dataesr/scientific-paragraphs-categorization huggingface dataset:
In terms of gene number, these losses represent 18% of the total number of genes detected by OrthoFinder (i. e. 5,616 genes). However, 70% of the genes (i. e. 716 genes) were finally included in the GSMN as they possess correspondence via the annotation or with external sources (S5 Fig in  S1 File ). The remaining unmapped 307 sequences were lost and represent a set to be preferentially explored for future network improvements. Thus, the orthology subnetwork is composed of 4,615 genes, 1,550 pathways, 2,946 enzymatic reactions and 3,238 metabolites. Enrichment with external sources. Penicillium rubens is a widely studied model organism, but the available data heterogeneity makes conciliation a delicate task. Therefore, the choice to add external data was made on consistent sources, namely PchCyc, the Pathway/ Genome Database (PGDB) from BioCyc  [38] , and the latest version of the organism's network, called Prubens  [39].
At that stage, those latter compounds cannot be monitored within the network unless focusing on their closest referenced precursor. Finally, set 'Targets1' is thus composed of 243 targets. The second list is obtained by querying specific NP databases. Using a taxonomy-oriented query from the naturaL prOducTs occUrrence databaSe (LOTUS)  [43] , 240 compounds for P. chrysogenum/P. rubens are retrieved along with their InChIKeys, which are used to search within the MetaCyc database. Based on an exact InChIKey match, followed by an analogue of the first two sections of the identifier and finally on the first part (i. e. corresponding to planar chemical structure search), only 8, 11 and 47 metabolites were found, respectively. Depending on these criteria, between 4, 7 or 13 of these metabolites were already present in the draft.
The database used for gap-filling (MetaCyc v23. 0) contains 17,201 reactions. As gap-filling is a heuristic approach, these reactions are filtered into two categories to select the more relevant set of reactions to query. The filtering presented below reduces the space to be interrogated by 46%. Consequently, Gap-filling was run firstly with MetaCyc subset reactions (i. e. 9,281 reactions) with a high confidence weight. The first category corresponds to reactions related to the fungi kingdom. This list was constructed based on a meta-network obtained by the reconstructions of a species' repertoires belonging to the fungi kingdom  [44]. This meta-network is composed of 3,810 reactions. Interestingly, 77% of the reactions (i. e. 2,964 reactions) were already present in the draft. This list of reactions was completed by the BioCyc PGDBs of Candida albicans, Exophiala dermatitidis and Saccharomyces cerevisiae, composed of 1,320, 1,714 and 1,562 reactions, respectively.
Combining these three latter networks yield a set of 2,905 reactions, among which 70% (i. e. The selection was based on data from the literature (i. e. Biomass  [26] , Sugar Catabolism  [40] , Penicillin Biosynthesis Pathways  [41]  and literature details on Supporting Information), genome mining  [42]  and LOTUS databases  [43]. All targets are divided according to constitutive (■ -215) and specialised (■ -438) metabolism. Metabolites found (✓) or not (✗) in the different versions of GSMNs are shown. Metabolites without MetaCyc-compatible information cannot be included in the networks (i. e. absence of compound identifier as for yanuthone D or the absence of reactions leading to compound production/degradation as for coprogen or PR-Toxin). https://doi. org/10. 1371/journal. pone. 0289757.
Secondly, we focus on the significance of the reversibility of reactions. When the reaction directionality is unknown, it is often assumed as reversible, which can introduce biases in model development and comprehension. To address this, we utilised evolving database annotations and improved knowledge in the field. Thus, we subjected the 967 reversible reactions of the initial reconstruction to further scrutiny using the SmartTable tool on the MetaCyc server. Among the 787 reactions retrieved from MetaCyc, 98 had their reversibility updated by closing one of their bounds. In addition, 165 reactions were without annotation labels, indicating unknown directionality, and thus were blocked. These initial modifications yielded a model that exhibits sensitivity to nitrogen variations. Specifically, by manipulating the openings of various nitrogen sources, we discern changes in the flux distribution within the model.
