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Computing with Metabolic Machines

15 pagesPublished: June 22, 2012


If Turing were a first-year graduate student interested in computers,
he would probably migrate into the field of computational biology. During his studies, he presented
a work about a mathematical and computational model of the morphogenesis process, in which chemical substances
react together. Moreover, a protein can be thought of as a computational element, i.e. a processing unit, able to
transform an input into an output signal. Thus, in a biochemical pathway, an enzyme reads the amount of reactants (substrates)
and converts them in products. In this work, we consider the biochemical pathway in unicellular organisms (e.g. bacteria) as a living computer, and we are able to program it in order to obtain desired outputs.
The genome sequence is thought of as an executable code specified by a set of commands in a sort of ad-hoc low-level programming language. Each combination of genes is coded as a string of bits $y \in \left \{ 0 , 1 \right \}^L$, each of which represents a gene set. By turning off a gene set, we turn off the chemical reaction associated with it. Through an optimal executable code stored in the ``memory'' of bacteria, we are able to simultaneously maximise the concentration of two or more metabolites of interest.
Finally, we use the Robustness Analysis and a new Sensitivity Analysis method to investigate both the fragility of the computation carried out by bacteria and the most important entities in the mathematical relations used to model them.

Keyphrases: Biological CAD, metabolic machine, Pareto optimality, Sensitive and Fragile Biological Circuits, Sensitivity and Robustness Analysis

In: Andrei Voronkov (editor). Turing-100. The Alan Turing Centenary, vol 10, pages 1--15

BibTeX entry
  author    = {Claudio Angione and Giovanni Carapezza and Jole Costanza and Pietro Lio and Giuseppe Nicosia},
  title     = {Computing with Metabolic Machines},
  booktitle = {Turing-100. The Alan Turing Centenary},
  editor    = {Andrei Voronkov},
  series    = {EPiC Series in Computing},
  volume    = {10},
  pages     = {1--15},
  year      = {2012},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/t48n}}
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