Method and electronic device for building comprehensive genome scale metabolic model

Abstract

Accordingly, the present embodiment discloses a method for constructing a comprehensive genome-scale metabolic model. The method comprises the step of determining that at least one of an experimentally unidentified virtual profile annotation and a functionally unspecified profile annotation is available in a profile annotation associated with a protein. Additionally, the method comprises the step of performing a machine learning procedure on at least one of the virtual profile annotation and the functionally unspecified virtual profile annotation after a fuzzy string matching and ranking procedure. Additionally, the method comprises the step of obtaining all possible protein annotations based on a fuzzy string matching procedure. Additionally, the method comprises the step of performing a ranking procedure on at least one of the virtual profile annotation and the functionally unspecified profile alternative annotation. Additionally, the method comprises the step of identifying all possible metabolic responses to the protein annotation by a fuzzy string matching procedure. Additionally, the method comprises the step of ranking the protein annotations based on metabolic responses.

Description

Technology Field

[0001] The present disclosure relates to metabolic engineering, and more specifically to a method and electronic device for building a comprehensive genome-scale metabolic model (GSMM). Background Technology

[0003] GSMM enables the exploration of genomic information for metabolic potential and engineering. The construction of GSMM involves the identification of all enzymatic reactions present in the genome. Once reactions are identified, additional reactions must be added using the gap-filling methodology to calculate the growth of the organism. However, the gap-filling methodology often adds spurious reactions that do not exist.

[0004] Automated response identification systems based partially on genome sequence annotations are incomplete. Gap filling of incomplete response sets adds false responses to the GSMM. As shown in Figure 1, in one example, proteins identified with approximately 70% or more are annotated as hypothetical proteins that are predicted to have specific functions in microbes but lack experimental evidence. Metabolic responses inferred from incompletely annotated genomes are incomplete. Gap filling of incomplete response sets adds unwanted / false responses to the GSMM, affecting the results of analysis and prediction.

[0005] GSMM is a powerful approach that enables the computational simulation of various metabolic phenotypes. However, since manually constructing accurate metabolic networks is very time-consuming, it is desirable to have automated computational methods to provide high-quality metabolic networks. Incomplete knowledge of biological chemistry leads to missing, ambiguous, or inaccurate gene annotations, resulting in incomplete metabolic networks. Computational programs designed to fill these gaps in metabolic models rely on network topology-based approaches, which can result in solutions that do not align with existing genomic data. In existing methods, genomic evidence can be used to directly introduce the genomic evidence into the decision-making process for gap-filling responses. This method can be used to maximize the consistency between available genomic data and gap-filled responses and to identify candidate genes for gap-filled responses. This method is an automated metabolic model construction system ( https: / / modelseed.org / KBase, an automated metabolite network reconstruction framework that includes ) tools ( https: / / kbase.us / It was integrated into the metabolic modeling service of ).

[0006] ProbAnnoPy uses an annotation method specialized for glycoproteins in cell membranes. However, this method fails to account for genomic diversity, including gene gains and losses resulting from horizontal gene transfer and gene duplication. The identification of reaction schemes is based on the functional roles of enzymatic reactions. Functional role-based reaction identification effectively fills gaps but can introduce unwanted false reactions.

[0007] In other existing methods, the annotation engine of RAST (Rapid Annotation using Subsystem Technology) annotates bacterial and archaeal genomes. This annotation engine operates by providing a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, it has become possible to build customizable and extensible versions of RAST to make it a more useful research tool and to keep pace with advancements in bioinformatics. The modular version of RAST enables researchers to build custom annotation pipelines. RAST provides not only software functions for identifying and annotating genomic features but also the ability to add custom features to the annotation task. RAST also provides the ability to customize the batch submission of genomes and the protocols for batch submission.

[0008] The RAST system uses K-mer similarity search solely for annotation. RAST also selects and references annotations from related biological genomes. These steps will lead to missing annotations in the genome. Since gene loss and gene gain across microorganisms are very common, the portability of these genes will lead to misidentification of responses.