Comparisons and evolution of Penicillium rubens GSMN Connectivity and metabolic coverage. The GSMNs, iAL1006, Prubens and our reconstruction encompass 1,660, 2,574 and 5,919 reactions that connect 2,429, 3,058 and 5,464 metabolites, respectively. These networks are supported by 1,006, 1,786, and 5,703 different genes, bringing the number of P. rubens genes into iPrub22 (i. e. metabolic coverage) to 45%, surpassing the respective percentages of 8% and 14% observed in the first and second reconstructions from 2013 and 2018. Therefore, besides an increasing number of reactions associated with better metabolic coverage links to recent improvements in databases and annotation methods, a significant evolution observed over the years is logically the enhanced connectivity within the models themselves (Fig  4 ).
This last point ensures the validation of the model's constraint definitions while simultaneously enabling an assessment of the potential production of specialised metabolites within the examined system. We evaluated eight biosynthetic pathways: seven were identified using the SMASH tool, and the eighth used mining reconstruction (i. e. pathways involving averufin and versicolorin B). In the context of databases such as MetaCyc, a pathway refers to a curated collection of biochemical reactions and associated genes or enzymes that are known to be involved in a specific biological process or metabolic. To provide a comprehensive analysis, we adopted a unified categorisation approach by grouping all intermediates, specialised metabolites, and by-products under the collective term "specialised metabolites" without differentiation between them.
Consequently, our classification encompasses two distinct categories: specialised metabolites and precursors. Indeed, the production of metabolites, such as andrastin A, yanuthone D, and PRtoxin, poses challenges due to the absence of a MetaCyc identifier or associated reactions for their synthesis within the database. Therefore, it becomes necessary to focus on studying their precursors to gain insights into their theoretical distribution fluxes. Furthermore, the potential production of isoepoxydon, roquefortins and assimilates, averufin and assimilates requires the addition of Red-NADPH-hemoprotein reductase, an unbalanced compound with a radical in its empirical formula. This particular compound serves as a flavoprotein catalyst that facilitates the reduction of cytochrome. Additionally, the availability of phenoxyacetate is essential to unblock the flux of penicillin V, while the import of decanoate or octanoate was essential for the production of penicillin K.
Interoperability. To ensure the interoperability of iPrub22, the identifiers of metabolites and reactions were kept consistent with MetaCyc and cross-referred through MetaNetX  [53]. We focus on various annotation enhancements (e. g. identifiers interoperability of metabolites, reactions, and genes between known databases) and on the addition of Systems Biology Ontology terms  [54]  (SBO) to match the standards established via MEMOTE. SBO annotations provide semantic information that characterises the model components  [55, 56]. A relatively low match rate on the BiGG (13%) and KEGG (43%) historical reconstruction databases was noted concerning metabolites annotations. On the other hand, more than two-thirds of the metabolites had a chemical identification (e. g. ChEBI, PubChem). Details are given in Table  2 , and iPrub22's different characteristics are available in Supporting Information in the form of a MATLAB live script (S7 File).
The reconstruction presented here is rated with a MEMOTE score of 74%  [54]. Finally, based on these various annotations, it remains possible to complete them with mass spectral information from the MassBank  [57]  and GNPS  [58]  mass spectral databases for further identification in real biological samples. Based on a mapping from the InChIKey, 508 metabolites were associated with at least one spectral data. Finally, interoperability between tools was ensured by upgrading the network distribution format to comply with SBML Level 3  [59].
Thus, one of the limits of the automated reconstruction lies in the assignment of genes for non-specific reactions (i. e. a large number of genes being associated with such type of reaction). Moreover, intra-operability and especially identifiers durability as the databases are updated is sometimes not guaranteed. For example, of the 1,291 reactions from PchCyc, 79 (6%) have an incompatible identifier with MetaCyc version 23. 0. For these reasons, few tools currently exist that allow automatic comparison of these networks, although efforts are being made in this direction  [64]. As a result, relatively few model organism reconstructions benefit from pre-existing data  [19, 20] , and when they do exist, they require a significant community effort  [18]. Another example of information loss is related to database mapping, which arises in particular when integrating subnetworks obtained by orthology search.
