In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products  associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process  Over years, several methodologies have been proposed to integrate omics data into metabolic models in order to derive context-specific networks that represent the active portion of the network under specific conditions. In this way, biologically meaningful phenotypic predictions can be derived as a function of genes expression profiles encoding for subunits or isoforms of the involved enzymes. Regardless of the used approach to integrate omics data, the reliability of the formulated hypotheses strongly depends on the quality of gene-protein-reaction (GPR) rules included into the models, which describe how gene products concur to catalyze the associated reactions. To date, the reconstruction of GPR rules for their integration within metabolic networks still remains a largely manual and time consuming process. Therefore, we propose the open-source framework GPRuler to automate the reconstruction process of GPR rules for any living organism. Applying the developed tool to four case studies, we verified the ability of GPRuler to reproduce the original GPR rules with a very high level of accuracy. Moreover, in all the tested scenarios, the proposed approach revealed to be in many cases more accurate than the original models. Current advances in genome sequencing technologies enable a fast and cheap overview into the genetic composition of virtually any organism. Nevertheless, determining the global metabolic profile of a cell or organism is fundamental to provide a comprehensive readout of its functional state, resulting from the interplay between genome, biochemistry and environment. In this context, genome-scale metabolic models (GEMs) offer a systemic overview for the investigation of cell metabolic potential, because of their key feature of embracing all available knowledge about the biochemical transformations taking place in a given cell or organism .Over the years, extensive efforts have been made to automate the reconstruction process of these models, reaching high levels of quality and detail. However, less attention has been dedicated to clarify the associations among the set of genes involved in the catalysis of a given reaction.According to the underlying catalytic mechanism, metabolic reactions can be classified as non-enzymatic and enzymatic. In the first scenario, metabolic reactions occur either spontaneously or catalyzed by small molecules, implying that no gene is necessary for their catalysis. In the second case, enzymatic reactions occur only when catalyzed by specific enzymes, which are protein macromolecules that are specifically responsible for reactions catalysis . From thCombining these two operators into expressions allow complex scenarios to be described, such as multiple oligomeric enzymes behaving as isoforms due to the sharing of a common part and to the presence of one or more subunits constituting distinctive features of the different isoforms.AND operator to join genes encoding for different subunits of the same enzyme, and the OR operator to join genes encoding for distinct protein isoforms of the same enzyme or subunit.In GEMs, the associations between genes, proteins and reactions and the description of how gene products concur to catalyze the associated reaction are usually encoded through logical expressions typically referred to as gene-protein-reaction (GPR) rules . GPR rulWe performed a survey of literature on gene-protein-reaction rules and reconstructed a network of citing and cited papers to highlight the most adopted strategies and main data sources used, as reported in RAVEN  Toolbox 2.0 [The analysis of current formally defined pipelines for the automatic generation of GPR rules in metabolic reconstructions highlighted two different categories of tools. In the first one, we found tools able to reconstruct the association reaction-genes without determining the Boolean operators connecting them. Hence time-consuming manual contribution is required to obtain the final correct GPRs. Just to name one tool within this category, the lbox 2.0  is a MATlbox 2.0  that assSimPheny [In the second category, we found a number of tools dealing with the automated or semi-automated generation of both genes and Boolean operators of GPRs by generally using individual or a limited set of data sources. One of the most cited tool among them is SimPheny . SimphenSimPheny , Simphenmerlin) [Escherichia coli K-12 organism suggested in the tool documentation. Nevertheless, identification of the corresponding GPR associations relies exclusively on the KEGG BRITE database, from which information on the structure of the protein complex modules are retrieved. These rules are defined for sets of genes having similar roles, and conserved across several species using the KEGG ORTHOLOGY database. Although the valuable information available in KEGG database, its exclusive usage without including proteins structure and interactions coming from other already mentioned data sources could limit the outcomes.An attempt to fully automatically reconstruct GPRs is also represented by the open source software Metabolic Models Reconstruction Using Genome-Scale Information (merlin) . Its graAlong similar lines, a number of other pipelines fall into this category, including Path2Models , ModelSEGPRuler to efficiently automate the reconstruction process of GPR rules within metabolic networks of given organisms. In particular, we want to guarantee a white box among the currently available tools in order to introduce an open-source, clear and reproducible pipeline that can be applicable to any organism independently of its genome size, where the manual intervention is minimized in favour of an automatic information retrieval and managing. To achieve our aim, we rely on an extra available data source not yet exploited by state of the art, which is the Complex Portal [In view of the above, we propose a new methodology called x Portal  databaseGPRuler was implemented in Python programming language (version 3.7). The overall GPRuler pipeline can be executed starting from two alternative inputs. In the first case, an already available draft SBML model or a simple list of reactions lacking the corresponding GPR rules can be provided. In the second case, GPRuler takes as exclusive input the name of the organism of interest. In both cases, the inputs are firstly processed to obtain the list of metabolic genes associated with each metabolic reaction in the target organism/model. This intermediate output is then used as input for the core pipeline, which returns as ultimate output the GPR rule of each metabolic reaction.GPRuler tool tutorials are available in the qLS GitHub repository at https://github.com/qLSLab/GPRuler.The The overall proposed pipeline and its sequential execution steps are schematized in the following and in GPRuler can handle the scenario where a metabolic model has already been reconstructed in terms of included biochemical reactions, but information on the associated genes and, of course, on GPR rules is not available yet.GPRuler queries five biological databases, namely MetaCyc, KEGG, Rhea [In order to reconstruct this information, GG, Rhea , the CheGG, Rhea , the TraGG, Rhea . FirstlyGPRuler identifies in primis the metabolites involved in the reaction (Metabolites identification green box), by using the ChEBI database. ChEBI is an open source dictionary about any distinguishable molecular entity naturally or synthetically produced involved in living organisms processes. Additionally to all the provided information about name, identifier, synonyms and direct links to the entries in KEGG and MetaCyc databases, we exploited two other pieces of information obtained from ChEBI: the IUPAC International Chemical Identifier (InChI) and the ChEBI ontology. The InChi is an alphanumeric string providing a unique digital signature to any compound based on its structural representation. The ChEBI ontology is a tree-like description of structurally related compounds, which are included in increasingly more general categories.To obtain the set of genes involved in the catalysis of a given input reaction, cross-links identifiers to the above mentioned databases must be retrieved. To this aim, Metabolites are also identified in our pipeline through the MetaCyc database, which is a curated database of experimentally derived metabolic pathways involved in both primary and secondary metabolism, and of their associated metabolites, reactions, and genes. The PythonCyc package provided a Python interface to Pathway Tools, which is bioinformatics software accessing to all the information available in MetaCyc database.Finally, KEGG COMPOUND and KEGG GLYCAN databases are queried to retrieve data about all the compounds annotated in this database, including links to external database, such as ChEBI. KEGG is a biological resource consisting of multiple cross-linked databases that collect information relative to genes, enzymes, compounds, reactions and pathways.Once the stored compounds data in ChEBI, MetaCyc and KEGG are extracted, the FuzzyWuzzy Python package is used for string matching between the target metabolite and a list of strings. Leveraging the Levenshtein distance to compute the similarity score between strings, the FuzzyWuzzy package extracts the best matches to the target string. After manual inspection of the string matching performances, we set an acceptance threshold for the metabolite name of 91, which was able to exclude clearly wrong matches.GPRuler determines the reaction identifiers (Reactions identification green box), as annotated in KEGG REACTION, MetaCyc and Rhea databases. Rhea is an expert-curated biochemical reactions resource using the chemical dictionary from ChEBI to describe reaction players. Rhea is currently linked to several resources, including UniProtKB, KEGG and MetaCyc.Once the metabolites involved in each reaction of the input list have been assigned a proper identifier, In view of the fact that the three considered reaction databases are not completely independent one from another, as first action before querying them, our pipeline joins them in what we refer to as macro database. This macro database includes the union of the biochemical reactions present in each of the examined databases. In case the same reaction is found in multiple resources (with the very same metabolites and stoichiometry), it is represented as a unique object encompassing all the available annotated information about the reaction. Each reaction entry will include the corresponding set of reactants and products, cross-links to external resources and the list of corresponding Enzyme Commission (EC) numbers and catalyzing genes, from each of the consulted databases.Genes identification green box). Target reactions can be splitted in two kinds: internal, i.e., taking place within a given compartment inside the model, and transport, i.e., moving metabolites across compartments. For internal reactions, to retrieve the identifier it is sufficient to search for a reaction in the macro database that shares the same reactants and products with the same stoichiometry.The macro database is then queried to infer for each input reaction the corresponding identifier and hence the corresponding annotated list of genes  system, which is an approved classification system for membrane transport proteins analogous to EC system used for the enzymes, except that it also includes functional and phylogenetic information. Exploiting the mapping provided by TCDB between the TC systems and the substrates annotated to be transported, TC codes for the target transport reaction are identified and the corresponding list of genes is isolated.Genes filter green box). To this aim, annotations about gene products localizations are inferred from the Uniprot database and the Gene Ontology (GO) knowledgebase [GPRuler gives priority to subcellular locations deriving from manual annotation over those deriving from automatic predictions, following the principle that the human (user) curated knowledge is more accurate than automatic one.Finally, we wanted to filter the identified list of genes associated with each reaction to retain only entries for which subcellular location is in accordance with the one reported in the input reaction list or SBML model . In particular, the KEGG GENOME database is firstly queried to retrieve the complete reference gene sequences of the target organism. In order to isolate only metabolic actors, the KEGG BRITE functional hierarchy is used to filter out all the non-metabolic genes, whereas the KEGG REACTION database is queried to extract from the remaining ones the corresponding list of biochemical reactions involving them (Reactions identification blue box). Programmatical access to all data stored in KEGG database is performed thanks to the Bioservices Python package [Genes filter blue box).The unambiguous identification of the target organism is then exploited to look for the corresponding list of genes involved in the catalysis of metabolic reactions ( package . To be aGPR rules reconstruction orange box), GPRuler relies on the information included in the Uniprot database  and in particular in the “Interaction” section. Uniprot is an open-source database that provides a collection of manually and computationally determined functional annotations about proteins. Among the available data, the “Interaction” section organises in multiple subsections information about the quaternary structure of proteins and the set of binary protein-protein interactions established with other proteins or protein complexes. In particular, interchangeable participants and/or different chains or subunits belonging to the same complex are referenced through their gene names within a textual description.To characterize the relationships among the metabolic genes isolated in the previous steps of the pipeline and reconstruct the GPR rules  by means of Python requests and json libraries in order to extract all the components constituting the investigated complexes.Among them, Complex Portal is a manually curated database of macromolecular complexes relative to 21 key organisms representative of different taxonomic groups, including Another cross-referenced database to Uniprot about protein-protein interactions is STRING database. STRING is a database with a high coverage of both annotated proteins and organisms. It deals with physical and functional associations of specific target proteins resulting from integration of publicly available protein–protein interaction data sources, including inference of functional associations between genes from genomic-context predictions, genome-scale laboratory experiments, gene co-expressions, co-citation analysis of scientific texts, manually curated interactions from other biological databases, knowledge transfer between organisms. The available knowledge is further complemented with computational predictions.The programmatic access to STRING database  is made possible by the STRING application programming interface (API). Among the implemented functionalities, “retrieving the interaction network” and “performing functional enrichment” are used to extract other protein-coding genes constituting a complex with the queried protein. In particular, the “retrieving the interaction network” method allows one to retrieve the interaction network established by the target protein. Once this network is obtained, the “performing functional enrichment” method allows to perform functional enrichment analysis for each one of the selected proteins by mapping them onto several databases, including Gene Ontology, KEGG pathways, UniProt Keywords, PubMed publications, Pfam domains, InterPro domains and SMART domains. Thanks to this method it is possible to select, from the proteins included in the extracted interaction network, those belonging to the same protein complexes of the input queried protein.Another type of relationship among genes involved in a given reaction that must be reconstructed is the possible existence of protein isoforms. In this regard, KEGG Orthology database , which belongs to the collection of databases included in KEGG database, is a database of functional orthologs, which have been defined based on their molecular functions by exploiting experimentally characterized genes or proteins with similar sequences in other organisms. From the KEGG gene identifier under investigation, it is possible to trace back to the associated KEGG Orthology identifier that returns for the specific organism under investigation the set of genes having this identifier, which are grouped together because of molecular functions similarity. Similarly to Uniprot, Bioservices package is used to programmatically access KEGG ORTHOLOGY database and automatically extract all the necessary information. In case that genes identifiers are not expressed as KEGG identifiers, a converter from external databases to KEGG database is provided by exploiting specific functionalities of Bioservices.GPRuler, all the data retrieved in the previous four steps is used to list, for each queried gene, the genes coding for subunits of the same protein complex to which the target gene belongs to or for alternative protein isoforms. Once this information is available, GPRuler organizes it to generate the final GPR rule. In this regard, all the possible pairs of genes associated to a given reaction are linked with either an AND or OR operator according to the type of relationship between them that has been previously inferred by GPRuler. In particular, the AND operator is assigned if the two genes encode for different subunits of the responsible enzyme, whereas the OR operator is assigned if they encode for alternative protein isoforms of the associated enzyme. In the simplest scenarios all the involved genes encode for isoforms of the same enzyme or they all encode for distinct subunits of the same enzyme. In a more heterogeneous scenario, in which both enzyme subunits and isoforms are present, GPRuler firstly joins genes related by an AND operator and enclose them in separated parentheses and then all the information about genes encoding for protein isoforms is included.In the last step of GPRuler is to determine the GPR rule associated with each reaction. It follows that determining the right relationships among the involved genes takes priority in validating the tool. To assess the degree of confidence of the reconstructed GPR rules and hence the accuracy of GPRuler, we exploited curated metabolic models as ground truth.The core ability of GPRuler is required because the final GPR rule will simply correspond to an empty string or to the unique responsible gene.According to the type of relationships established among the genes involved in a given reaction, GPR rules can be categorized into five classes. In the simplest case, reactions are associated with empty rules (here labelled as “No gene” rules) when no gene is involved in reaction catalysis, or to single gene rules (here labelled as “One gene” rules) when a unique gene is responsible for reaction catalysis. In these cases, minimum effort from OR operators among genes (here labelled as “OR” rules), by AND operators (here labelled as “AND” rules), or by both AND and OR operators in the most intricate scenarios (here labelled as “Mixed” rules).An active role of our approach is played over more complex situations when multiple genes are involved in reaction catalysis generating the here labelled as “Multi gene” rules, where the right relationships among the involved genes need to be determined. In particular, “Multi gene” GPR rules can be characterized exclusively by GPRuler by using a workstation equipped with Intel Xeon 2.60GHz cpus.We executed GPRuler, we evaluated their accuracy by comparison with ground truth GPRs. We considered as ground truth the GPRs of four extensively curated models relative to well known organisms, namely HMRcore [GPRuler coincides with the corresponding ground truth rule, we compared their truth tables. The truth table consists of one column for each of the N Boolean input variables, for 2N rows, one for each of their possible combination. The final column stores the evaluated value of the GPR for each row. We considered each pair of GPRs as identical only if their truth tables are identical. We labelled these identical cases as “Perfect match”. It is worth noting that comparison of rules exceeding 20 genes was performed manually because of the computational cost.Following the generation of the rules by  HMRcore , Recon3D HMRcore , Yeast 7 HMRcore  and Yeas HMRcore . To evalIn the opposite case, when at least one row of the table differs (“negative match”), the discrepancy can be imputed either to differences in the set of genes involved in the two GPRs under comparison and/or to different Boolean operators connecting them. The first scenario can be excluded by means of the Jaccard index. The Jaccard index is defined as the cardinality of the intersection set divided by the cardinality of the union sets, and quantifies the gene coverage of each reconstructed GPR rule as compared to the ground truth. The Jaccard index ranges between 0, if any overlap is observed, and 1, when the two sets coincide. For reactions having Jaccard index of 1, we used normalized Hamming distance between the two truth matrices to quantify the mismatch extent of their GPRs in terms of the included operators. The normalized Hamming distance is the number of different positions in the two vectors over the length of the two vectors. It ranges between 0, if the two truth tables are opposite, and 1, if the two truth tables are identical.In GPRuler outputs are more or less in line with the underlying biology, as compared to the GPRs used as ground truth. In detail, we adopted information coming from GeneCards [GPRuler, we labelled the GPR as “Not automatically reconstructed by GPRuler”. On the contrary, if some of the genes in the ground truth rule are wrongly associated to the reaction or improper Boolean operators are used between genes, whereas GPRuler rule is in line with the underlying biology, we labelled the GPR as “Corrected by GPRuler”. Finally, we considered as automatically reconstructed by GPRuler (“Automatic” labelled) both GPRs that were labeled as “Perfect match” and as “Corrected by GPRuler”. On the contrary, those rules that cannot be correctly reconstructed by GPRuler unless a subsequent curation by the user are referred to as “Not automatic”. The proportion of Automatic and Not automatic rules is shown in For all negative matches, we manually checked the annotations of the involved gene products available in the Uniprot database and in organism specific databases to determine whether the differences observed in the eneCards  and HGNCeneCards  databaseeneCards  for YeasGPRuler in reconstructing the GPR rules of HMRcore model, which is a core model of central carbon metabolism that we extracted from the genome-wide HMR metabolic model [We firstly assessed the performance of ic model  and we sic model , 44–46. The HMRcore model consists of 324 reactions that are mainly associated to single gene rules representing the 43.8%. The remaining rules are 23.8% classified as “No gene” and 32.4% as “Multi gene”.GPRuler on the reactions included in HMRcore and the subsequent comparison with those stored in the original model produced a perfect match for 158 of the 324 examined reactions, corresponding to a total of 48.8%. Considering the category of each rule, we correctly inferred 77.9% of “No gene” rules , 47.2% of the “One gene” rules , 15.4% of “AND” rules , 37.7% of the “OR” rules  , and none of the 15 “Mixed” rules.The application of GPRuler, depicted in Looking at the 166 wrongly obtained GPR rules , 119 false negatives rules turned out to be