[0009] Therefore, it is desirable to address the aforementioned disadvantages or other shortcomings, or at least provide a useful alternative. The problem to be solved

[0011] The primary objective of the present invention is to provide a method and an electronic device for constructing a comprehensive genome-scale metabolic model.

[0012] Another objective of the present invention is to obtain protein-associated profile annotation information from a genome annotation database.

[0013] Another objective of the present invention is to determine that at least one of a virtual (function predicted but not experimentally identified) profile annotation and a profile alternative annotation with no specific function is available in a profile annotation associated with a protein.

[0014] Another objective of the present invention is to obtain all possible protein annotations based on a fuzzy string matching procedure.

[0015] Another objective of the present invention is to perform a ranking procedure for at least one of a virtual profile annotation and a profile alternative annotation in which the function is not specified.

[0016] Another objective of the present invention is to identify all possible metabolic responses to protein annotations by a fuzzy string matching procedure.

[0017] Another objective of the present invention is to perform a machine learning procedure on at least one of a virtual profile annotation and a virtual profile alternative annotation in which the function is not specified after a fuzzy string matching and ranking procedure.

[0018] Another objective of the present invention is to rank protein annotations based on metabolic reactions.

[0019] Another objective of the present invention is to perform gap filling and flux balance analysis on the enzyme reaction predicted through ranked annotations. means of solving the problem

[0021] Accordingly, the present embodiment discloses a method for constructing a comprehensive genome-scale metabolic model. The method comprises the step of obtaining a profile annotation associated with a protein from an annotation database by means of an electronic device. Additionally, the method comprises the step of determining by means of an electronic device that at least one of a virtual profile annotation and a profile alternative annotation with no specific function is available in the profile annotation obtained as being associated with the protein. Additionally, the method comprises the step of obtaining all possible protein annotations from the profile annotation obtained by means of an electronic device based on a fuzzy string matching procedure. Additionally, the method comprises the step of performing a ranking procedure for at least one of the virtual profile annotation and the profile alternative annotation with no specific function based on all possible protein annotations obtained by means of an electronic device. Additionally, the method comprises the step of identifying all possible metabolic responses to the protein annotation by means of a fuzzy string matching procedure using the ranking procedure by means of an electronic device. Additionally, the method comprises the step of ranking the protein annotation based on all possible metabolic responses identified by means of an electronic device.

[0022] In one embodiment, the step of obtaining a protein annotation by an electronic device comprises: performing a machine learning procedure on at least one of a virtual profile annotation and a virtual profile alternative annotation with no specific function after a fuzzy string matching procedure and a ranking procedure; and obtaining a protein annotation based on the fuzzy string ranking procedure and the machine learning procedure.

[0023] In one embodiment, the annotation database includes a compilation of sequences having maximum annotation diversity and minimum sequence redundancy.

[0024] In one embodiment, protein annotations are ranked based on at least one of the alignment length of the protein, mismatches in the alignment length of the protein, the gap length of the protein, and the sequence length of the protein.

[0025] In one embodiment, metabolic reactions are identified based on complexes identified in protein annotations and unique occurrences in protein annotations.

[0026] In one embodiment, the method further comprises the step of performing gap filling and flow balance analysis on ranked protein annotations. Additionally, the method further comprises the step of generating a comprehensive GSMM model based on gap filling and flow balance.

[0027] In one embodiment, ranked protein annotations are used to mine metabolic reactions using a fuzzy string matching procedure.

[0028] In one embodiment, the profile annotations are sequence similarity-based profile annotations, response identification compliant profile annotations, and non-sequence similarity-based profile annotations.

[0029] Accordingly, the present embodiment discloses an electronic device for constructing a comprehensive genome-scale metabolic model. The electronic device includes a processor coupled with memory. The processor is configured to obtain profile annotations associated with proteins from an annotation database. Additionally, the processor is configured to determine that at least one of virtual profile annotations and profile annotations with unspecified functions is available from the obtained profile annotations associated with proteins. Additionally, the processor is configured to obtain all possible protein annotations from the obtained profile annotations based on a fuzzy string matching procedure. Additionally, the processor is configured to perform a ranking procedure on at least one of virtual profile annotations and profile alternative annotations with unspecified functions based on all obtained possible protein annotations. Additionally, the processor is configured to identify all possible metabolic responses to the protein annotations by fuzzy string matching using the ranking procedure. Additionally, the processor is configured to rank the protein annotations based on all identified possible metabolic responses.