During the creation of the orthology subnetwork, more than half of the reactions were discarded due to a lack of Meta-Cyc-compatible identifiers. These reactions, supported by genomic data, could, therefore, constitute a preferred pool for future completion of the GSMN (i. e. addition of reactions or refinement of existing GPRs). For example, from the 546 lost reactions during the mapping on N. crassa, 505 were allocated to metabolite transport and had model-specific identifiers. Only manual curation will allow for the recovery of such information. Thus, this known interoperability problem (i. e. lack of MetaNetX  [53]  identifiers or model-specific identifiers) highlights the need for uniform writing standards both at the database level and in the intrinsic writing of models. Also, this points out the issue of the relevance of reconciling the subsequently reconstructed networks.
The functional annotation approach should, therefore, cover the one performed by orthology search. However, 16% of the reactions in the draft still come exclusively from one or more subnetworks resulting from the orthology search. Therefore, this method also helps to benefit from the manual expertise within the reconstructions used for orthology search, as it would appear that the information they contain is not systematically referenced in the functional annotation database (i. e. 764 reactions are exclusively specific to the orthology subnetwork-Fig  1 ). In addition, one would also expect the most informative templates to be the most phylogenetically related. In the case of the iPrub22 reconstruction, most information obtained by orthology search came from S. japonica. Therefore, the most informative template for the reconstruction is not fundamentally the most biologically relevant.
It unintentionally and automatically pruned 9 genomic sequences whose 6 belong to the roquefortines pathways. Concerning the second example, within MetaCyc, the reactions leading to different penicillins biosynthesis are listed in 3 pathways: PWY-5629 (isopenicillin-N biosynthesis-2/2 reactions found in the draft); PWY-7716 (penicillin G and penicillin V biosynthesis-3/4 reactions found in the draft); PWY-5630 (penicillin K biosynthesis-1/1 reactions found in the draft). Although their names vary from one database to another and within the literature, the genes responsible for the production of these compounds were identified as pcbAB or ACV or ACVS (Pc21g21390); pcbC or IPN or IPNS (Pc21g21380); pcbDE or iAT or AAT or PenDE (Pc21g21370); phl or pclA (Pc21g14900). PenDE, ACVS and phl were discarded by PathoLogic during reconstruction like the roquefortines biosynthetic pathways genes.
g. preferential selection of spontaneous or fungi reactions). Iterative processes were performed with the repair networks and targets from most to least relevant to achieve 'reasoned gap-filling'. Nonetheless, beware that performing a complete gap-filling, i. e. using the set of putative metabolites present in our reconstruction against the entire MetaCyc database, is absurd from a biological and evolutionary point of view. This set was used only against controlled versions of repair networks (i. e. for the detection of spontaneous reactions and for the last round of gap-filling whose supporting genomic sequences are unknown in MetaCyc). By reasoning in this way, it is possible to give an additional confidence weight to almost half of the gap-filling reactions (S12 Fig in  S1 File ). As shown in the results presented in Table  1 , gap-filling improves the connectivity of the GSMN.
It is well known that interoperability between databases is a delicate task. Therefore, the most exhaustive annotation of the entities in the model is essential and is now an undeniable criterion for increasing the quality of a network  [23, 25]. Firstly, when these annotations belong to specific databases such as KNApSAcK (i. e. a database dedicated to specialised metabolites), they constitute an entry point for model exploration. For information purposes, iPrub22 contains 80 metabolites annotated with an identifier from this database. Secondly, the molecule's structural identifiers allow the entities to be identified and used as an anchor for querying other databases. For instance, using an InChIKey mapping considering only the first identifier section, thus disregarding the stereochemistry and compound's charge, 9.