replaceable with those generated through our methodology because in line with the information stored in the exploited databases. Following manual curation of the ground truth model, the percentage of correctly predicted GPR rules of HMRcore increased from 48.8% to 85.5 , 13.3% of the “One gene” rules , 1.6% of “AND” rules , 8.9% of the “OR” rules , and 1.2% of the “Mixed” rules .The application of GPRuler because in line with biological knowledge. After correcting the ground truth model, the percentage of correctly predicted GPR rules of Recon3D increased from 32.3% to 69.5% (as shown in GPRuler depicted in Looking at the remaining 9165 reactions (67.7%), 5039 rules turned out to be false mismatches and therefore replaceable in Recon3D model with those generated by 2OXOADOXm” and “AKGDm”. GPR rules of these reactions did not result in completely accordance with the corresponding rule reconstructed through GPRuler due to the inclusion of the PDHX gene. A manual investigation of Human Cyc database [GLXO1” reactions includes all genes corresponding to a different enzyme compared to the involved one. Consequently, inclusion of these genes in the current rules should be reviewed. A second and consistent set of reactions was found to have contradictory GPR rules due to AND and OR operators simultaneously joining same genes. On behalf of this scenario, the reactions of Recon3D model labelled as “AHEXASE2ly”, “AHEXASEly”, “NDPK10”, “NDPK1” and “PFK” fall in this category. The most likely explanation of this behavior is the non proper automatic generation of GPR rules. Alongside with this hypothesis, a biological explanation of contradictory genes relationships might emerge in relation to alternative forms of the same enzyme. In these situations, the usage of parentheses within the logical expression becomes fundamental to discriminate automatic from biological annotations.A consideration about the examined rules in Recon3D model concerns some recurrent imprecisions that should be revised in the original model independently of their comparison with rules generated from our approach. A first representative case regards the two reactions labelled in Recon3D model as “database  highlighGUACYC”, “CREATt4_2_r”, “GLYKm”, “GUACYC”, “HPCLxm”, “NOS1”, “NOS2”, “r0400”, “r0924”, “MTHFC”, “PSP_L”, “TPI”, “r0345”, “GLYK”, “FTHFL”, “MTHFD2”, “HMR_2041”, and “HMR_4790”. In this case, comparison of the original GPR rule with GPRuler indicated the need for a revision of the included gene identifiers. In all these reactions, genes that no longer exist due to dismissal of their identifiers are included, preventing the correct determination of the rule under consideration.A last scenario emerged from the analysis of Recon3D model regards the reactions labelled as “GPRuler on Recon3D model was 231 hours and 33 minutes. The complete list of GPR rules generated by GPRuler for Recon3D model is available in the The execution time of GPRuler, we considered as ground truth two other genome-scale models, namely Yeast 7 and Yeast 8, which are the two latest releases of S. cerevisiae genome-scale metabolism. The application of the pipeline to these two models returned comparable results.To further evaluate the performance of GPRuler applied to the reactions included in Yeast 7 proved to be able to correctly reconstruct 1600 GPR rules, representing the 45.8% of the rules in the model. Considering the category of each rule, we correctly inferred 69.6% of “No gene” rules , 40.6% of the “One gene” rules , 7.1% of “AND” rules , 17.1% of the “OR” rules , and none of the “Mixed” rules.Yeast 7 consists of 3493 reactions whose GPR rules resulted mostly categorized as “No gene” (34.1%) and “One gene” (48.7%). The remaining 17.2% consists of “Multi gene” rules. GPRuler increased up to 70.5% , 39.7% of the “One gene” rules , 5.5% of “AND” rules , 17.5% of the “OR” rules , and none of the “Mixed” rules.The updated model version of Yeast 7, namely Yeast 8, is a little bit larger than its predecessor since it consists of 3991 reactions. GPR rules classification showed that “No gene” and “One gene” rules cover, respectively, 34.5% and 47.4% of all the metabolic reactions included into the model, and “Mixed” rules cover the 18.1%. The execution of GPRuler to correctly reconstruct the 71.4% of the model rules Click here for additional data file.S2 FileRxn the reaction for which the GPR rule has been generated, in the column rule_original the corresponding original rule in the model, in the column rule_GPRuler the rule reconstructed by GPRuler and in the column Evaluation the the label assigned after comparison between the rule in the rule_original and in the rule_GPRuler column .Each row shows in the column (CSV)Click here for additional data file.S3 FileRxn the reaction for which the GPR rule has been generated, in the column rule_original the corresponding original rule in the model, in the column rule_GPRuler the rule reconstructed by GPRuler and in the column Evaluation the the label assigned after comparison between the rule in the rule_original and in the rule_GPRuler column .Each row shows in the column (CSV)Click here for additional data file.S4 FileRxn the reaction for which the GPR rule has been generated, in the column rule_original the corresponding original rule in the model, in the column rule_GPRuler the rule reconstructed by GPRuler and in the column Evaluation the the label assigned after comparison between the rule in the rule_original and in the rule_GPRuler column .Each row shows in the column (CSV)Click here for additional data file.S5 FileRxn the reaction for which the GPR rule has been generated, in the column rule_original the corresponding original rule in the model, in the column rule_GPRuler the rule reconstructed by GPRuler and in the column Evaluation the the label assigned after comparison between the rule in the rule_original and in the rule_GPRuler column .Each row shows in the column (CSV)Click here for additional data file.S6 File(MD)Click here for additional data file.


Industrial biotechnology represents one of the most innovating and labour-productive industries with an estimated stable economic growth, thus giving space for improvement of the existing and setting up new value chains. In addition, biotechnology has clear environmental advantages over the chemical industry. Still, biotechnology’s environmental contribution is sometimes valued with controversy and societal aspects are frequently ignored. Environmental, economic and societal sustainability of various bioprocesses becomes increasingly important due to the growing understanding about complex and interlinked consequences of different human activities. Neglecting the sustainability issues in the development process of novel solutions may lead to sub-optimal biotechnological production, causing adverse environmental and societal problems proportional to the production volumes.ab initio improvement of the biotechnological production design.In the paper, sustainable metabolic engineering (SME) concept is proposed to assess and optimize the sustainability of biotechnological production that can be derived from the features of metabolism of the exploited organism. The SME concept is optimization of metabolism where economic, environmental and societal sustainability parameters of all incoming and outgoing fluxes and produced biomass of the applied organisms are considered. The extension of characterising features of strains designed by metabolic engineering methods with sustainability estimation enables  Biotechnology has several clear environmental advantages over the chemical industry as (1) the use of renewable bioresources instead of non-renewable petroleum-based resources; (2) production of biodegradable products instead of nondegradable and/or non-recyclable products; (3) avoidance of toxic industrial waste and gases, etc. Still, biotechnology’s environmental contribution is sometimes valued with controversy. Biotechnology uses biomass as the fermentation feedstock presuming its renewability and carbon-neutrality. Yet, resource inputs required to obtain and process the biomass feedstock, especially in its pure form , for its use in the biotechnological production process should be considered to assess the total environmental load. An additional point of discussion is the competing use of land for growing biomass for fuel or chemical feedstock production versus growing foodstuffs and maintaining biodiverse forests that serve as the global carbon sink.The growing biotechnology represents one of the most innovating and labour-productive industries of the bioeconomy sector by value added. Bioeconomy has gained traction as a driver towards smart and green growth. More than 40 nations are currently investing and promoting strategies and policies in the bioeconomy sector. In 2017, the European Union’s (EU) bioeconomy sector employed more than 417 thousand people in the manufacture of bio-based chemicals, pharmaceuticals, plastics, rubber, and liquid biofuels, which generated 63,528 million euro of value-added  The prominent presence of the UN Sustainable Development Goals The sustainability of different bioprocesses becomes increasingly important parameter due to the growing understanding about consequences of different human activities Mathematical modelling and assessment are the only option to assess the impact of current technological limitations on the potential of improvements  without having high number of ‘wet’ experiments. Modelling can describe the whole solution space constrained by the current technological alternatives and search optimum within the huge space of alternatives with different optimisation methods and algorithms. Modelling can be expensive, but it may turn out relatively cheap comparing to a potential failure of investments. We acknowledge that there are many processes where modelling cannot help much, for instance, due to our limited understanding about the functionality of organisms under different conditions. Still, even when models can not describe all details, it is wise to assess the optimisation potential early at the bioprocess design workflow to use modelling for the elements and aspects where methods and tools are available. The known reliable constrains  may provide enough information to find the optimum assuming optimistically that unknown details do not reduce the attractivity of solution to an uninteresting level. If the changes are not attractive even with the optimistic assumptions, the question is answered – the proposed solution is not worth implementing.analysis and synthesis – just as in all other fields of engineering The metabolism of an organism exploited in a biotechnological process determines many features of the. Therefore, metabolism at cellular level is a popular optimisation target addressed by metabolic engineering – purposeful modification of metabolic networks of biochemical reactions The aim of the paper is to discuss the application, options and limitations of mathematical modelling of metabolism in facilitating the sustainability of industrial biotechnology by introducing the sustainable metabolic engineering concept.2A typical sequence of activities setting up a biotechnological production is the selection of economically beneficial product and/or substrate pair or set. Then appropriate organism or modified strains for the production are selected. It may as well work in the opposite direction when appropriate target products are sought for an organism with specific features to exploit them. Nonetheless, once the organism and product or substrate are selected, many features of the biotechnological production become determined and the rest of potentially influencing parameters have to be accepted as given An alternative is to invest more in the modelling at the design phase B in  expandinAn intensive modelling implementation at the research and development phase B in  demands Looking at the exchange between