[0030] These and other aspects of the embodiments of the present invention will be better recognized and understood when considered in conjunction with the following description and the accompanying drawings. However, it should be understood that while the following description illustrates preferred embodiments and many specific details thereof, it is provided as an example rather than a limitation. Many changes and variations may be made within the scope of the embodiments of the present invention without departing from the spirit thereof, and the embodiments of the present invention include all such variations. Brief explanation of the drawing

[0032] The method is illustrated in the attached drawings, in which similar reference numerals indicate corresponding parts in various drawings. The embodiments of the present invention will be better understood from the following description with reference to the drawings. Figure 1 illustrates the process of constructing a comprehensive genome-scale metabolic model. FIG. 2 illustrates various hardware components of an electronic device for constructing a comprehensive genome-scale metabolic model according to one embodiment as disclosed herein; FIG. 3 illustrates various hardware components of a processor included in an electronic device according to one embodiment as disclosed herein; FIG. 4 is a flowchart illustrating a method for constructing a comprehensive genome-scale metabolic model according to one embodiment as disclosed herein; FIG. 5 is an exemplary flowchart illustrating a method for constructing a comprehensive genome-scale metabolic model according to one embodiment as disclosed herein; and FIG. 6 is another exemplary process flow illustrating a method for constructing a comprehensive genome-scale metabolic model according to one embodiment disclosed herein. Specific details for implementing the invention

[0033] The embodiments of the present invention and their various features and advantageous details are more fully explained by reference to non-limiting embodiments shown in the accompanying drawings and described in detail in the following description. Descriptions of well-known components and processing techniques are omitted to avoid unnecessarily obscuring the embodiments of the present invention. Furthermore, since some embodiments may be combined with one or more other embodiments to form new embodiments, the various embodiments disclosed herein are not necessarily mutually exclusive. Unless otherwise indicated, the term "or" as used herein refers to non-exclusiveness. The embodiments used herein are intended merely to facilitate understanding of the manner in which the embodiments of the present invention may be practiced and to enable a person skilled in the art to further practice the embodiments of the present invention. Accordingly, the embodiments should not be interpreted as limiting the scope of the embodiments of the present invention.

[0034] As is customary in the art, embodiments may be described and illustrated by blocks that perform the described functions or functions. These blocks, which may be referred to herein as units or modules, etc., are physically implemented by analog or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, etc., and may optionally be driven by firmware and software. The circuit may be embodied, for example, as one or more semiconductor chips, or on substrate supports such as printed circuit boards. A circuit comprising a block may be implemented by dedicated hardware or a processor (e.g., one or more programmed microprocessors and associated circuits), or by a combination of dedicated hardware for performing some functions of the block and a processor for performing other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and distinct blocks without departing from the scope of the invention. Likewise, blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the invention.

[0035] It should be understood that the attached drawings are used to facilitate understanding of various technical features and that the embodiments presented herein are not limited by the attached drawings. As such, the present disclosure should be interpreted to extend to any alterations, equivalents, and substitutes other than those specifically described in the attached drawings. Terms such as "first," "second," etc., may be used herein to describe various elements, but these elements should not be limited by these terms. These terms are generally used only to distinguish one element from another.

