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How to Make a Synthetic Multicellular Computer

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  • Javier Macia
  • Ricard Sole

Abstract

Biological systems perform computations at multiple scales and they do so in a robust way. Engineering metaphors have often been used in order to provide a rationale for modeling cellular and molecular computing networks and as the basis for their synthetic design. However, a major constraint in this mapping between electronic and wet computational circuits is the wiring problem. Although wires are identical within electronic devices, they must be different when using synthetic biology designs. Moreover, in most cases the designed molecular systems cannot be reused for other functions. A new approximation allows us to simplify the problem by using synthetic cellular consortia where the output of the computation is distributed over multiple engineered cells. By evolving circuits in silico, we can obtain the minimal sets of Boolean units required to solve the given problem at the lowest cost using cellular consortia. Our analysis reveals that the basic set of logic units is typically non-standard. Among the most common units, the so called inverted IMPLIES (N-Implies) appears to be one of the most important elements along with the NOT and AND functions. Although NOR and NAND gates are widely used in electronics, evolved circuits based on combinations of these gates are rare, thus suggesting that the strategy of combining the same basic logic gates might be inappropriate in order to easily implement synthetic computational constructs. The implications for future synthetic designs, the general view of synthetic biology as a standard engineering domain, as well as potencial drawbacks are outlined.

Suggested Citation

  • Javier Macia & Ricard Sole, 2014. "How to Make a Synthetic Multicellular Computer," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-13, February.
  • Handle: RePEc:plo:pone00:0081248
    DOI: 10.1371/journal.pone.0081248
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    References listed on IDEAS

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    1. Paul Nurse, 2008. "Life, logic and information," Nature, Nature, vol. 454(7203), pages 424-426, July.
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    Cited by:

    1. Alex J. H. Fedorec & Neythen J. Treloar & Ke Yan Wen & Linda Dekker & Qing Hsuan Ong & Gabija Jurkeviciute & Enbo Lyu & Jack W. Rutter & Kathleen J. Y. Zhang & Luca Rosa & Alexey Zaikin & Chris P. Bar, 2024. "Emergent digital bio-computation through spatial diffusion and engineered bacteria," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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