8% of GSMN metabolites are linked to mass spectral information retrieved from the GNPS and MassBank databases. This coverage is lower than the average 40% detected by Frainay et al. work  [73]. This confirms the ever-limited aspect of obtaining and integrating metabolomics data acquired in given organisms within GSMNs. Such difficulty could be one more problem in validating GSMN behaviour, at the chemical level, with respect to biological observation. However, as mentioned above, MEMOTE  [54]  is a suite of tests aimed at qualifying a reconstruction or model by providing a summary of its characteristics both in terms of topology and flux. Its quality score considers the annotation content of the model. For example, a complete annotation in SBO terms guarantees a quality of 25%. This tool provides an easily accessible resource aiming toward model standardisation for detecting blocking points and/or abnormalities, thus helping manual curation.
This is particularly true for analyses related to genes and their products, as this notation was only introduced in Version 2 of the FBC package. The recurrent non-reproducibility of results, known as the reproducibility crisis  [75] , affects all areas of science and in silico modelling is no exception  [76]. In fact, limited interoperability between software may yield incorrect models parsing, which can lead to errors or misunderstandings when analysing GSMNs  [77]. Thus, the proposed model was standardised in the most recent Versions of SBML and its extension package flux balance constraints (FBC). However, even if the emphasis on reconstruction has been placed on the maximum transparency and traceability of data acquisition, with the constant evolution of databases and tools, providing all the information for the strict whole model reproducibility remains challenging.
[24]  (e. g. inconsistent media definition in specific databases or undefined chemical input in experimental settings). However, environmental specification is a critical component of flux analyses and serves as the cornerstone for such investigations. As filamentous fungi are known to possess cellular machinery that enables extracellular nutrient absorption, uptake reaction selection was carried out by focusing on extracellular metabolites. Specifically, we integrated reactions from the well-curated iAL1006 model and complemented them with information on the extracellular localisation of gene products (e. g. inclusion of reversible or non-reversible transport reactions between the intracellular and extracellular compartments, regardless of whether they are artefactual). Thus, we adopted the "seed" approach (i.
e. metabolites occurring naturally in the environment of the organism studied and essential input for the GSMN) to generate biologically relevant models that have the potential to enhance our understanding of metabolic processes and facilitate future investigations. Indeed with 185 uptake reactions included in the reconstruction, iPrub22 offers a broad range of possibilities for modelling simulations. Moreover, proposing diverse combinations will give clues on organism behaviour by identifying aspects of iPrub22 predictions that exhibit either high sensitivity or resilience to changes in the environmental composition. Finally, once the nutrient nature has been defined, the subsequent step entails relying on experimental data to establish proper uptake bounds for flux simulation analyses. Then, the modifications performed to deal with problems encountered when converting the reconstruction to a flux model focus mainly on updating and correcting (1) the directionality of reversible reactions (i.
e. adding constraints by closing reactions whose directionality remains unknown on MetaCyc), (2) the redundancy contained in the reconstruction and (3) the identification of blocked reaction. The redundancy point is addressed at different levels. On the one hand, automatic and manual curation of duplicated reactions is carried out (i. e. reactions with the same reactants and products). The presence of such reactions is explained by the duplicate's existence within the databases and by the data reconciliation contribution (i. e. addition of reactions whose unbalanced equations do not allow their automatic detection). On the other hand, there is redundancy within the networks linked to varying metabolite names. Indeed, the same chemical entity may have several forms, and thus identifiers, that coexist within the model without having a bridge reaction between the different forms.
The above-mentioned modifications (S7 File) have yielded a model that exhibits diverse growth behaviours in response to variations in 35 different carbon sources and 35 different nitrogen sources (Fig 3 displays a sample of the results, and the complete data are provided in the S6 File). This approach provided valuable insights into the models' performance and their responses to varying nutrient availability scenarios. Interestingly, the iPrub22 model demonstrates its ability to reflect some observed behaviours with P. rubens. In accordance with Nielsen et al. observations  [80] , it is established that P. rubens requires a source of carbon, such as sugars, polysaccharides, organic acids, lipids, or certain amino acids, and a source of nitrogen, which can be provided by organic or inorganic compounds like ammonia, nitrate, or nitrite, to support its growth.