[0036] Accordingly, the present embodiment achieves a method for constructing a comprehensive genome-scale metabolic model. The method comprises the step of obtaining a profile annotation associated with a protein from an annotation database by means of an electronic device. Additionally, the method comprises the step of determining by means of an electronic device that at least one of a virtual profile annotation and a profile alternative annotation with no specific function is available in the profile annotation obtained as being associated with the protein. Additionally, the method comprises the step of obtaining all possible protein annotations from the profile annotation obtained by means of an electronic device based on a fuzzy string matching procedure. Additionally, the method comprises the step of performing a ranking procedure for at least one of the virtual profile annotation and the profile alternative annotation with no specific function based on all possible protein annotations obtained by means of an electronic device. Additionally, the method comprises the step of identifying all possible metabolic responses to the protein annotation by means of a fuzzy string matching procedure using the ranking procedure by means of an electronic device. Additionally, the method comprises the step of ranking the protein annotation based on all possible metabolic responses identified by means of an electronic device.

[0037] Unlike conventional methods and systems, the proposed method can be used to maximize the number of genomic annotations identifiable from the genome by utilizing a fuzzy string matching procedure. The fuzzy string matching procedure is a technique for finding strings that match patterns approximately (approximately rather than exactly). Once annotations are identified, the proposed method can be used to rank each annotation according to alignment parameters. These ranked annotations are used to discover metabolic reactions and are ranked based on the uniqueness of the identified reactions and complexes. This significantly improves the number of reactions identified from the genome and reduces the addition of gap-filling reaction equations.

[0038] The proposed method can be used to automatically construct genome-scale metabolic models from genome sequences. In the proposed method, integrated sequence homology and machine learning genome annotation methodologies can annotate the largest number of proteins in the genome under investigation. Reaction identification based on fuzzy string matching procedures from ranked annotations reduces gap-filling reaction equations, enabling comprehensive genome-scale metabolic models in an accurate manner. Identifying a larger number of genome-specific reactions leads to high-quality genome-scale metabolic models. Through deep, high-quality genome-scale metabolic models, industrial biotechnologists can explore opportunities in synthetic biology, metabolic engineering, and strain design across biopharma, bioremediation, and bioproduction of high-value compounds.

[0039] The above method can be used to identify maximum genome-specific responses based on specific genome annotations, provide actionable metabolic responses from inferred annotations, and reduce hypothetical, un-annotated proteins during genome annotation.

[0040] The above method can be used to identify responses mapped to ranked annotations of proteins based on a fuzzy string matching procedure. Based on the proposed method, most of the microbial genome is annotated. The above method can be used to identify responses from a given genome.

[0041] In the proposed method, genome annotation is performed using a user-defined annotation database having maximum annotation diversity and minimum sequence redundancy. The method can be used to enhance the identification of reactions from annotated enzymes. The method can be used to annotate the genome for accurate protein annotation and subsequent metabolic reaction identification, which enables the construction of genome-scale metabolic models.

[0042] Here, with reference to the drawings, more specifically to FIGS. 2 to 6, a preferred embodiment is shown.

[0043] FIG. 2 illustrates various hardware components of an electronic device (100) for constructing a comprehensive genome-scale metabolic model according to one embodiment as disclosed herein. The electronic device (100) may be, for example, a cellular phone, a smartphone, a Personal Digital Assistant (PDA), a tablet computer, a laptop computer, a smart watch, an Internet of Things (IoT), but is not limited thereto.

[0044] In one embodiment, the electronic device (100) includes a processor (110), a communicator (120), a memory (130), a display (140), and an annotation database (150). The processor (110) is connected to the communicator (120), the memory (130), the display (140), and the annotation database (150).

[0045] In one embodiment, the processor (110) is configured to obtain a profile annotation associated with a protein from an annotation database (150). The profile annotation may be, for example, an HMMER profile annotation, a BLAST profile annotation, etc., but is not limited thereto. The profile annotation may be used to identify homologous proteins or nucleotide sequences and to perform sequence alignment.

[0046] In one embodiment, the profile annotations are sequence similarity-based profile annotations, response identification conformity profile annotations, and non-sequence similarity-based profile annotations.

[0047] In one embodiment, the annotation database (150) includes a compilation of sequences having maximum annotation diversity and minimum sequence redundancy. The annotation database (150) is provided with maximum annotation diversity and minimum sequence redundancy. Table 1 shows knowledgebase creation and assessment.

