A break in their own isolation

The qualities described by Sheila and Aaron – being ‘open to learning and helping,’ learning to ‘let go’ of obsessive mastery of a topic or project, valuing ‘enrichment’ – and, in other interviews, of being a ‘constant learner’ who is not ‘arrogant,’ who can ‘ask more questions’ – suggest a social and moral reframing of the ideal interdisciplinary scientist. This reframing depends on scientists’ capacity to embrace a ‘break’ in their own ‘isolation,’ and hence to define themselves relationally, as part of an eclectic and dynamic ecosystem; and to express relational ideals such as humility, helpfulness and mutuality in place of authority and control. — Liron Shani, Talie Fried & Michael M. J. Fischer, “Becoming amphibious: scientists’ identities and affective relations in the swamp of computational biology” (2025)

Why post this? Enjoyed this ethnographic analysis of the “swamp” of computational biology, which has broader relevance as an example of how adaptive capacity manifests in practice. The paper shows researchers developing “relational credibility” – a legitimacy built through openness, not institutional authority. Successful computational biologists develop “sufficient knowledge and sensitivity to understand the other side” through sustained engagement across difference.

Instead of the synthesis and integrative frameworks prioritised by conventional interdisciplinary work¹, this “amphibious” model doesn’t try to resolve tensions between different knowledge types. Instead, it creates viable ways to inhabit uncertainty, to work within those same tensions, operating across domains with different timescales, risk tolerances, and material infrastructure. In computational biology, “getting [one’s] hands wet” isn’t metaphorical, but requires literal hands-on engagement with biological experiments, alongside collaborators from other fields – an embodied translation through mutual learning, with no single participant operating from a position of mastery.²

Yet this raises questions about accessibility. Who can afford to be amphibious? This kind of adaptive capacity isn’t equally available; sustained engagement and vulnerability comes with its own emotional costs. Success in computational biology depends on capacities that most existing evaluation systems neither recognise nor support, motivating researchers to develop informal workarounds and alternative forms of peer validation.

How specific is this to computational biology? The paper’s “swamp” metaphor highlights fluidity and temporality, but where else does making sense of complex problems require crossing between different approaches to risk, time, and materials? When does this amphibious model work, and when (and where) does it break down?