the system’s scales , parametAt the bioreactor and factory/industry scale the techno-economic analysis (TEA) 3Metabolic engineering deals with the complexity of interlinked pathways up to genome scale and has the potential to significantly increase the amount, plurality and variety of products obtained from renewable feedstocks ab initio – during the selection of the organism and its optional modification (engineering) as the consequences of organism selection dictate most of other interactions  sustainability assessment and inclusion of its diverse aspects that in detail have not been considered before.Among different modelling approaches, constraint based stoichiometric modelling e fluxes A in the Sustainable Metabolic Engineering (SME) concept – optimization of metabolism where economic, environmental and societal sustainability parameters of all incoming and outgoing fluxes and produced biomass of the applied organisms are considered.We propose the COBRA v3.0RavenMerlinGSMM optimization by constraint-based stoichiometric modelling, applied in metabolic engineering context, currently seems to be the most appropriate way to gain an overview about the expected exchange of metabolites between the organism and the environment. To implement the SME concept, the objective function of the optimisation process must include more than typical biomass, product flux and/or yield maximisation. The value of the objective function should be calculated by multiplying the vector of metabolite exchange fluxes obtained from genome-scale stoichiometric metabolic models by the corresponding vector of sustainability parameters that characterize the metabolite exchange fluxes B. The SMEcoinvent or GaBi and others ab initio sustainability assessment are available and must be applied in a coordinated manner with the metabolic exchange obtained by metabolic modelling.According to the SME concept, optimisation and ranking of metabolic engineering designs is executed by including economic, environmental, and societal parameters in the objective function determined by the balance of metabolic flux exchange. The economic parameters that can potentially be included in the sustainability calculation of the metabolic flux set are, for example, media compound costs, market price of product components, and waste management costs (or negative price of the produced components) It is important that the objective function that is the exchange flux vector multiplied by the sustainability vector associated with economic, environmental and social criteria B, enableThe implementation of SME concept enables detailed analysis of the spectrum of organisms, media compounds and produced components due to their impact on the objective function as (1) the most appropriate organism can be selected as chassis, (2) the spectrum of product components might be metabolically engineered to avoid metabolites characterized by negative sustainability impact, (3) media compounds might be replaced by more sustainable compounds. As a result, metabolic engineering can be applied to handle even higher complexity of optimisation aroused by the sustainability goal included in the objective function.The optimisation results (organism designs) can be ranked according to the sustainability indicator score where each design is characterised by the selected chassis organism, number of reaction deletions and insertions and weighted sum of economic, environmental and social score compounds for a particular substrate/product set.An important issue in the implementation of the SME concept is the flux variability All metabolic exchange fluxes mentioned in the definition do not necessarily mean that sustainability parameter values must be found for each metabolite. The impact of small fluxes below some threshold may be neglected or lumped, yet that becomes possible only after their values are calculated as a result of genome-scale metabolic modelling. On the other hand, some metabolites may have high impact on the sustainability even when the flux is relatively small. The sustainability indicator vector elements can contain also different functions to take into account non-linearity. Meanwhile, it may be complicated to consider the non-linear impacts between various sustainability goals see . Social The constraint based stoichiometric modelling at genome-scale can be combined with other methods  to improve the accuracy of sustainability assessment and the biotechnological feasibility of the engineered strain. The kinetic modelling can be used to approach the main production pathways with higher accuracy considering also the impact of metabolite concentration on the throughput of the pathway 4The sustainability criterion of industrial biotechnology should not be limited to the use of renewable resources or production of bio-based products. Sustainability of industrial biotechnology must also incorporate the zero-waste and zero-emission concepts, biosafety, biosecurity and social acceptance of the process and products along with overall economic benefits. It is technically feasible to assess the optimality of a biotechnological production in all aspects of sustainability – economic, environmental, and societal. The set of all fluxes consumed and produced by an organism provides enough information for the sustainability assessment. A full balance of fluxes entering and leaving the organism can be calculated and optimised by the well-developed constraint based stoichiometric genome-scale metabolic modelling and optimisation. The extension of metabolic engineering with sustainability optimisation functionality (sustainable metabolic engineering) provides a whole new dimension in the design of the biotechnological production.Egils Stalidzans: Conceptualization, Methodology, Visualization, Writing – original draft, Writing - review & editing. Elina Dace: Conceptualization, Methodology, Visualization, Writing – original draft, Writing - review & editing, Project administration.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Stapylococcus aureus colonises the nose of healthy individuals but can also cause a wide range of infections. Amino acid (AA) synthesis and their availability is crucial to adapt to conditions encountered in vivo. Most S. aureus genomes comprise all genes required for AA biosynthesis. Nevertheless, different strains require specific sets of AAs for growth. In this study we show that regulation inactivates pathways under certain conditions which result in these observed auxotrophies. We analyzed in vitro and modeled in silico in a Boolean semiquantitative model  the regulatory impact of stringent response (SR) on AA requirement in S. aureus HG001 (wild-type) and in mutant strains lacking the metabolic regulators RSH, CodY and CcpA, respectively. Growth in medium lacking single AAs was analyzed. Results correlated qualitatively to the in silico predictions of the final model in 92% and quantitatively in 81%. Remaining gaps in our knowledge are evaluated and discussed. This in silico model is made fully available and explains how integration of different inputs is achieved in SR and AA metabolism of S. aureus. The in vitro data and in silico modeling stress the role of SR and central regulators such as CodY for AA metabolisms in S. aureus. Staphylococcus aureus is often part of the natural flora without causing infections 1. Yet it is also one of the most important pathogens causing a wide range of diseases in almost any region or organ of the body ranging from skin and wound infection to endocarditis or osteomyelitis2. The high virulence potential of S. aureus is also due to its genomic variability and capability to adapt to different environmental conditions4.S. aureus requires many amino acids (AAs) for growth despite the presence of all essential biosynthesis gene clusters indicates that auxotrophy might in large extent be due to regulatory circuits. At least three global regulatory systems, the stringent response (SR) and the transcription factors CodY and CcpA, contribute to the expression of amino acid metabolic genes. A comprehensive understanding of the effects of these regulators on AA synthesis requires the integration of all sensed signals and downstream effects.The observation that 5. S. aureus also possesses two small (p)ppGpp synthetases, RelP and RelQ. However, they are non-responsive towards AA limitation but likely contribute to a basal level of (p)ppGpp under non-stressed conditions6. In Firmicutes, (p)ppGpp synthesis is accompanied by a severe drop of the intracellular GTP level which leads to downregulation of processes like ribosome biogenesis and translation and causes de-repression of CodY target genes7. The global regulator CodY controls the expression of a large number of metabolism and virulence genes, including many genes involved in AA biosynthesis and transport, in response to the availability of GTP and the branched-chain amino acids isoleucine, leucine, and valine9. CcpA, the master regulator of carbon catabolite repression, is another known modulator of AA metabolism in S. aureus. Under conditions of glucose availability, CcpA activates key enzymes of the glucose metabolism which provides necessary intermediates for AA biosynthesis. Moreover, CcpA directly regulates genes involved in arginine and proline biosynthesis11 and in amino acid catabolism, thereby linking utilization of alternative carbon sources to glucose availability13.AA limitation leads to activation of the (p)ppGpp synthetase domain of the RSH enzyme (RelA/SpoT homolog)S. aureus HG001 parental strain and isogenic mutants defective in RSH, codY and ccpA, respectively. Our approach allows to (i) gain a better understanding of the regulatory processes, (ii) develop a useful in silico tool for hypothesis testing and (iii) evaluate the essential AA in different S. aureus strains. Moreover, this may be used for development of a new anti-staphylococcal treatment strategy.Hence the goal of this work is to model the regulation of AA metabolism, including major regulatory components , in a Boolean network and to validate the model based on the growth behavior of the ccpA and (p)ppGpp0-21 (the (p)ppGpp0 codY mutant) were obtained by transduction of the ccpA14 or codY15 mutation into strain HG001 and (p)ppGpp0, respectively. Rshsyn is a derivative of strain HG001 in which the synthetase domain within rsh was mutated16. Bacteria were grown overnight in chemically defined medium15 supplemented with the appropriate antibiotics . Strains were inoculated to an OD600 of 0.05 in fresh CDM (without antibiotics) and grown to OD600 of 0.5. Bacteria were pelleted and washed twice with 2 ml PBS and used to inoculate prewarmed CDM lacking single AAs in a 96 well microtiter plate to an initial OD600 of 0.05. Growth was monitored using Tecan infinite M200 Pro (Tecan Group Ltd) with data acquisition in 30 min intervals. Each strain was analyzed in triplicate.Bacterial strains are listed in Table 17 and the protein–protein interaction database STRING18 were used. Furthermore, data from microarray analysis of the stringent response (RSH-dependent)16 and other microbiological literature were considered. Judging from the genome it was determined for S. aureus that in principle all AA should be synthesizable15. Therefore, further information from GenBank and sequence annotation not only of NCTC 8325 but for unclear annotation also information from COL and Newman were considered to curate the network. Thus, all this information was used to verify the nodes and types of interactions, making sure the network comprises all the central genes, products, metabolic intermediates and enzymes as well as their interactions for stringent response and amino acid metabolism. The model represents the strain HG001 but the interactions of this model are valid and well conserved for all related strains of clade A according to19.For setup of a first topology network of amino acid metabolism, data available from