Abstract
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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-15 |
| Journal | Turing-100. Volume 10 of EPiC Series |
| Volume | 10 |
| DOIs | |
| Publication status | Published - 2012 |