dataesr/scientific-paragraphs-categorization huggingface dataset:
Database Model data are available in the PMDB database under the accession numbers PM0083365, PM0083366, and PM0083367 Materials ATP, GTP, CTP, uridine triphosphate (UTP), methionine, SAM, HEPES, MgCl2, KCl, IPTG, Tris/HCl, Na 2 HPO 4 , NaH 2 PO 4 , potassium phosphate, NaCl, imidazole, bmercaptoethanol, DTT, kanamycin, glycerol, NaOH, HCl, ammonium acetate, Bacto agar, Bacto tryptone, and Bacto yeast extract all other chemicals, and HPLC grade solvents were purchased from commercial sources and used as supplied unless otherwise mentioned. Page Ruler prestained protein ladder, 10-180 kDa was purchased from Thermo Fisher Scientific (Waltham, MA, USA). BL21 (DE3) competent cells were bought from New England Biolabs (NEB, Ipswich, MA, USA). Benzonase and cOmplete His-Tag Purification Resin (NiNTA) were purchased from Sigma-Aldrich (St.
Sequence data collection Methionine adenosyltransferase sequences from M. jannaschii (UniProt: Q58868, METE_METJA), Escherichia coli (UniProt: Q58605, METK_METJA), and MAT1A from Homo sapiens (UniProt: Q00266, METK1_HUMAN) were used to conduct NCBI BLAST  [58]  searches across the nonredundant database. Furthermore, the database search conditions were filtered based on the NCBI recommended e-value cutoff, that is, 1e-5, and only search results with query coverage > 90% and sequence identity > 55% were considered in constructing the initial sequence dataset. The collected sequences were then subjected to reciprocal BLAST searches to confirm orthologous relationship(s), as it is a common computational method for predicting putative orthologs. METK sequences for archaea from the phylogenetic group asgard (superphylum) were not considered in this study owing to the lack of taxonomic classifications associated with the sequences that were reported as hits in the NCBI BLAST searches.
Sequence analyses The CD-HIT program  [59]  was utilized to reduce sequence redundancy, by clustering at an 80% sequence identity cutoff threshold, with default settings. The Gblocks program  [60]  was implemented to identify highly conserved sites across sequence alignments, with assistance from the secondary structure information (from experimentally solved crystal structures) to guide the alignments. Sequence alignment was carried out using the MAFFT computer program  [61] , followed by a manual check to identify any errors  [59]. To reduce the sequence redundancy, we used CD-HIT, by clustering at 80% sequence identity cutoff threshold with default settings. The Gblocks program  [60]  was used to identify highly conserved sites across sequence alignments with assistance from the secondary structure.
SSN studies Sequence similarity networks were constructed based on the output obtained from the EFI-Enzyme Similarity Tool (EST) server  [62] , and subsequently, the cytoscape program (https://cytoscape. org/) was utilized to explore the SSNs via the organic layout. Here, we managed the initial dataset using CD-HIT-based clustering by selecting a sequence identity threshold of 80%. The 'organic' layout algorithm was chosen for graphical clustering.
Phylogenetic analysis and ancestral sequence reconstruction ModelTest  [63]  was used to pick out the best evolutionary model for sequence alignments based on the Bayesian inference criterion. The LG model  [64]  with invariant sites (+I) for discrete gamma categories (+G4) was selected to construct maximum likelihood (ML) phylogenetic trees. Initially, we constructed ML-based trees with the IQ-TREE program  [65]  (using 10,000 bootstrap replicates) for the datasets, to inspect the topology distribution within the dataset. The confidence level for the nodes was assessed using Felsenstein's bootstrap method  [66] , and the consensus tree was redrawn using FigTree (http://tree. bio. ed. ac. uk/software/ figtree/). The tree file from the IQ-TREE program output was parsed to obtain the ancestral sequences at the highlighted nodes.