biochemical databases such as KEGG20. For simulations and growth predictions, the network was constructed, visualized and made machine-readable using the software yEd-Graph Editor (v3.14.1).All these data sources together with expert curation by sequence and domain analysis were assembled to ultimately yield a reliable network confirmed by different data sources and analyzes that includes all biologically important functional aspects of all protein interactions. Although all major relevant interactions and proteins were considered no biophysical or biochemical details were included  to keep the model as simple as possible. Model connectivity was further refined by expert curation and bioinformatics analysis, for instance CodY and CcpA target genes were identified based on conserved binding motifs of metabolic genes21:S. aureus was implemented.Every known, literature-reported connection concerning the SR and the AA metabolism in Concerning a specific single interaction and differences in literature reports about it, the most frequently found connection type was selected when there was conflicting information. If, for example, a reaction was described several times as existing and only once as undetectable, in the synopsis the decision for the fact most frequently mentioned in the literature was made. In addition, attention was paid to the quality of the respective study on which the statement was based, so whether, for example, a connection was indirectly refuted in a study or whether it was proven in detail with all individual steps and reactions.S. aureus. If there was at least one report available stressing its importance, we chose to implement the node at the rim and suitable interaction edges to make sure not to miss any biologically relevant component.To make the simulation treatable and affordable, the model has always to be a strong simplification of the complete cellular interaction network. It has hence to focus on the network of interest, in this case the amino acid metabolism and its regulation. However, care has to be exerted not to miss important input at the rims of the subnetwork. Hence, concerning these margins or the boundaries of the model, uncertainty may arise about the importance of the node or interaction for the SR and AA metabolism of As a general rule, a biological process which cannot easily be simplified and adapted to the activating inhibiting network model we clipped strongly down to the central interactions which best reproduced the biological output correctly. We hence did not implement all available connections. Table For setting up the network in the yEd-Graph Editor we adhere to the following four already established rulesIn the AA metabolism frequently occurring AND conditions i.e. the requirement of substrates and enzymes to generate the products are implemented in the simulation by means of custom-made, developed and tested "AND-Logic-gates" Fig. . These l22 a program calculating the activation or inhibition of nodes following the implemented exponential function kinetics23. With this our network including all the known nodes important for the AA metabolism and SR  as well as the known interactions  between them was simulated  and thus the activating or inhibiting input of the totality of afferents is determined. These e-functions approximate the kinetic parameters in a simplified manner allowing simulations that show dynamically how far a node is partially or fully activated or inhibited over time by the combination of different activating and inhibiting inputs and thus closely mirror the logical succession of events within the network.For these semi-quantitative simulations, the network is transformed within Jimena. The simulation calculates the activation of a single node by considering the strength of the afferent connections by taking into account the activation level of the source node by interpolating between full on and off states of the network nodes using exponential functions22. This includes models of pharmacological24 and immune responses25. Moreover, we give time-units at the x-axis in our simulation outputs and results figures of the model. However, this is just for visualization, the solutions found by Jimena do not operate this way, the software calculates continuous solutions for the complete system of differential equations according to the Boolean topology modeled22.The overall fit of the simulated activation curves modeled by the exponential functions to biological response curves is well. This works in spite of the limitation that e-functions are used, as the qualitative modeling software Jimena solves automatically the whole set of non-linear ordinary differential equations mirroring the Boolean network of the cellular signaling process. As hence all constraints have to be fulfilled at the same time, the difference between observed response curve and modeled response curve stays smallS. aureus as well as the influence and mechanisms of SR.The simulation provides hence a suitable dynamical model of the interactions and regulatory mechanisms in the AA synthesis in S. aureus wild-type strain HG001, the model is easily adapted to conditions in other strains editing the network model  according to the strain-specific annotation from public databases including AureoWiki26.All data and the model are made available for further experimentation and investigations on SR as well as extension of the model. Though using data from All simulations were done on a computer with Windows Home 10 (64 Bit) and Intel core I7-4700MQ CPU at 2.40 GHz and 8 GB RAM.For the in silico reproduction of the in vitro experiments different time range pulses were programmed in Jimena. The growth medium was simulated by constant pulses in Jimena by activating AA present in the medium with 0.7, withdrawn AA were set to 0.0. For simulating the different knockout (KO) mutants the respective node was set to 0.0. For each condition, exactly one simulation is given. The simulation reproducibly always yields the same result. For the growth experiments, on the other hand, three measurements were conducted for each condition .The agreement of the in vitro experiments to alternative networks with iterative minor changes was determined for comparison and only better fitting models were accepted. In an iterative process we first incorporated further information available from literature, biochemical databases and including phylogenetic information on conserved modulatory interactions from related species and validated these networks according to our experimental data . More importantly, connections giving wrong modulatory input not leading to the observed responses were changed or removed and some initially missed connections were added. In this way, we achieved a refined network  or whether just the growth tendency of mutant strains compared to the wild type was correctly predicted by the model .We distinguish in the following a comparison regarding whether the order relation (strength of growth) was properly predicted  which integrates over the growth and thus reflects AA synthesis performance most reliably.Comparisons were done using categorized values. Values were categorized .The number of categories cannot be based on empirically verifiable numbers, but must nevertheless be within a semi-quantitative comparable framework since a quantitative comparison of the in silico and in vitro data is with the current lack of kinetic and metabolite data impossible. As the number of categories increases, the quantitative character of the comparison increases. Yet when the number of categories decreases, the comparison increasingly loses its informational content. That is why after testing various possibilities the results have been divided into just three categories: weak, medium and high synthesis. This intuitively obvious division has a reasonable number of categories for a semi-quantitative comparison allowing verification or falsification of a good categorial fit of the model or not (three state prediction model). Other categorization options and their semi-quantitative consistencies were compared  several AA were found essential for growth, namely Arginine, Cysteine, Glycine, Leucine, Proline and Valine. Out of these Arginine and Proline are essential in all tested strains. AA deprivation of Cysteine, Methionine and Phenylalanine resulted in reduced growth in all strains. Mutations in codY could enhance growth in medium lacking Glycine, Methionine, Phenylalanine, Serine, Threonine, Tyrosine, Leucine and Valine. The synthesis of Glycine, Histidine, Isoleucine, Methionine, Phenylalanine, Serine, Threonine and Tyrosine is promoted by Rsh and thus reduced in the RSH mutant. Only in medium lacking cysteine the rshSyn mutant showed a growth advantage compared to all other strains.In the RSH mutant are probably due to accompanied increase in the GTP pool and CodY de-repression of AA biosynthesis genes. Glycine and Phenylalanine are the two AA whose synthesis is repressed by CcpA as deduced from the growth of the ccpA mutant. Taken together, in good agreement with the previous literature, our growth experiments underline the role of CodY as one of the most important nodes and the central regulator in the AA metabolism and SR of S. aureus ppGpp, CcpA, the amino acid biosynthetic operons (e.g. ilv operon and many more). Table S. aureus.The amino acid synthesis network was constructed by implementing all nodes, i.e. enzymes, AA and intermediate products known from biochemical21 network connectivity was used as a measure of influence on the network and thus as a predictor to identify these nodes.Master regulators and thus the mutant strains used for these experiments were selected because of their proposed importance in the network. As in our previous studies28. CodY repression can be relieved via RSH mediated drop of the GTP pool and/or via Isoleucine limitation15  and the effectors of CodY activity, GTP, Isoleucine and ClpC (3 afferent connections). CodY amongst numerous other genes, inhibits major AA biosynthesis gene clusters such as those involved Threonine, Methionine or branched-chain amino acid (BCAA) biosynthesisn15 Fig. .Figure 429. CcpA regulates via gnd the utilisation of glucose for the pentose phosphate pathway29. Moreover CcpA downregulates for example the alanine production30 and controls the transformation of proline into arginine and vice versa10. CcpA, besides regulating AA synthesis directly, also improves exploitation of glucose, producing metabolites important for the AA metabolism. Furthermore CcpA regulates the BCAA synthesis via the ilvDBC-leuABC-ilvA operon31 ppGpp inhibits translation33 and rRNA synthesis34 thus reducing resource consumption in cases of AA deprivation. The CodY-mediated effect of the SR is abrogated in a pppGpp0/CodY strain as it is not able to synthesise pppGpp.The node with the third most interactions is (p)ppGpp. As for RSH enzymes of other bacteria, (p)ppGpp synthetase activity of RSH is likely induced by unloaded tRNA as consequence of AA limitationcodY, ccpA, rshSyn and pppGpp0/codY) were tested and their detailed behaviour was studied in silico in the simulation and also in in vitro in growth experiments.Thus three master regulatory nodes were identified and successfully implemented in the network. These knockouts and a combination . For a more realistic definitive network reflecting the real processes in A categorization of results was done first. Categorization always results in an artificial fragmentation that may offer the potential for an incorrect scoring if not done carefully , Moreover, regarding our application, regulation of amino acid metabolism, we hence did focus on a primarily qualitative comparison. Finally, all raw data required for further such categorizations are listed in Table After our categorization, there remained 19% in vitro—in silico discordances. These are further analyzed and investigated in Table Overmodulation (˄) means