[0048] Annotation database Number of sequences Curated database Uniprot (EC-numbered protein from the bacterial kingdom) 194,191 155,418 Metacyc protein sequence 10,630 10,630 SEED database 32,415,017 15,974,003 gun 32,619,838 16,140,051

[0049] Additionally, the processor (110) is configured to determine that at least one of the virtual profile annotations and the profile annotations for which no function is specified is available in the profile annotations associated with the protein. Additionally, the processor (110) is configured to obtain all possible protein annotations based on a fuzzy string matching procedure. Additionally, the processor (110) is configured to perform a ranking procedure for at least one of the virtual profile annotations and the profile alternative annotations for which no function is specified. Additionally, the processor (110) is configured to identify all possible metabolic responses to the protein annotations by a fuzzy string matching procedure. Additionally, the processor (110) is configured to rank the protein annotations based on the metabolic responses.

[0050] In one embodiment, protein annotations are obtained by performing a fuzzy string matching and ranking procedure, and then performing a machine learning procedure on at least one of a virtual profile annotation and a virtual profile replacement annotation in which the function is not specified.

[0051] In one embodiment, Table 2 shows protein annotations based on sequence features using machine learning procedures such as support vector machine (SVM), RF, and deep learning.

[0052] Contig Prokka annotation Predicted EC Quality score EC annotation MC_contig_00043 virtual protein 1.1.1.193 0.87 5-amino-6-(5-phosphoribosylamino)uracial reducatase (EC 1.1.1.1) MC_contig_00043 virtual protein 1.14.13.- 0.78 2-Octaprenyl-3-Methyl-6-Methoxy-1,4-Benzoquinol Hydroxylase (2-OCTAPRENYL-3-METHYL-6-METHOXY-1,4-BENZOQUINOL HYDROXYLASE) (EC 1.14.13.-) MC_contig_00043 virtual protein 1.14.13.- 0.78 Vanillate O-Dimethylase Oxidoreductase (EC 1.14.13.-) MC_contig_00043 virtual protein 1.14.13.- 0.78 Nitrilotriacetate monooxygenase component A (EC 1.14.13.-) MC_contig_00043 virtual protein 3.5.4.26 0.87 Diaminohydroxyphosphoribosylaminopyrimidine deaminase (EC 3.5.4.26)

[0053] In one embodiment, protein annotations are ranked based on at least one of the alignment length of the protein, the mismatch in the alignment length of the protein, the gap length of the protein, the sequence length of the protein, etc. Ranking for protein annotations is performed based on the following formula (1-3). Table 3 shows fuzzy string matching (see drawing sheet).

[0054] f align = alignment length / sequence length … (1)

[0055] f mismatch = discrepancy + gap length + e value … (2)

[0056] Rank = f align / ( f align+ f mismatch) … (3)

[0057]

[0058] In one embodiment, metabolic reactions are identified based on complexes identified in protein annotations and unique occurrences in protein annotations.

[0059] In one embodiment, ranked possible gene annotations are used to identify responses from the modelSEED database using a fuzzy string matching procedure. The identified responses are filtered and ranked based on the identified complexes and unique occurrences as shown in Table 4.

[0060] Prokka annotation Feature-based annotation Identified complex Identified response MCC_contig_00043 virtual protein 5-amino-6-(5-phosphoribosylamino)uracil reductase (EC 1.1.1.193) [cpx.1021, cpx.543, cpx.2459] [rxn02474, rxn02475] MCC_contig_00043 virtual protein Diaminohydroxyphosphoribosylaminopyrimidine deaminase (EC 3.5.4.26) [cpx.1021, cpx.3143] [rxn02475]

[0061] In one embodiment, the processor (110) is configured to perform gap filling and flow balance analysis on ranked protein annotations. Additionally, the processor (110) is configured to generate a GSMM model based on the gap filling and flow balance. In one embodiment, the ranked protein annotations are used to discover metabolic reactions using a fuzzy string matching procedure.

[0062] The processor (110) is configured to execute instructions stored in memory (130) and perform various processes. The communication unit (120) is configured to communicate internally between internal hardware components and external devices through one or more networks. In one embodiment, the annotation database (150) is part of memory (130). In another embodiment, the annotation database (150) is outside of memory (130).