Molecular modeling and structural studies Ancestral sequences (of interest) were modeled using the Swiss model at the ExPASy server (https://swissmodel. expasy. org/). 3D models were visualized using PYMOL (https://pymol. org/2), to identify, tabulate, and study the key interaction residues. The overall quality of the 3D models depends directly on the shared sequence identity between the target sequence and the sequence of the template structure; therefore, we utilized template structures with high sequence similarity. Interface residues were identified using PYMOL and the PISA server (https://www. ebi. ac. uk/pdbe/pisa/). Structural superposition studies were performed using the mtm-align server  [69]. The 17 sequences for Fig. 6 , Figs S4 and S5 were collected using blastp and the PDB database for sequence search.
Charge and hydrophobicity calculations Per-site hydrophobicity and hydrophilicity calculations were carried out using the ProtScale program  [70]  from the Expasy server (https://web. expasy. org). We implemented the default 'Kyte-Doolittle' scale  [71]  for the calculations, with a residue window size of three, while the rest of the settings were left as default. Charge distribution per-site was calculated using the charge server from EMBOSS (http://www. bioinformatics. nl/cgi-bin/emboss/charge). A window length of size 3 was implemented, and the rest of the settings were left as default.
Simulated population We simulated a livestock population over 50 generations of selection. As a first step, we constructed a historical population in which selection was absent and mating was at random, using the QMSim software  [44]. The first 2000 generations (generation -3050 to -1050) consisted of 1500 individuals, after which the size of the population gradually decreased to 100 over 500 generations (generation -1050 to -550) to generate linkage disequilibrium. This was followed by a gradual increase in population size to 1500 over 500 generations (generation -550 to -50). From the last historical generation (generation -50), 100 females and 100 males were randomly sampled and used as input for our own custom Fortran program, in which they were randomly mated (mating ratio 1:1) with a litter size of 10 (5 females and 5 males).
information of the selection candidates (PBLUP_OP). The fourth and fifth methods selected individuals with the highest genomic estimated breeding values from a genomic BLUP model that either included own performance information of the selection candidates (GBLUP_OP) or not (GBLUP_NoOP). Breeding value estimation for the last three methods was performed using the MTG2 software  [58]. Each generation, breeding values were estimated simultaneously with estimating the variance components, using the phenotypic information of the previous three generations, and for PBLUP_OP and GBLUP_OP using also phenotypic information of the present generation. The PBLUP method used a relationship matrix based on a pedigree that included all individuals from the present generation and the previous eight generations.
Numerous large scale maps are now made readily available in databases or repositories  [15] [16] [17] [18] , and have become a fundamental resource in Systems Medicine  [18]. Such maps, by nature, represent static knowledge of molecular mechanisms. However, they can be used as a backbone for the construction of various kinds of mathematical models  [19]. A number of graphical tools allow users to build dynamical models together with their underlying molecular networks, represented as maps  [20]. Moreover, methods and tools have been proposed to generate dynamical models automatically from maps, using quantitative  [21] [22] [23]  or qualitative  [23] [24] [25]  interpretations. Such methods are particularly suited to build large scale Boolean models, since their parameterization might depend solely on the structure of the map.
The circadian clock map Several maps of the circadian clock mechanism have been generated and made publicly available through the PANTHER (http:// www. panth erdb. org/)  [28] , Reactome (https:// react ome. org/)  [15]  or KEGG (https:// www. genome. jp/ kegg/)  [18]  databases. Although these interactive maps are useful tools for data exploration and pathway analysis, they all suffer from specific limitations ranging from absence or over abstraction of key regulatory modules to unbalanced complexity, to format conversion and layout rendering issues. This makes their direct use as sub-networks of larger maps time consuming if not virtually impossible. We therefore undertook the construction of a novel map of the mammalian core circadian clock devoid of such limits with two main objectives in mind: (1) the map should be able to capture the known dynamics of the circadian clock when analyzed using the qualitative modelling approach used in this paper and (2) allow the merging with an existing SBGN PD map recapitulating the cell cycle network  [27].