that in vitro and in silico qualitatively the same effect is observed, yet the simulation shows activation quantitatively stronger than the in vitro growth results. Undermodulation (˅) however means that despite the qualitative concordance the simulation anyhow shows activation quantitatively weaker than the in vitro growth results.In conclusion most misfits are found in the semi-quantitative comparison. But qualitatively in most of the cases a corresponding and matching effect can be observed when knocking out different network nodes.S. aureus.A very good qualitative in vitro to in silico correspondence of the AA synthesis strength in the WT can be found in most cases. Similarly the change in synthesis performance by switching off the tested nodes is in silico almost always predicted correctly Table , Fig. 1.Deprived of one single AA the strains show, qualitatively, seven different growth patterns suggesting seven patterns how the different central nodes influence the synthesis of these AA. In qualitative comparisons, these patterns can also be found correspondingly in silico Table , Fig. 1.35. The Boolean network models the cellular decision processes but as on and off, yes or no, decisions. This is a clear limitation, as cellular processes happen with a whole range of dynamics between full activation and complete shutdown of activities, receptors or kinases. Hence, in a second step, dynamic simulations using Jimena interpolated between the on and off states using exponential functions to simulate the succession of events but also the intermediate states of activation for all proteins in the network and its logical connections. As detailed kinetics are only estimated by the simulation, these are semi-quantitative simulations, reliable on the implied order relations  but allowing no accurate quantification. Hence, all our modeling is of a qualitative nature, which is clearly a limitation, but it works with the limited data which are currently available.Due to the limited data available, a detailed simulation of the mechanisms, including, for example, enzyme kinetic data, is not possible. A Boolean network and, building on this, its dynamic simulation, even if maybe invalid in modeling cellular processes quantitatively, is an excellent option for at least a qualitative depiction of the real conditions when quantitative data are lacking. The analysis allows to reveal possible connections and making them understandableS. aureus to adapt to a large variety of resources available in the vast variety of habitats in which it is able to grow36. The SR may also affect the biofilm building ability and virulence of S. aureus by activation of the quorum sensing system Agr, since Agr activity is modulated by CodY21. Yet, metabolism itself is important for virulence39. Thus SR and metabolism in general is an important factor making S. aureus and other bacteria so successful pathogens and causing so much medical problems. Due to the comprehensive representation of not only the SR but also the AA- and energy metabolism, the validated network presented here is a vantage point for further understanding and investigation of those complex processes.SR with its influence on the AA-metabolism allows S. aureus growth44. Arginine, Valine, Cysteine, Proline, Glycine and Leucine were frequently found to be essential which is well in accordance with our experiments and simulations  in which switching off the nodes in silico does not qualitatively predict the effect as it can be observed in vitro it is the effect of the quadruple mutant and thus the knocking out of four nodes  at the same time. The qualitative effects of turning off each node individually are simulated correctly. Thus, the mismatch in the synthesis strength of the quadruple mutant most likely results from systemic effects, e.g., the incorrect respective weighting of the correctly represented interactions of these nodes against each other. Yet in the fourth case isoleucine has a slightly reduced synthesis performance, when knocking out RSH which is in silico qualitatively not reproduced. In all these cases both inappropriate weighting of the connections in the pathway yet also a missing node could account for that. Further in detail investigation of these pathways is needed in future, using the simulation for hypothesis generation as well as fast and easy testing of different hypotheses.49 need to be considered, which could not be incorporated due to the limited network size. In the future new connections which will further improve the predictive power of the simulation must be expected to be found, possibly even through a targeted search based on this simulation. Yet despite the understandable aim for improving the predictive power of the simulation it always has to be considered, that it must remain a simplified simulation and representation of reality in order to do its job which is, for example, the development and preliminary testing of hypotheses. However, whether the simulation itself gets better is always a matter of whether the increasing complexity also leads to a relevant improvement in the predictive power. With a qualitative agreement of 92% using simple interactions without kinetic properties, this simulation has the optimal trade off in this regard. An additional insertion of kinetic data and thus an extreme increase in the complexity of the simulation has only a very low potential for improvement (8%). Thus, when weighing complexity and goodness of fit, a balance that can hardly be improved has been reached. As a consequence, the exponential functions of this method once again prove to sufficiently reflect reality, like already shown in numerous previous studies .For an even more precise simulation, firstly not yet available kinetic data is needed to weight the interactions. This applies in particular, if we would like to parametrize the e-functions we use in the simulation directly or even use other, more complex functions for the model. Second, it must be considered that the current state of knowledge regarding the interactions of the nodes is probably not yet comprehensive and third, inputs from outside like sensing environmental signalsThe same applies to the simulation margins, no simulation can reflect the entire reality but is naturally limited. Of course, there will always be inaccuracies at these margins. Enlargement of the network only shifts these inaccuracies at the price of increasing complexity. This simulation with its 195 nodes and 320 edges for the representation of such complex mechanisms hence offers a good tradeoff between accuracy and model reduction.S. aureus it also helps to better elucidate in combination with published data and our own experiments the function and interactions of the central regulatory nodes.The network focuses on key regulatory nodes with great impact on nutrient stress response. This includes nodes important for the SR but also for the AA—and the energy metabolism like CodY, RSH and Ccpa but also the Met- and the Ilv-Operon and many more. These in the literature stronger represented nodes are much better examined and discussed than rather peripheral nodes and therefore have a more precise representation in the network. The distance to these central nodes moreover increases the probability that edges and possibly also nodes have not been discovered and described so far and thus are missing in the network. However a lot of detail is provided by the model, for example when looking at all the peripheral interactions and nodes included. Another example is the in detail represented glycolysis pathway providing important intermediates for the AA biosynthesis. Nevertheless, this model still remains simplified. It obviously does not represent the exact interaction mechanisms on a molecular level yet just the effect one node has on another node (activating/inhibiting) is considered. As a result of this the model works without detailed kinetic data which are often not available and experimentally hard to obtain. However, this model not only provides a platform for simulation of SR and AA-Metabolism in S. aureus infection, and, by virtue of the high conservation of AA metabolism, against Gram positive bacteria in general.Our model supports novel antibiotic targeting strategies either against SR or the AA metabolism and the modeled further network components. This helps to fight resistance in 50 acting directly on the AA-Metabolism and the SR of S. aureus. Knowing the mechanisms behind the S. aureus stress response and being able to simulate it allows identifying all essential points in the network, depending on the exact strain and the environmental conditions and disrupting the connected AA pathway only in S. aureus. However, more efficient strategies considering SR and regulation in general are possible. When combining our recent model with our models on Quorum-Sensing (QS) and biofilm formation in S. aureus21 it is obvious that the nutrient stress in S. aureus has also a strong impact on QS and biofilm formation and thus on the virulence of S. aureus in general. Nutrient stress is shown to lead to de-repression of CodY target genes13 including Agr activity. Thus, there is a direct link between central metabolism and virulence gene expression and biofilm formation21. Thus nutrient stress reduces biofilm formation turning S. aureus towards the easier to treat planktonic state. Hence, a second new antibiotic strategy implied by our modeling would be to use nutrient stress or targeting the SR regulatory proteins directly by new compounds51. Such sensitizers to make S. aureus more vulnerable and less virulent would be novel and could again probably be used against other bacteria. Dysregulation or hyper activation of bacterial proteins with damaging consequences is in general a promising strategy52. A third strategy combines our model with our studies on toxic metabolic intermediates53 and targets our network such that toxic intermediates of the AA metabolism accumulate. This can either be achieved by activating the enzymes of the AA metabolism which have a low Michaelis–Menten constant and produce the more toxic intermediates or inhibiting the directly following AA metabolizing enzymes so that the toxic intermediate accumulates. All these different strategies can now be modeled and studied in silico with our model, even transferred to other infections such as fungal infections or gram negative bacteria. Moreover, the most promising compounds can be synthesized and used as leads for novel antibiotics in particular against Gram positive bacteria, and, most importantly, MRSA. With a more selective therapy well known side effects of an aggressive antibiotic therapy like e.g. pseudomembranous colitis, an infection of the colon due to antibiotic induced imbalance in the bacterial colon flora, may be avoided54.There are already antibiotics like CotrimoxazoleS. aureus including testing and modeling the effect of various mutations. The AA essentiality in the WT and in the mutant strains is delineated in the simulation and well supported by the experimental data. Our data support a fast adaptation mechanism by which S. aureus can adapt its AA metabolism in less than 24 h. The in silico model allows also to develop new antibiotic targeting strategies on AA metabolism including its regulation or accumulation of toxic intermediates for S. aureus and MRSA.Here we present a model of SR. We analyze amino acid metabolism (AA) and key regulators such as CodY and CcpA, as well as the Ilv- and the Met-operon and their effects. The network model and its dynamics agree well with data from literature and databases. This is validated directly by conducting growth curve experiments with different AA metabolism mutants . The network is fully made available, is easily simulated and presents a good and reliable representation of SR and regulated processes in AA and energy metabolism in Supplementary Information 1.Supplementary Information 2.