[0063] The memory (130) also stores instructions to be executed by the processor (110). The memory (130) may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memory (EPROM) or electrically erasable and programmable memory (EEPROM). Additionally, in some embodiments, the memory (130) may be considered a non-transitory storage medium. The term "non-transitory" may indicate that the storage medium is not embodied by a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted as meaning that the memory (130) is non-movable. In some embodiments, the memory (130) may be configured to store a greater amount of information than the memory. In certain embodiments, the non-transient storage medium may store data that may change over time (e.g., Random Access Memory (RAM) or cache).

[0064] It should be understood that while FIG. 2 illustrates various hardware components of the electronic device (100), other embodiments are not limited thereto. In other embodiments, the electronic device (100) may include fewer or more components. Additionally, the labels or names of the components are used for illustrative purposes only and do not limit the scope of the invention. One or more components may be combined to perform the same or substantially similar functions to construct a comprehensive genome-scale metabolic model.

[0065] FIG. 3 illustrates various hardware components of a processor (110) according to one embodiment as disclosed herein. In one embodiment, the processor (110) comprises a profile annotation engine (110a), a fuzzy string ranking engine (110b), a machine learning modeler (110c), and a protein annotation ranking engine (110d). The profile annotation engine (110a) is configured to obtain a profile annotation associated with a protein from an annotation database (150). The fuzzy string ranking engine (110b) is configured to determine that at least one of a virtual profile annotation and a profile alternative annotation with no specific function is available in the profile annotation associated with the protein.

[0066] The machine learning modeler (110c) is configured to perform a machine learning procedure on at least one of the virtual profile annotations and virtual profile alternative annotations with unspecified functions after a fuzzy string matching and ranking procedure. The protein annotation ranking engine (110d) is configured to obtain all possible protein annotations based on the fuzzy string matching procedure and to perform a ranking procedure on at least one of the virtual profile annotations and profile alternative annotations with unspecified functions. Additionally, the protein annotation ranking engine (110d) is configured to identify all possible metabolic responses to the protein annotations by the fuzzy string matching procedure and the machine learning procedure. Additionally, the protein annotation ranking engine (110d) is configured to rank the protein annotations based on the metabolic responses.

[0067] It should be understood that while FIG. 3 illustrates various hardware components of the processor (110), other embodiments are not limited thereto. In other embodiments, the processor (110) may include fewer or more components. Additionally, the labels or names of the components are used for illustrative purposes only and do not limit the scope of the invention. One or more components may be combined to perform the same or substantially similar functions to construct a comprehensive genome-scale metabolic model.

[0068] FIG. 4 is a flowchart (400) illustrating a method for constructing a comprehensive genome-scale metabolic model according to one embodiment as disclosed herein. The operations (402-412) are performed by a processor (110).

[0069] In 402, the method comprises the step of obtaining a profile annotation associated with a protein from an annotation database (150). In 404, the method comprises the step of determining that at least one of a virtual profile annotation and a profile alternative annotation with no specific function is available in the profile annotation associated with the protein. In 406, the method comprises the step of obtaining all possible protein annotations based on a fuzzy string matching procedure. In 408, the method comprises the step of performing a ranking procedure on at least one of a virtual profile annotation and a profile alternative annotation with no specific function. In 410, the method comprises the step of identifying all possible metabolic responses to the protein annotation by a fuzzy string matching procedure, and in 412, the method comprises the step of ranking the protein annotation based on the metabolic response.

[0070] FIG. 5 is an exemplary flowchart (500) illustrating a method for constructing a comprehensive genome-scale metabolic model according to one embodiment as disclosed herein. The operations (502-524) are performed by a processor (110).

[0071] In 502, the method comprises the step of obtaining a genome sequence. In 502, the method comprises the step of identifying a protein from the genome sequence. In 506, the method comprises the step of obtaining a profile annotation associated with the protein (e.g., HMMER profile annotation, BLAST profile annotation, etc.) from an annotation database (150). In 508, the method comprises the step of determining that at least one of a virtual profile annotation and a profile alternative annotation with no specific function is available in the profile annotation associated with the protein.

