiality and artistry of the resulting images. However, the status of these images becomes clearer if we relate them to earlier notions of the role of the visual in scientific discovery. The highest estimation of visualization as the final result of scientific research was characteristic of Renaissance science. The artistic representation of ideal essential properties, instead of a strict correspondence to a particular biological object, is an epistemic virtue typical of the naturalists of the 17th and 18th centuries. Both suggested a close collaboration between the scientist and the artist; and standards for visualizing macromolecules in bioinformatics grow out of a similar collaboration (Geis’ drawings). The desire for maximum accuracy and detail inherits the regulation of “mechanical objectivity” (as Daston and Galison put it into words), for which it is also important to eliminate humans from the image production process (in bioinformatics, to transfer these functions to computer programs). Thus, 3D-visualization of protein structures bears traces of historically different value orientations, but the scientific practice of the 20th and 21st centuries, supplemented by computer technologies, allows them to be intertwined in particular disciplinary units.
Keywords: epistemology, visualization, scientific object, bioinformatics, data analysis
References:
Chadarevian, S. (2018). John Kendrew and myoglobin: protein structure determination in 1950s. Protein Science, 27, 1136–1143.
Daston, L., & Galison, P. (2018). Obyektivnost’ [Objectivity]. Translated from English by T. Varkhotov, S. Gavrilenko, & A. Pisarev. Novoe literaturnoe obozrenie.
Glazychev, V. L. (1989). Gemma Kopernika. Mir nauki v izobrazitel’nom iskusstve [Copernicus’ Gemma. The world of science in visual art]. Sovetskiy khudozhnik.
He, M., & Petoukhov, S. (2011). Mathematics of bioinformatics. Theory, practice and applications. John Wiley & Sons Inc.
Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615–626.
Kendrew, J. C. (1964). Myoglobin and the structure of proteins. Nobel lecture, December 11, 1962. In Nobel Foundation, Nobel Lectures, Chemistry 1942–1962 (pp. 676–698). Elsevier.
Lenhard, J. (2007). Computer simulation: the cooperation between experimenting and modelling. Philosophy of science, 74(2), 176–194.
Lenhard, J. (2019). Calculated surprises: a philosophy of computer simulation. Oxford University Press.
Lesk, A. (2013). Introduction to bioinformatics. Binom. Laboratoriya znaniy. (In Russian).
Lisovich, I. (2015). Skal’pel’ razuma i kryl’ya voobrazheniya: nauchnye diskursy v angliyskoy kul’ture rannego Novogo vremeni [The scalpel of reason and the wings of imagination: Scientific discourse in English culture in the Early Modern era]. HSE.
Merleau-Ponty, M. (1992). Eye and mind. Translated from English by A. Gustyr. Iskusstvo. (In Russian).
Morrison, M., & Morgan, M. (1999). Models as mediating instruments. In M. Morrison, & M. Morgan (Eds.), Models as mediators: perspectives on natural and social science (pp. 10–37). Cambridge University Press.
Pevsner, J. (2015). Bioinformatics and functional genomics. 3rd ed. John Wiley & Sons Inc.
Rheinberger, H.-J. (2000). Cytoplasmic particles. The trajectory of a scientific object. In L. Daston (Ed.), Biographies of scientific objects (pp. 270–294). University of Chicago Press.
Stevens, H. (2013). Life out of sequence: a data-driven history of bioinformatics. University of Chicago Press.
Tjio, J. H., & Levan, A. (1956). The chromosome number of man. Hereditas, 42, 1–6.
Yates, F. (2019). Theatre of the World. Translated from English by A. Dementyev. Tsiolkovskiy. (In Russian).
Issue: 4, 2021
Series of issue: Issue 4
Rubric: ARTICLES
Pages: 12 — 35
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