The gut microbiota is the human body’s largest population of microorganisms that interact with human intestinal cells. They use ingested nutrients for fundamental biological processes and have important impacts on human physiology, immunity and metabolome in the gastrointestinal tract.M2R, a Python add-on to cobrapy that allows incorporating information about the gut microbiota metabolism models to human genome-scale metabolic models (GEMs) like RECON3D. The idea behind the software is to modify the lower bounds of the exchange reactions in the model using aggregated in- and out-fluxes from selected microbes. M2R enables users to quickly and easily modify the pool of the metabolites that enter and leave the GEM, which is particularly important for those looking into an analysis of the metabolic interaction between the gut microbiota and human cells and its dysregulation.Here, we present M2R is freely available under an MIT License at https://github.com/e-weglarz-tomczak/m2r.Bioinformatics online. The human cell metabolism is regulated by cell-intrinsic and cell-extrinsic factors. For instance, cell-intrinsic factors are genes and hormones, while cell-extrinsic factors are associated with the environment, and include nutrients availability, tissue-specific context and microbiota that resides on or within human tissues. Genome-wide modeling provides a useful approach to uncover the molecular basis of metabolism . Its rolcobrapy (M2R).Here, we consider a problem of the host-microbiota co-metabolism and the influence of the gut microbiota on the pool of metabolites available to a host. The goal of this work is to provide a method and software that modifies a human genome-scale metabolic model (e.g. Recon3D) using information about in- and out-fluxes obtained through the Flux Balance Analysis (M2R consists of five steps: (i) loading microbiota models, (ii) reading in- and out-fluxes of metabolites, (iii) aggregating all in- and out-fluxes, (iv) normalizing all in- and out-fluxes and (v) modifying reactions in a genome-scale reconstruction metabolic model that contain metabolites present in the loaded gut microbiota models. A schematic representation of our framework is presented inM2R is implemented in Python (the code could be run using Python 2.x or Python 3.x), using cobrapy for loading, writing and processing models.The main idea of the presented program is to modify a metabolic model by taking into account information of metabolites that take part in the external reactions of the selected gut microbiota models. Typically, genome-scale metabolic models are assumed to be in their steady-state. Hence, it is possible to consider only those metabolites of the gut microbe(s) that are part of their external reactions, because these are responsible for the consumption or production of nutrients.In analyses based on fluxes, a lower/upper bound of a reaction determines a possible uptake/secretion. Further, if there is no prior knowledge available like gene expressions or proteomics, and/or nutrients availability data the lower/upper bounds of reactions in the model are typically set to an arbitrary value, e.g. 1000.rLB is a lower-bound of a reaction r, if and of denote an in-flux and an out-flux of the considered gut microbe(s) metabolite involved in the reaction r, respectively, and if and/or of equals 0.The gut microbiota could consume nutrients or produce metabolites, thus, we can use this information to modify the lower bounds of the reactions in the host model. First, we need to read and aggregate fluxes of the microbe(s) metabolites associated with the microbe(s) external reactions. Then, we normalize them to a given value, i.e. by dividing aggregated in- and out-fluxes by the maximum value of all fluxes and then multiplying them by the desired value, e.g. 1000. Finally, we update the lower bounds associated with the microbe(s) metabolites as follows: α that defines the weight of the biological systems. Since it is estimated that in a human body there are at least as many microbes as nucleated human cells, we set α to 0.5 in our demonstration.We introduce the factor M2R is easy-to-use and allows us to update the pool of nutrients/metabolites available for the host based on the gut microbiota metabolism. Lastly, the code is freely available and it could be further developed in the future.In this work, we present Python-based software for modifying the human genome-scale metabolic model with the gut microbe(s) model(s). Its development in Python, an open-source platform, simplifies distribution and enables the use on most operating systems. E.W.-T. was financed by a grant within Mobilność Plus V from the Polish Ministry of Science and Higher Education [1639/MOB/V/2017/0].Conflict of Interest: none declared.btab060_Supplementary_DataClick here for additional data file.


Retinal photoreceptors are amongst the most metabolically active cells in the body, consuming more glucose as a metabolic substrate than even the brain. This ensures that there is sufficient energy to establish and maintain photoreceptor functions during and after their differentiation. Such high dependence on glucose metabolism is conserved across vertebrates, including zebrafish from early larval through to adult retinal stages. As the zebrafish retina develops rapidly, reaching an adult-like structure by 72 hours post fertilisation, zebrafish larvae can be used to study metabolism not only during retinogenesis, but also in functionally mature retinae. The interplay between rod and cone photoreceptors and the neighbouring retinal pigment epithelium (RPE) cells establishes a metabolic ecosystem that provides essential control of their individual functions, overall maintaining healthy vision. The RPE facilitates efficient supply of glucose from the choroidal vasculature to the photoreceptors, which produce metabolic products that in turn fuel RPE metabolism. Many inherited retinal diseases (IRDs) result in photoreceptor degeneration, either directly arising from photoreceptor-specific mutations or secondary to RPE loss, leading to sight loss. Evidence from a number of vertebrate studies suggests that the imbalance of the metabolic ecosystem in the outer retina contributes to metabolic failure and disease pathogenesis. The use of larval zebrafish mutants with disease-specific mutations that mirror those seen in human patients allows us to uncover mechanisms of such dysregulation and disease pathology with progression from embryonic to adult stages, as well as providing a means of testing novel therapeutic approaches. The zebrafish retina, akin to its human counterpart and the retina of other vertebrates, is one of the most metabolically active tissues in the body . PhotoreThe metabolic profile of the vertebrate retina is unique and complex, encompassing adaptations that enable rod and cone photoreceptors to perform their function and promote survival . Hence, The current review will discuss the conserved structure and function of developing and mature zebrafish retina, including evidence from a range of other vertebrate species. We will explore the metabolism and regulatory mechanisms between rods and cones, as well as the RPE and Müller glia cells to sustain visual function. Lastly, the use of zebrafish models of human IRDs will be explored in this context, highlighting how they have contributed to our current understanding of the molecular basis of these disorders and their progression.Danio rerio) is a well-established model organism, frequently used for investigating the genetic and molecular basis of human development and disease ) and exhibits an ocular phenotype similar to that of LCA patients, albeit with a later onset [td15obe mutants revealed a general thinning of the retina and severe photoreceptor and RPE degeneration, accompanied by decreased visual acuity, as determined by OKR. Analysis of retinal ultrastructure in the mutant fish showed mitochondrial alterations in cone photoreceptors, including their enlargement and a more compact distribution within the IS at 12 months. Mitochondria are able to withstand stress conditions, such as changing Ca2+ concentrations, to a degree before employing compensatory mechanisms, for example changing their size or shape [td15obe model [LCA represents the most severe type of IRD, with patients often presenting with blindness in the first year of life. There are 26 causative genes that affect a number of different cellular processes, leading to the disease phenotype . One suc the RPE . A recener onset . By 12 mor shape ,160. SucIt is becoming increasingly clear that metabolism is fundamental to photoreceptor function and survival in all vertebrates. The highly conserved, carefully controlled metabolic pathways enable rod and cone photoreceptors in the outer retina to maintain distinct catabolic and anabolic reactions and differentially respond to changes in light intensity, mediating visual function. The interplay between photoreceptors and the surrounding cells ensures that their respective metabolic demands are met, by supplying the correct substrates to fuel their independent metabolic reactions. Thus, disturbing metabolism in photoreceptor cells, RPE and/or the choroid, can result in retinal degeneration and consequent sight loss. Due to their diurnal nature and vision-dependent behavioural features, such as prey capture, zebrafish appear to be particularly adapted to maximise energy metabolism in cones, although further studies are necessary to obtain a more complete picture of their metabolic profiles and how it compares to other vertebrate species. Additionally, understanding how glucose metabolism influences the development, survival, and viability of rods and cones in the zebrafish retina can provide crucial insight into the possible mechanisms behind human retinal disease pathologies, since retinal structure and function are highly conserved across all vertebrates. This is key to identifying a common metabolic pathway to target, which would aid the development of therapeutic approaches for a range of IRDs and prevent photoreceptor degeneration irrespective of the specific genetic causes, which remain heterogenous and difficult to tackle individually.