1  Detailed view of the NR1D1 subnetwork, visualized in CellDesigner. The subnetwork is represented using the CellDesigner format  [33] , which is compatible with the SBGN Process Description language  [26]  gene is a transcriptional target of the CLOCK:ARNTL heterodimer. The map contains 156 species associated with 14 genes, 14 mRNA and 43 proteins and their prosttranslational modifications, association or degradation, 4 simple molecules and heat. One hundred and eighteen processes include 21 associations, 7 dissociations, 14 transcriptional and 14 translational events, 42 posttranslational modifications and 20 transports occurring between the cytoplasm and either the nucleus or extracellular compartments. Heat and glucocorticoids (GC) displayed in the extracellular space represent important inputs contributing to the synchronization of peripheral clocks  [30, 31].
Merging of the circadian clock and cell cycle maps For the validation of our map and its coupling with the cell cycle, we merged it with the detailed map of the mammalian cell cycle (RB/E2F pathway) provided in  [27]. Merging the two maps first required a harmonization step, which was realized programmatically. This step was necessary since our map was built so it could also be used as a standalone, and followed slightly different or updated nomenclatures compared to the cell cycle map. Moreover, some entities were not described with the same level of details in the two maps, e. g. , with respect to phosphorylation sites. Based on our knowledge of the maps, we manually listed these entities, and the required modifications. Rather than proceeding to these modifications manually, we implemented them in a program using the csbgnpy Python library, which can modify the conceptual models underlying SBGN PD maps (see "Methods" section for more details).
Detailed map of the circadian clock We built a new map of the circadian clock that encompasses all known key regulatory mechanisms governing the core clock, as well as key entities that also play a major role in the mammalian cell cycle. To our knowledge, this map is the most detailed and up-to-date map made publicly available as compared to the currently existing maps from Reactome (https:// react ome. org), PANTHER (http:// www. panth erdb. org), and KEGG (https:// www. genome. jp/ kegg) databases. The circadian clock map from Reactome includes a moderately detailed core clock and numerous clock outputs while the ones from PANTHER and KEGG describe a minimalist core clock with 6 and 13 nodes respectively. Compared to these two existing resources, the present map focuses on the core clock components, includes important synchronisation input pathways and nodes present in other critical cellular processes (cell cycle, metabolism), and incorporates a detailed and updated information related to numerous posttranslational modifications (phosphorylation, acetylation, poly-ADP-ribosylation, and ubiquitination).
Quantitative models with a greater level of detail in the prosttranslational regulation of the clock have been produced, but combining their precision with the size of the network of our map remains a highly challenging task  [47]. The map was designed using CellDesigner  [33]  and is compatible with the SBGN PD standard  [26]. As such, it may be easily shared, edited, and updated. Its referencing and integration in map databases, such as the Atlas of Cancer Signalling Network  [48] , is under consideration.
• Modulation arc: represents the modulation of a process by an entity pool. There are five types of modulation arcs: the generic modulation, stimulation, inhibition, necessary stimulation, and catalysis. • Logical Operator Node: represents complex logical functions that model modulations involving more than one EPN. There are three types of logical operator nodes: the AND operator, OR operator, and NOT operator. An actual drawing in SBGN of a network is called an SBGN map. SBGN maps may be stored and exchanged under the SBGN-ML format  [57]. Multiple software allow users to draw such maps  [58] , one of which is CellDesigner  [33]. This particular software focuses on the edition of SBGN PD maps and has been used to draw large comprehensive maps  [27, 28, 48, 59, 60]. It only partially supports the SBGN-ML format and mostly relies on its own format to import and export maps.
As eukaryotic circadian clocks are based on interwoven transcriptional/translational feedback loops, the circadian clock map incorporates genes, mRNA and protein products with their associated posttranslational modifications (phosphorylation, ubiquitination, acetylation). However, when transcriptional regulation was not essential or not documented, only proteins were considered for some modules. It also incorporates input pathways known to be important for the synchronization of peripheral clocks such as glucocorticoid signaling and temperature. Genetic interactions not supported by mechanistic evidence have not been included. This map compiles only experimentally validated results obtained mostly in the mouse animal model. The map is enriched with more than 300 external links to the Entrez, UniprotKB, ChEBI and PUBMED databases.
