![]() ![]() Evolutionary programming as a platform for in silico metabolic engineering. (2014) A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks. Nam H, Campodonico M, Bordbar A, Hyduke DR, Kim S, et al.We would like to investigate the feasibility of such biological computer taking into account different layers of information. Starting from a recently published work on metabolic machines we would like to compute using additional layers of biological information, such as methylation and protein protein interactions. By turning on a gene set, we turn on the chemical reaction associated with it. Each combination of genes is coded as a string of bits each of which represents a gene set. 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. bacteria) as a living computer, and we are able to program it in order to obtain desired outputs. ![]() In this work, we consider the biochemical pathway in unicellular organisms (e.g. Thus, in a biochemical pathway, an enzyme reads the amount of reactants (substrates) and converts them in products. a processing unit, able to transform an input into an output signal. Moreover, a protein can be thought of as a computational element, i.e. During his studies, he presented a work about a mathematical and computational model of the morphogenesis process, in which chemical substances react together. If Turing were a first-year graduate student interested in computers, he would probably migrate into the field of computational biology. My suggested approach would be R both for evaluation and as a dataset since it has easily accessible source code and is good for statistics, but there are many reasonable approaches.Ĭomputing with multi-omics condition-specific Turing machines The starting point would be to extract a set of simple features, explore their relationships and try to predict metadata like author and date of writing, before either generalizing to more languages or developing more in depth features and more specific hints and advice. This project is about using a machine learning approach to explore these properties of code. Tools like lint and style checkers can be used to check code against manually curated, strict style rules, like spacing style, but when a human reads code to assess quality we tend to also look for subtler and more context specific cues, such as function size and variable descriptiveness. Multi layer and Deep Mining of comorbidities.Computing with multi-omics condition-specific Turing machines. ![]()
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