Escherichia coli. Our results indicate that the growth rate, the abundance and appearance time of different cell phenotypes as well as the amount of by-products secreted to the medium are sensitive to some extent to the local crowding conditions in all scenarios tested, except in rich-nutrient media. Crowding conditions enhance the formation of nutrient gradient in biofilms, but its effect is only appreciated when cell metabolism is controlled by the nutrient limitation. Thus, as soon as biomass (and/or any other extracellular macromolecule) accumulates in a region, and cells occupy more than 14% of the volume fraction, the crowding effect must not be underestimated, as the microbial dynamics start to deviate from the ideal/expected behaviour that assumes volumeless cells or when a homogeneous (reduced) diffusion is applied in the simulation. The modeling and simulation of the interplay between the species diversity (cell shape and metabolism) and the environmental conditions  can help to design effective strategies for the optimization and control of microbial systems.Microorganisms are frequently organized into crowded structures that affect the nutrients diffusion. This reduction in metabolite diffusion could modify the microbial dynamics, meaning that computational methods for studying microbial systems need accurate ways to model the crowding conditions. We previously developed a computational framework, termed CROMICS, that incorporates the effect of the (time-dependent) crowding conditions on the spatio-temporal modeling of microbial communities, and we used it to demonstrate the crowding influence on the community dynamics. To further identify scenarios where crowding should be considered in microbial modeling, we herein applied and extended CROMICS to simulate several environmental conditions that could potentially boost or dampen the crowding influence in biofilms. We explore whether the nutrient supply (rich- or low-nutrient media), the cell-packing configuration , or the cell growing conditions (planktonic state or biofilm) modify the crowding influence on the growth of  In nature, many organisms grow in crowded biofilms that protect against stressful conditions, making their control/eradication a challenge. Modeling these microbial systems is a valuable tool for studying the interactions among cells and exploring strategies for manipulating the system. Even though the composition of biofilms changes over time due to the accumulation of biomolecules in the medium as well as the growth of cells—both in size and number, many current modeling methods do not explicitly take into account for these changes. This study analyzes how sensitive the biofilm simulation is to these crowding conditions to determine whether they can be safely ignored or need to be included for accurate results. We compared different simplifications of the crowding effect on spatio-temporal microbial simulations under several scenarios. We found that the traditional use of a reduced diffusion constant fails to capture the heterogeneous nature of a biofilm and could introduce deviations to the dynamics of the system , especially in poor nutrient mediums. The crowding conditions modeling in microbial systems can provide a guidence for selecting effective treatments to disrupt and control biofilms associated to chronic diseases. The spatio-temporal modeling of microbial systems can shed light on the dynamics and species interactions –4, the pSeveral frameworks have already been proposed to integrate the metabolic information of microbial species, estimated using either Monod kinetics –7,9 or tCROwding Modeling of In-silico Community Systems (CROMICS), a methodology that captures the effect of crowding conditions on the spatio-temporal modeling of microbial communities (Manuscript submitted). CROMICS combines techniques such as individual-based modeling (IbM) and thermodynamics flux analysis (TFA)  stoichiometric matrix of the m intracellular metabolites and n biochemical and transport reactions, and v is the [n x 1] flux vector. As in the original TFA formulation, each reversible reaction (and the corresponding flux) in this formulation is divided into its forward and backward components. vUf (mmol gDW-1 h-1) that a reaction f can take for the current environmental conditions. The exchange flux vUf,ex,met is determined by the local substrate concentration met and the active/passive transport inside the cell  couples the thermodynamic and mass constraints by preventing the flux through a reaction with positive Gibbs free energy Q is a constant with a large value (arbitrary). The value of ηf,a and the logarithm of the intracellular concentration Cint,a of the metabolites a involved in reaction f, i.e., ca = ln where the standard concentration C0 is equal to 1 M , enzymatic reactions (with suffix u), and ribosomal proteins required to maintain a growth rate (with suffix R). The weighting parameters are set to wu = 1.55 x 10−3 gDW h mmol-1, wR = 0.169 h [wex = 0.Finally, vbio and vf,ex,met. The advantage of using NNs is the fast computational time for the exchange metabolic fluxes, even when complex objective functions are required by the stoichiometric-based models. The NN of a microbial species is trained using (as outputs) the metabolic flux solutions computed by aTFA for 100,000 uptake-flux samples (inputs). These flux samples were used to constrain the upper flux limits  of the corresponding metabolites in the aTFA formulation (Eq 1c). An uptake-flux sample consists of n uptake fluxes, such as glucose, acetate, and O2 (as in our case study), where the flux of each metabolite met was randomly taken from the range 0 to VM,met, i.e., the physiological maximum uptake flux (mmol gDW-1 h-1). Other exchange fluxes in the GEM model were left unconstrained . To reduce the possible alternative flux solutions originating from underdetermined stoichiometric systems, two consecutive aTFA optimizations—the maximization of biomass and the subsequent minimization of the sum of the fluxes subject to previously computed vbio [vshrinkage in an attempt to maintain the cell energy requirements.CROMICS uses neural networks (NNs) as an alternative for estimating ted vbio —were perE. coli, a NN was created containing 2 hidden layers of 15 neurons each using the Neural Network toolbox of Matlab R2018b. The Pearson correlation coefficient r between the fluxes predicted by NN and aTFA was estimated to be 0.999, while the normalized mean squared error was 8.5 x 10−5 , where collections of molecules moving across the lattice are followed at every (discrete) time step Δt. The number of metabolites met, Fd,met (mmols per box), that jump from box ij to one of the nvox contiguous boxes ijnext  (Eq 16). In this way, the crowding conditions affect both the effective concentration Ceff,met (Eq 15) and the movement (diffusion) of metabolites across the system (Eq 8).The factor 1/wd is a weight factor that depends on the type of grid and the direction d in the scheme lattice , is computed as:The macroscopic density of metabolites ωmet in x, Δt, and the diffusion coefficient in a dilute (non-crowded) medium, such as water, Dw,met (mm2 s-1).The relaxation parameter pL is equal to 4 for SqL, and 8 for HexL [x and Δt, and therefore of ωmet, determine the accuracy of cLBM. Numerical difficulties are found for ωmet > 1, and more accurate simulations are obtained when ωmet approaches 1 [The parameter for HexL ,41. The oaches 1 .Dw,met of different orders of magnitude. For example, the ratio Dw,glucose/Dw,oxygen is equal to 0.33 [x and Δt, ωglucose/ωoxygen = 0.66. This leads to inaccuracies in the diffusion results, since ωmet < 1 for at least one of the metabolites. Furthermore, if met is the limiting substrate, the error can be propagated to the calculations of the metabolic capabilities of microbial cell.In biofilm modeling, the simulated metabolites can have  to 0.33 . Thus, fωmet < 1, the details of which are provided in Fd,met (t (where ωmet < 1), can be computed directly from t/Δt1 as:t1 is the time step that makes ωmet = 1. The cLBM correction proposed for ωmet < 1 has been validated using the diffusion simulations computed by the MC algorithm [ω-correction shows a very good agreement with those computed by MC Click here for additional data file.S2 FigThe glucose supply was set to 2.25 mM.(TIF)Click here for additional data file.S3 FigH normalized by Δx. (D) Number cells identified with phenotype respiration: glucose + O2 ➔ (acetate) + biomass, fermentation: glucose ➔ acetate + biomass, and inactive cells.2D microbial system was discretized using a square lattice. (A) Biomass. (B) Acetate produced. (C) Active layer depth (TIF)Click here for additional data file.S4 Fig−5, while the Pearson correlation r is 1. Fluxes vf are given in mmol gDW-1 h-1, while the growth rate vbio is in h-1.The NN with 2 hidden layers of 15 neurons each was trained using 100 K random samples. The normalized mean square error between the fluxes predicted by aTFA and NN was estimated to be 8.5 x 10(TIF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.