Literature DB >> 34269947

Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity.

Juhyun Kim1, Rafael Silva-Rocha2, Víctor de Lorenzo3.   

Abstract

Any descriptive language is necessarily metaphoric and interpretative. Two somewhat overlapping-but not identical-languages have been thoroughly employed in the last decade to address the issue of regulatory complexity in biological systems: the terminology of network theory and the jargon of electric circuitry. These approaches have found many formal equivalences between the layout of extant genetic circuits and the architecture of man-made counterparts. However, these languages still fail to describe accurately key features of biological objects, in particular the diversity of signal-transfer molecules and the diffusion that is inherent to any biochemical system. Furthermore, current formalisms associated with networks and circuits can hardly face the problem of multi-scale regulatory complexity-from single molecules to entire ecosystems. We argue that the language of economic theory might be instrumental not only to portray accurately many features of regulatory networks, but also to unveil aspects of the biological complexity problem that remain opaque to other types of analyses. The main perspective opened by the economic metaphor when applied to control of microbiological activities is a focus on metabolism, not gene selfishness, as the necessary background to make sense of regulatory phenomena. As an example, we analyse and reinterpret the widespread phenomenon of catabolite repression with the formal frame of the consumer's choice theory.
© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Entities:  

Keywords:  Catabolic repression; Genetic circuits; Networks; Pseudomonas; Resource allocation

Mesh:

Year:  2021        PMID: 34269947     DOI: 10.1007/s10123-021-00194-w

Source DB:  PubMed          Journal:  Int Microbiol        ISSN: 1139-6709            Impact factor:   2.479


  51 in total

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