[0072] If at least one of the virtual profile annotation and the profile alternative annotation whose function is not specified is available in the profile annotation associated with the protein, in 510, the method comprises the step of obtaining all possible protein annotations based on a fuzzy string matching procedure.

[0073] In 512, the method comprises the step of determining that at least one of a virtual profile annotation and a profile alternative annotation with no specific function is available in a profile annotation associated with a protein after a fuzzy string matching procedure. If at least one of a virtual profile annotation and a profile alternative annotation with no specific function is available in a profile annotation associated with a protein, in 514, the method comprises the step of performing a machine learning procedure on at least one of a virtual profile annotation and a virtual profile annotation with no specific function after a fuzzy string matching and ranking procedure. In 516, the method comprises the step of determining that at least one of a virtual profile annotation and a profile alternative annotation with no specific function is available in a profile annotation associated with a protein after a fuzzy string ranking procedure and a machine learning procedure.

[0074] In 518, the method comprises the step of obtaining a protein annotation. In 520, the method comprises the step of performing a ranking procedure on at least one of a virtual profile annotation and a profile alternative annotation for which function is not specified. Additionally, the method comprises the step of identifying all possible metabolic responses to the protein annotation. In 522, the method comprises the step of ranking the protein annotation based on the metabolic response. In 522, the method comprises the step of performing gap filling and flow balancing analysis on the ranked protein annotation. In 524, the method comprises the step of generating a GSMM model based on the gap filling and flow balancing.

[0075] Another exemplary process flow illustrating a method for constructing a comprehensive genome-scale metabolic model is described in Fig. 6. The operations in Fig. 6 are similar to the operations in Fig. 5.

[0076] Various actions, acts, blocks, steps, etc. in the flowcharts (400 and 500) may be performed in the presented order, in a different order, or simultaneously. Additionally, in some embodiments, some of the actions, acts, blocks, steps, etc. may be omitted, added, modified, skipped, etc. without departing from the scope of the present invention.

[0077] The embodiments disclosed herein may be implemented using at least one software program that operates on at least one hardware device and performs network management functions to control elements.

[0078] The foregoing description of specific embodiments is intended to sufficiently reveal the general nature of the embodiments thereof so that a person skilled in the art can easily modify and / or adapt various applications such as these specific embodiments by applying current knowledge without departing from the general concept; therefore, such adaptation and modification are intended to be understood within the scope of the meaning and equivalents of the disclosed embodiments. It should be understood that the phraseology or terminology used herein is for illustrative purposes only and is not intended to be limiting. Accordingly, although the embodiments thereof have been described in the view of preferred embodiments, a person skilled in the art will recognize that the embodiments thereof may be modified and practiced within the spirit and scope of the embodiments as described herein.