According to the classification of metabolites established by the MetaCyc ontologies, 13% of iPrub22 compounds would be associated with specialised metabolism. However, it is not uncommon to find metabolisms building blocks relative to constitutive metabolisms such as geranyl diphosphate or myoinositol in the specialised metabolites lists. This fact illustrates that the specialised metabolites concept, as understood by biologists or chemists, diverges from what is implemented in the databases. Thus, based on the information provided in the literature and the results of genome mining, the percentage of specialised metabolites contained in iPrub22 should be further minimised. It raises the question of what constitutes a specialised metabolite for these communities and highlights the constant need for annotation standardisation.
Firstly, are the metabolic pathways leading to the biosynthesis of those NPs comprehensively elucidated and documented in the literature? Furthermore, if so, are these pathways indexed in the relevant databases such as MetaCyc? For instance, CobraMod  [92]  is a pathway-centric curation tool designed to enable the modification and extension of GSMN. This tool extracts metabolic information from BiGG, KEGG, and BioCyc databases. However, depending on the existence of biosynthetic pathways for the selected metabolites and their degree of completeness, what measures are envisaged to enhance the modelling accuracy? This point leads to the two lists creation, one containing the metabolites for which an identifier exists in MetaCyc, and the other composed of orphan metabolites for which, among other things, the closest known antecedents (i.
These gaps could potentially be resolved by adding modules or a single reaction, balanced and supported by all the genes involved in the biosynthesis of the compounds of interest. Recently an automatic pipeline called BiGMeC (Biosynthetic Gene cluster Metabolic pathway Construction) has been developed to automatically reconstruct metabolic pathways associated with BGCs, specifically targeting PKS and NRPS  [95]. Based on the analysis of antiSMASH outputs, the resulting enzymatic reactions take into account redox cofactors and energy demand. The reactions determined in this way are compatible with the BiGG database, promising a facilitated integration of the biosynthesis pathways encoded in the well-characterised or not BGCs into the GSMNs. Finally, in the case of orphan metabolites, it will be necessary to create both the metabolites and the reactions underpinned by genomic sequences.
To maintain optimal traceability, the addition of these entities will be supported by as much metadata as possible such as bibliographic references for the constitution of biosynthetic pathways, InChI-Key, InChI, PubChem identifiers, molecular weights, charges and SMILES until their incorporation into public databases. In addition to the biological relevance and the creation of a model as close to reality as possible, the interest in precisely recreating these pathways lies in the possibility of subsequently analysing in greater detail the distribution and flux reallocation from the precursors of all the targeted metabolites. The last point concerns the interoperability between specialised metabolic databases and reconstruction databases. The LOTUS database  [43]  (i. e. database of specialised metabolites classified by taxonomy) is queried to target specialised metabolism more specifically.
Of the 240 hits found for P. rubens, only 47 compounds are potentially usable (i. e. metabolites mapped based on their InChIKey versus MetaCyc). This low proportion of integrable data is even more marked if we focus on the Natural Product Atlas database  [96] , a microbial NP resource containing, among others, referenced data for structure, compound names and source organisms. Still, based on the InChIKey string, 102 compounds were reported for P. rubens, but only 6-aminopenicillanate was present in MetaCyc (v23. 0) as in the network structure. Extending the search to the genus Penicillium, 2,159 NPs are reported. However, only 20 of these compounds have an exact match on MetaCyc, and 44 molecules are raised, looking only at the first section of the InChIKey. Among them, 17 are present in iPrub22, including the following seven exact matches: citreoisocoumarin, griseophenone C, brevinamide F, verruculogen, paxilline, 6-aminopenicillanate and β-10-hydroxy-12-demethyl-11,12-dehydropaspaline.
These 17 metabolites are involved in 6 consumption reactions, 8 production reactions and 1 reversible transport reaction. These 15 reactions were introduced in the GSMN from either previous networks (i. e. 3 from iAL1006 and 6 from Prubens), gap-filling (i. e. 4 reactions) or functional annotation (i. e. 2 reactions). Once again, this reflects the limited compatibility and communication between databases for specific data integration since most of the specialised metabolism in the strict sense found in iPrub22 comes from manual processes.