Discovering adaptation-capable biological network structures using control-theoretic approaches

On 17 December, 2021

Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every living organism to sense a change in its surroundings and return to its operating condition prior to the disturbance. In this paper, we present a generic systems theory-driven method for designing adaptive protein networks. First, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as ‘design requirements’ for the underlying networks. We go on to prove that a protein network with different input–output nodes (proteins) needs to be at least of third-order in order to provide adaptation. Next, we show that the necessary design principles obtained for a three-node network in adaptation consist of negative feedback or a feed-forward realization. We argue that presence of a particular class of negative feedback or feed-forward realization is necessary for a network of any size to provide adaptation. Further, we claim that the necessary structural conditions derived in this work are the strictest among the ones hitherto existed in the literature. Finally, we prove that the capability of producing adaptation is retained for the admissible motifs even when the output node is connected with a downstream system in a feedback fashion. This result explains how complex biological networks achieve robustness while keeping the core motifs unchanged in the context of a particular functionality. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic and robust framework for designing various kinds of biological networks.

Blog article: https://tech-talk.iitm.ac.in/discovering-cellular-networks-for-biological-adaptation/ (IITM Tech Talk)

Original Paper: 

  • [DOI] P. Bhattacharya, K. Raman, and A. K. Tangirala, “Discovering adaptation-capable biological network structures using control-theoretic approaches,” PLoS Computational Biology, vol. 18, iss. 1, p. e1009769, 2022.
    [bibtex]
    @article{Bhattacharya2022Discovering,
      title = {Discovering adaptation-capable biological network structures using control-theoretic approaches},
      volume = {18},
      issn = {1553-7358},
      url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009769},
      doi = {10.1371/journal.pcbi.1009769},
      abstract = {Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every living organism to sense a change in its surroundings and return to its operating condition prior to the disturbance. In this paper, we present a generic systems theory-driven method for designing adaptive protein networks. First, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as ‘design requirements’ for the underlying networks. We go on to prove that a protein network with different input–output nodes (proteins) needs to be at least of third-order in order to provide adaptation. Next, we show that the necessary design principles obtained for a three-node network in adaptation consist of negative feedback or a feed-forward realization. We argue that presence of a particular class of negative feedback or feed-forward realization is necessary for a network of any size to provide adaptation. Further, we claim that the necessary structural conditions derived in this work are the strictest among the ones hitherto existed in the literature. Finally, we prove that the capability of producing adaptation is retained for the admissible motifs even when the output node is connected with a downstream system in a feedback fashion. This result explains how complex biological networks achieve robustness while keeping the core motifs unchanged in the context of a particular functionality. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic and robust framework for designing various kinds of biological networks.},
      language = {en},
      number = {1},
      urldate = {2022-01-27},
      journal = {PLoS Computational Biology},
      author = {Bhattacharya, Priyan and Raman, Karthik and Tangirala, Arun K.},
      month = jan,
      year = {2022},
      note = {Publisher: Public Library of Science},
      keywords = {Network analysis, Network motifs, Eigenvalues, Control theory, Directed graphs, Dynamical systems, Peak values, Transfer functions},
      pages = {e1009769},
      file = {Full Text PDF:C\:\\Users\\Karthik\\Zotero\\storage\\5THKKWIH\\Bhattacharya et al. - 2022 - Discovering adaptation-capable biological network .pdf:application/pdf;Snapshot:C\:\\Users\\Karthik\\Zotero\\storage\\JXV5MYW8\\article.html:text/html},
    }

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