Modification and merging of the cell cycle and circadian clock maps In order to modify and merge the cell cycle and circadian clock maps, we developed csbgnpy, a Python library that eases the manipulation of SBGN PD maps, at the conceptual level. With csbgnpy, maps can be modified programmatically without alteration of the input files, and the applied modifications can be easily tracked. csbgnpy extracts all the biological and biochemical concepts represented by a map (e. g. entity pools, processes, modulations) and stores them in memory under the form of Python objects that can be manipulated using simple Python code. The operations facilitated by csbgnpy include the creation of new concepts and their addition to a map; the search for concepts in a map; the deletion or modification of concepts of a map; and the construction of new maps by merging or intersecting two or more maps.
Concepts in csbgnpy may also be manipulated using a new textual representation called sbgntxt, that eases their construction or identification. csbgnpy can read maps from different file formats such as SBGN-ML, CellDesigner and BioPAX (using third-party libraries or converters, in particular cd2sbgnml  [61]  for the CellDesigner format) and write maps to the SBGN-ML format (with no layout). csbgnpy is freely available at https:// github. com/ Adrie nroug ny/ csbgn py, and can be modified and redistributed under the terms of the GPLv3 license. The modification and merging of the two maps, as well as the export of the resulting map to an SBGN-ML file, were performed programmatically using csbgnpy, directly from the input CellDesigner files. All changes made to both maps are described in details in Additional file 1.
Construction of the dynamical model We built a qualitative dynamical model from the merged map using software Pint  [10]. Pint is a static analyzer for dynamics of asynchronous automate networks (AN). It can also be used to generate a dynamical model encoded under the form of an AN from an SBGN PD map stored under the SBGN-ML format. Generation of the model relies on the general semantics, which is a qualitative dynamical interpretation of SBGN PD maps described in  [24]. We briefly describe this interpretation and the encoding under the form of an AN hereafter. The general semantics is a qualitative Boolean interpretation that extends the one of BIOCHAM  [62]. Under this interpretation, (i) each EPN may be in one of two states "present" or "absent"; (ii) each process may be in one of two states "switched on" or "switched off "; (iii) each modulation may be in one of two states "active" or "inactive".
Model analysis Construction of the precursor states The set of precursor states was built based on two constraints: each precursor state (1) should contain the complex formed of the three proteins pRB (unphosphorylated), DP1, and E2F1 and (2) should allow all entities of the map to be reached when not considering effects of modulators on processes. We used Answer Set Programming (ASP)  [63]  and software clingo  [64]  to compute the set of precursor states. For this, we built an ASP program encoding the reaction graph underlying the merged map (with all modulations removed) and the following rules and constraints: • Rule 1: an entity can be in the precursor state or not; • Rule 2: if an entity is in the precursor state, then it is reachable; • Rule 3: if an entity has a process producing it and consuming an empty set (source), then it is reachable; • Rule 4: if an entity is produced by a process whose reactants are reachable, then it is reachable; • Constraint 1: the precursor state must contain the complex formed of the three proteins pRB (unphosphorylated), DP1 and E2F1; • Constraint 2: all entities must be reachable; • Optimization 1: minimize the total number of entities of the precursor state; • Optimization 2: minimize the total number of state variables set to a value of the entities of the precursor state; • Optimization 3: minimize the total number of complexes of the precursor state.
Rule 1 generates all possible precursor states. Rules 2-4 defines reachable entities. Constraint 1 and 2 constrain the set of output precursor states: an precursor state must contain the aforementioned complex, and all the entities it contains must be reachable. Optimization 1-3 further reduce the number of output precursor states, by ensuring that they are minimal with respect to cardinality and their number of unmodified and free proteins. The ASP program was then solved using clingo. There were 24 optimal solutions, constituting the 24 precursor states considered in this study. Dynamical analysis We used static analyzer Pint  [10]  to perform the various dynamical analyses of the validation process. For asynchronous automata networks (similarly to asynchronous Boolean networks), the verification of reachability properties has a high computational complexity.
Funding This research was funded by the  French Agence Nationale pour la Recherche (ANR) :  HYCLOCK  project ( ANR-14-CE09-0011 ),  iCycle project  ( ANR-16-CE33-0016-01 ),  AlgoReCell  project ( ANR-16-CE12-0034 ), " Investments for the Future" LABEX SIGNALIFE  ( ANR-11-LABX-0028-01 ), UCAJEDI Investments in the  Future  project ( ANR-15-IDEX-01 ) and the  Canceropôle Provence-Alpes-Côte d' Azur , the  French National Cancer Institute (INCa)  and the  Provence-Alpes-Côte d' Azur Region.