Claims

Claim 1 A method for building a comprehensive genome-scale metabolic model comprising: obtaining a profile annotation associated with a protein from an annotation database (150) by means of an electronic device (100); determining by means of an electronic device (100) that at least one of a hypothetical profile annotation not experimentally identified and a profile alternative annotation with uncharacterized function is available in the obtained profile annotation associated with the protein; obtaining all possible protein annotations from the profile annotation determined to be available in the obtained profile annotation associated with the protein based on a fuzzy string matching procedure by means of an electronic device (100); performing a ranking procedure on at least one of the hypothetical profile annotation and the profile alternative annotation with uncharacterized function based on all possible protein annotations obtained by means of an electronic device (100); A step of identifying all possible metabolic reactions for a protein annotation by a fuzzy string matching procedure using a ranking procedure by a device (100); and a step of ranking the protein annotation based on all possible metabolic reactions identified by an electronic device (100). Claim 2 A method according to claim 1, comprising: a step of obtaining all possible protein annotations from profile annotations determined to be available in a secured profile annotation associated with a protein based on a fuzzy string matching procedure by an electronic device (100); and further comprising a step of obtaining machine-learned protein annotations by performing a machine-learned procedure on at least one of a virtual profile annotation and a virtual profile alternative annotation not specified in function after performing a rank procedure on at least one of the virtual profile annotation and a virtual profile alternative annotation not specified in function based on all possible protein annotations obtained by the electronic device (100), wherein, in the step of identifying all possible metabolic responses for protein annotations by a fuzzy string matching procedure using a rank procedure by the electronic device (100), the “protein annotation” is the “obtained machine-learned protein annotation.” Claim 3 A method according to claim 1, wherein the annotation database comprises a compilation of sequences having maximum annotation diversity and minimum sequence redundancy. Claim 4 A method according to claim 1, wherein the protein annotation is ranked based on at least one of the alignment length of the protein, mismatches of the alignment length of the protein, the gap length of the protein, and the sequence length of the protein. Claim 5 The method of claim 1, wherein the metabolic reaction is identified based on the complex identified in the protein annotation and the uniqueness of the reaction in the protein annotation. Claim 6 The method of claim 1, further comprising: performing gap filling and flux balance analysis on ranked protein annotations; and generating a Genome Scale Metabolic Model (GSMM) based on gap filling and flux balance. Claim 7 The method of claim 1, wherein the ranked protein annotation is used to mine metabolic reactions using a fuzzy string matching procedure. Claim 8 The method of claim 1, wherein the profile annotations are sequence similarity-based profile annotations, response identification compliant profile annotations, and non-sequence similarity-based profile annotations. Claim 9 As an electronic device (100) for constructing a comprehensive genome-scale metabolic model, the electronic device (100) comprises a memory (130); and a processor (110) coupled to the memory (130), wherein the processor (110) obtains a profile annotation associated with a protein from an annotation database (150); determines that at least one of a virtual profile annotation and a profile annotation with no specific function is available in the obtained profile annotation associated with the protein; obtains all possible protein annotations from the profile annotation determined to be available in the obtained profile annotation associated with the protein based on a fuzzy string matching procedure; performs a ranking procedure for at least one of a virtual profile annotation and a profile alternative annotation with no specific function based on all possible protein annotations obtained; and identifies all possible metabolic responses for the protein annotation by fuzzy string matching using the ranking procedure; An electronic device (100) configured to rank protein annotations based on all identified possible metabolic reactions. Claim 10 The electronic device (100) of claim 9 further comprises obtaining all possible protein annotations from profile annotations determined to be available in obtained profile annotations associated with a protein based on a fuzzy string matching procedure; and, after performing a ranking procedure on at least one of virtual profile annotations and profile alternative annotations not specified in function based on all possible protein annotations obtained, performing a machine learning procedure on at least one of virtual profile annotations and virtual profile annotations not specified in function to obtain machine learned protein annotations, and, in "identifying all possible metabolic responses for protein annotations by fuzzy string matching using a ranking procedure," wherein "protein annotations" are "obtained machine learned protein annotations." Claim 11 An electronic device (100) according to claim 9, wherein the annotation database comprises a compilation having maximum annotation diversity and minimum sequence redundancy. Claim 12 An electronic device (100) according to claim 9, wherein the protein annotation is ranked based on at least one of the alignment length of the protein, the mismatch in the alignment length of the protein, the gap length of the protein, and the sequence length of the protein. Claim 13 An electronic device (100) according to claim 9, wherein the metabolic reaction is identified based on the complex identified in the protein annotation and the uniqueness of the reaction in the protein annotation. Claim 14 In claim 9, the processor (110) performs gap filling and flow balance analysis on ranked protein annotations; and the electronic device (100) is configured to generate a genome-scale metabolic model (GSSM) based on gap filling and flow balance. Claim 15 In claim 9, the electronic device (100) is used to discover metabolic reactions using a fuzzy string matching procedure for ranked protein annotations. Claim 16 An electronic device (100) according to claim 9, wherein the profile annotations are sequence similarity-based profile annotations, response identification conformation profile annotations, and non-sequence similarity-based profile annotations.

Images