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The language of molecular biology says that the goal of bioengineering is achieving control over biological processes and other creatures. Maybe there are better alternatives. 

Microbial products are becoming common alternatives for pesticides and fertilizers in light of the unsustainability of chemical products. What the microbes in these products are, though—that is, how they are enacted—varies across regulatory, research and development, and growing spaces, and that variation matters to how they are regulated. From document analyses, interviews, and ethnographic work with scientists, growers, and policy actors, we find that these microbes are epistemically uneven, sometimes with pinned-down identities, and sometimes with loosely woven textures with holes. Amid calls to tailor regulations specifically for these products, we suggest that regulations predicated on discrete identities and predictable and controllable functions will fail to account for all users’ experiences, and that regulation may need to learn to live with the lacy texture of microbes across contexts. 

ABSTRACT Biological complexity is widely seen as the central, intractable challenge of engineering biology. Yet this challenge has been constructed through the field’s dominant metaphors. Alternative ways of thinking—latent in progressive experimental approaches, but rarely articulated as such—could instead position complexity as engineering biology’s greatest resource. We outline how assumptions about engineered microorganisms have been built into the field, carried by entrenched metaphors, even as contemporary methods move beyond them. We suggest that alternative metaphors would better align engineering biology’s conceptual infrastructure with the field’s move away from conventionally engineering-inspired methods toward biology-centric ones. Innovating new conceptual frameworks would also enable better aligning scientific work with higher-level conversations about that work. Such innovation—thinking about how engineering microbes might be more like user-centered design than like programming a computer or building a car—could highlight complexity as a resource to leverage, not a problem to erase or negate. 

Abstract Single engineered microbial species cannot always conduct complex transformations, while complex, incompletely defined microbial consortia have heretofore been suited to a limited range of tasks. As biodesigners bridge this gap with intentionally designed microbial communities, they will, intentionally or otherwise, build communities that embody particular ideas about what microbial communities can and should be. Here, we suggest that metaphors—ideas about what microbial communities arelike—are therefore important tools for designing synthetic consortia‐based bioreactors. We identify a range of metaphors currently employed in peer‐reviewed microbiome research articles, characterizing each through its potential structural implications and distinctive imagery. We present this metaphor catalogue in the interest of, first, making metaphors visible as design choices, second, enabling deliberate experimentation with them towards expanding the potential design space of the field, and third, encouraging reflection on the goals and values they embed. 

ABSTRACT Language constitutes an essential set of scientific construction tools, not only for communicating knowledge, but for conceptualizing the world. Metaphors in particular, as conventions that guide and reproduce analogical reasoning, merit attention that they largely do not receive. My research addresses this deficit by examining how metaphors for handling microbes shape possibilities for working with yeast and bacteria in synthetic biology, microbiome research, and other fields that reconfigure what microbes can be. Though poised to reexamine assumptions, these fields routinely rest on metaphors and other language tools that quietly embed ways of thinking that may work against wider aims—for example, imagining bacteria as imperfect machines that should therefore be rendered increasingly passive and controllable. Researchers, therefore, need to examine how language tools structure their observations and expectations so that the tools they choose are appropriate for the work they want to do.