Availability of data and materials The circadian clock map introduced in this article is provided in the CellDesigner format in the Additional file 3 (Circa-dianClock. xml). The complete computational analysis, from the merge of the circadian clock and cell cycle maps to the qualitative model validation and mutation predictions, can be repeated using the notebooks and Docker image (based on the CoLoMoTo Docker distribution  [12]  ) available at https:// github. com/ paule ve/ Clock Cycle-noteb ooks and https:// doi. org/ 10. 5281/ zenodo. 47194 00. All analyses were run on a desktop computer with a 3. 6Ghz CPU and 256GB of RAM. Total execution time was four hours for all analyses but reachability of phase M (analysis B) which was computed by simulation in less than 24 hours, without parallelization.
The merge of the circadian clock and cell cycle maps has been performed with the csbgnpy Python library in version 0. 1, available at https:// github. com/ Adrie nroug ny/ csbgn py. The qualitative interpretation of the coupled map resulting in a formal dynamical model expressed in the autonama network framework has been performed with the sbgn2an Python library in version 0. 1, available at https:// github. com/ Adrie nroug ny/ sbgn2 an and the pypint Python library in version 1. 6. 1, available at https:// loicp auleve. name/ pint. The qualitative analysis, including model validation and mutation predictions, has been performed using the software Pint in version 2019-05-24, available at https:// loicp auleve. name/ pint.
Metabolic network, mapping of metabolic fluxes and experimental data Mesophilic growth conditions were studied by selecting a GSM for Escherichia coli (str. K-12 substr. MG1655): iJO1366, which has proven to predict phenotypes in a wide range of growth conditions  [34]. For the sake of consistency, metabolomics and fluxomics data were obtained from the same experiment (S1 Dataset and S1 Table)  [35]. Briefly, cells were grown in glucoselimited chemostats at 37˚C with minimal medium and a fixed specific growth rate (μ) of 0. 20 h -1. The experimental glucose uptake rate (2. 93 mmol gDCW -1 h -1 ) was used as a constraint, leaving the default lower and upper bounds for transport reactions. Maximisation of the biomass yield was selected as the objective function, and no flux value was forced through the biomass reactions (v biomass ).
Generation of experimental design The original matTFA toolbox uses unique values for t and I  [20] , and S is not taken into account (Table  1 ). To explore their potential impact in the predictive capabilities, a modified matTFA (mod-matTFA) allowing to consider alternative parameters values and methods was created (Table  2 ). For the sake of reproducibility  [36] , the complete list of files used in this study was collected in S2 Table, and are publicly available in Nottingham SBRC's GitHub profile (https://github. com/SBRCNottingham/Impact-of-Physicochemical-Parameters-onthermodynamics-based-FBA). Analyses were performed using the COBRA toolbox  [37]  in MATLAB R2016b with the solver CPLEX 12. 8. 0 to ensure compatibility. Since I affects the Gibbs energy of formation, an adjustment from the reference state (D f G o j ) was needed to obtain the standard transformed Gibbs energy of formation (D f G 0o j )  [32].
Assessment of fluxomics and metabolomics predictive capabilities Mesophilic growth conditions for E. coli were selected as a case study to explore the impact of metabolic and physiochemical constraints on the predictive capabilities of TFA at the fluxomics and metabolomics level. Accordingly, 64 different factor combinations (Table  2 ) were tested using mod-matTFA. It is important to note that not all test yielded a solution where cell growth was achieved (i. e. v biomass > 0 mmol gDCW -1 h -1 ). Since different factor combinations converged into the same set of solutions, tests were characterised at the fluxomics and metabolomics levels by considering either the full set of values, or the subset with an experimental counterpart. Results yielding feasible solutions were also compared against 13 C-MFA flux values (S1 Table ) and experimental metabolomics data (S1 Dataset), respectively.