Systems and methods for constructing genomic-based phenotypic models

A computer implemented process for constructing a scalable phenotypic output network model, comprising the computer implemented steps of:

• (a) accessing a database of network gene components comprising an annotated network set of open reading frames (ORFs) of a bioparticle genome;
• (b) forming a data structure associating said network gene components with network reaction components, said data structure establishing a data set specifying a network model of connectivity and flow of said network reaction components;
• (c) modifying said data set to enumerate a biochemical demand on said specified network model, and
• (d) transforming said modified data set into a mathematical description of reactant fluxes defining said network model of connectivity and flow, wherein said enumerated biochemical demand corresponds to an aggregate reactant demand flux defining a phenotypic output of said network model of a bioparticle.

A computer implemented process for self-optimizing a network model of a bioparticle, comprising the computer implemented steps:

• (a) accessing a database of network gene components comprising an annotated network set of open reading frames (ORFs) of a bioparticle genome;
• (b) forming a data structure associating said network gene components with network reaction components, said data structure establishing a data set specifying a network model of connectivity and flow of said network reaction components;
• (c) transforming said data set into a mathematical description of reactant fluxes defining said network model of connectivity and flow;
• (d) determining the competence of said connectivity and flow within said network model, said competence indicating underinclusion or overinclusion of network reaction component content of said network model, and
• (e) identifying an ameliorating network reaction component capable of augmenting said competence of said network model, incorporation of said ameliorating network reaction component into said data structure producing a modified data structure specifying in an optimized network model of said bioparticle.

A computer implemented process for constructing a data structure specifying a network model of a bioparticle, comprising the computer implemented steps:

• (a) accessing a database of network gene components comprising an annotated network set of open reading frames (ORFs) of a bioparticle genome;
• (b) selecting an ORF from said annotated network set encoding a gene product having a network reaction function;
• (c) determining the occurrence of a constituent gene product for said selected encoded gene product;
• (d) determining the occurrence of an additional gene product participating in said network reaction;
• (e) forming a data structure from said selected and determined gene products, said data structure associating said network gene components and network reaction components comprising cognate ORFs, encoded gene products, network reactions and reaction constituents, and
• (f) repeating steps (a)-(e) selecting another ORF from said annotated network set until substantially all of said network gene components of said annotated network set have been surveyed for encoding a gene product having a network reaction function to produce a data structure establishing a data set specifying a network model of connectivity and flow.

A system for constructing a scalable output network model of a bioparticle, comprising:

• (a) an input data set of network gene components comprising an annotated network set of open reading frames (ORFS) of a bioparticle genome;
• (b) executable instructions forming a data structure associating said network gene components with network reaction components, said data structure establishing a data set specifying a network model of connectivity and flow of said network reaction components;
• (c) executable instructions determining the occurrence of a reaction component satisfying a macro requirement deficiency in structural architecture of said network model, inclusion of an identified reaction component satisfying said macro requirement deficiency in said data structure supplementing said connectivity and flow of said network model;
• (d) a heuristic logic decision algorithm determining confidence of said network reaction components within said data structure, and
• (e) executable instructions mathematically describing from said data set reactant fluxes defining said network model of connectivity and flow, wherein said mathematical description defines a scalable output network model of a bioparticle.

A system for constructing a scalable phenotypic output network model of a bioparticle, comprising:

• (a) an input data set of network gene components comprising an annotated network set of open reading frames (ORFs) of a bioparticle genome;
• (b) executable instructions forming a data structure associating said network gene components with network reaction components, said data structure establishing a data set specifying a network model of connectivity and flow of said network reaction components;
• (c) executable instructions modifying said data set to enumerate a biochemical demand on said specified network model, and
• (d) executable instructions mathematically describing from said modified data set reactant fluxes defining said network model of connectivity and flow, wherein said enumerated biochemical demand corresponds to an aggregate reactant demand flux defining a phenotypic output of said network model of said bioparticle.

A system for constructing a self-optimizing network model of a bioparticle, comprising:

• (a) an input data set of network gene components comprising an annotated network set of open reading frames (ORFs) of a bioparticle genome;
• (b) executable instructions forming a data structure associating said network gene components with network reaction components, said data structure establishing a data set specifying a network model of connectivity and flow of said network reaction components;
• (c) executable instructions mathematically describing from said data set reactant fluxes defining said network model of connectivity and flow;
• (d) executable instructions computing competence of said connectivity and flow within said network model, said competence indicating underinclusion or overinclusion of network reaction component content of said network model, and
• (e) executable instructions augmenting said competence of said connectivity and flow within said network model, said executable instructions specifying inclusion or exclusion of an ameliorating network reaction component, wherein incorporation of said ameliorating network reaction component into said data structure produces a modified data structure specifying an optimized network model of said bioparticle.