{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T21:42:28Z","timestamp":1747345348397},"reference-count":0,"publisher":"EasyChair","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>If Turing were a first-year graduate student interested in computers,<\/jats:p><jats:p>he would probably migrate into the field of computational biology. During his studies, he presented<\/jats:p><jats:p>a work about a mathematical and computational model of the morphogenesis process, in which chemical substances<\/jats:p><jats:p>react together. Moreover, a protein can be thought of as a computational element, i.e. a processing unit, able to<\/jats:p><jats:p>transform an input into an output signal. Thus, in a biochemical pathway, an enzyme reads the amount of reactants (substrates)<\/jats:p><jats:p>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.<\/jats:p><jats:p>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.<\/jats:p><jats:p>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.<\/jats:p>","DOI":"10.29007\/t48n","type":"proceedings-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T17:57:36Z","timestamp":1516730256000},"page":"1--15","source":"Crossref","is-referenced-by-count":2,"title":["Computing with Metabolic Machines"],"prefix":"10.29007","volume":"10","author":[{"given":"Claudio","family":"Angione","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni","family":"Carapezza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jole","family":"Costanza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Lio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Nicosia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"11545","event":{"name":"Turing-100. The Alan Turing Centenary"},"container-title":["EPiC Series in Computing"],"original-title":[],"deposited":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T17:57:43Z","timestamp":1516730263000},"score":1,"resource":{"primary":{"URL":"https:\/\/easychair.org\/publications\/paper\/FRz"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[null]]},"references-count":0,"URL":"https:\/\/doi.org\/10.29007\/t48n","relation":{},"ISSN":["2398-7340"],"issn-type":[{"type":"print","value":"2398-7340"}],"subject":[]}}