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      Bio-pixels: A stem cell-based interactive–generative interface designed to redefine technologies of self-making in new media arts

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          Abstract

          Bio-pixels is a stem cell-based interactive–generative interface designed to investigate the concept of ‘self-making’. The project uses stem cells as a biological prototype of an identity-free substance and defines in vivo stem cell differentiation processes as nature’s self-making technology. It therefore considers in vitro-induced differentiation processes as artificial self-making technologies that were recontextualized through the interactions between the world of genes and the world of bits. The project’s system was functionally built based on three operational principles derived from convergence technologies that facilitate a mutual functional shift between bio-media and digital media and reveal the extent to which this shift leads to a reconciliation between our biological and narrative identities.

          Empirically, the project remodelled visual maps of cellular activities during the induced differentiation processes by which cells acquire their identity. Finally, a generative biological–digital mirror was architected by which the viewers see their faces resynthesized as the result of the interactions between the artificial remodelled differentiation processes and the participants’ activities at the project’s physical place and its Twitter page. Within this context, Bio-pixels highlights the consequences of today’s bioinformatics on in vitro artificial processes of self-making through which the public can control, enhance or resynthesize their identities.

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          Most cited references24

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          Induced pluripotent stem cells: past, present, and future.

          The development of iPSCs reflected the merging of three major scientific streams and has in turn led to additional new branches of investigation. However, there is still debate about whether iPSCs are functionally equivalent to ESCs. This question should be answered only by science, not by politics or business. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Genetical Implications of the Structure of Deoxyribonucleic Acid

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              Genetic circuit design automation.

              Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits forEscherichia coli(880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization.
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                Author and article information

                Journal
                Convergence: The International Journal of Research into New Media Technologies
                Convergence
                SAGE Publications
                1354-8565
                1748-7382
                December 2020
                November 29 2019
                December 2020
                : 26
                : 5-6
                : 1367-1390
                Affiliations
                [1 ]Helwan University, Egypt
                Article
                10.1177/1354856519890096
                e5b962ce-a07a-4c02-9a1f-2e32e08e0ec3
                © 2020

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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