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Atlas: automatic modeling of regulation of bacterial gene expression and metabolism using rule-based languages

Abstract Motivation Cells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes. Results We developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria. Availability and implementation Code, models and tutorials are available online (https://github.com/networkbiolab/atlas). Supplementary information Supplementary data are available at Bioinformatics online.


Introduction
Recent technological advances have enabled the inquiry into, and understanding of, biological systems at an unprecedented level of detail (e.g. Regev et al., 2017). From such developments, the impact of stochastic dynamics in living systems has been corroborated, measured and modeled (Raj and van Oudenaarden, 2008). To date, most of the available models look for the reproduction of cell metabolism at the genome scale using constraint-based models (Szigeti et al., 2018). However, constraint-based models disregard the dynamic and stochastic nature of metabolism (Costa et al., 2016) and the prediction of the impact of genetic modifications remains challenging [e.g. Foster et al. (2019) or Long and Antoniewicz (2019)]. Dynamic modeling of metabolism has been proposed to circumvent the drawbacks of constraint-based models (e.g. Hädicke and Klamt, 2017) despite specific issues, such as the need for calibration, extensive validation, and showing a time-consuming development. Also, it is necessary to consider that metabolism is only one aspect of cellular behavior, and models are desired that encompass all cellular processes (Karr et al., 2012(Karr et al., , 2015Sanghvi et al., 2013). If available, those models would help create an understanding of complex cell dynamics, with applications in biotechnology or biomedicine (Carrera and Covert, 2015), e.g. designing minimal cells (Rees-Garbutt et al., 2020) or synthetic genomes (Fredens et al., 2019). Although there are available whole-cell models for Mycoplasma genitalium (Karr et al., 2012) and recently for Escherichia coli (Macklin et al., 2020) and there is a proposed pathway to develop integrative and larger models (Covert et al., 2008;Szigeti et al., 2018), whole-cell models are still not widely developed or adopted (Szigeti et al., 2018). activity of transcription factors (Covert et al., 2008). These processes interplay in networks of molecular interactions between DNA, RNA, proteins and metabolites (Grimbs et al., 2019;Herná ndez-Prieto et al., 2014). Here, we aimed to perform the integrative modeling of transcription, translation, regulation of gene expression, metabolism and genome architecture, which is considered a prototype whole-cell model (Szigeti et al., 2018). We developed Atlas, a software that facilitates the dynamic modeling of gene regulation and bacterial metabolism by using biological networks to develop rulebased models (RBMs) employing the PySB framework (Lopez et al., 2013) for later simulation, curation and analysis. The developed software takes inspiration from available tools that automate the reconstruction of draft constraint-based models, such as Merlin (Dias et al., 2015), RAVEN (Agren et al., 2013;Wang et al., 2018), ModelSEED (Henry et al., 2010), KBase (Arkin et al., 2018) and other software (reviewed in Faria et al., 2018).
An RBM employs an abstract language very similar to chemical equations capable of encoding millions of individual reactions (Danos et al., 2007) depending on the strictness of rule definitions. Further, we chose to develop RBMs because of their modularity; also, they allow deterministic simulations through network generation (Blinov et al., 2004(Blinov et al., , 2006Hlavacek et al., 2006), do not require the modeling of mass balances for all molecular species (network-free simulations) (Sneddon et al., 2011), and permit stochastic simulations employing Gillespie's Stochastic Simulation Algorithm (Gillespie, 1976) or modifications (Danos et al., 2007(Danos et al., , 2008Sneddon et al., 2011). In addition to the mentioned features, RBMs are more readable than their counterparts, such as ODEbased models, which makes RBMs easier to review, inspect and correct collaboratively using version control tools, e.g. Git (Perez-Riverol et al., 2016). Finally, rule-based languages were used previously to model automatically signaling pathways, e.g. with the software INDRA (Gyori et al., 2017) and KAMI (Harmer et al., 2019).
Data were formatted as biological networks. For instance, genes are connected to their regulators in a canonical gene regulatory network (GRN) and we modified the network to connect transcription factors to DNA-binding sites and RNA polymerase-sigma factors (RNAP-r) to promoters to obtain a sigma-specificity network. In the case of metabolic networks, we employed tripartite networks in which a reaction connects to the associated enzyme and metabolite(s) instead of the more common bipartite representation of reactions and metabolites. Finally, three types of interaction networks [protein-protein, protein-DNA binding sites (a GRN) and proteinmetabolite] were formatted as collapsed hyper-graphs (Klamt et al., 2009) to encode complexes, i.e. networks where (a group of) nodes connect to a group of nodes. We employed brackets to denote complexes, e.g. ' [crp, crp]

Natural and synthetic GRNs used as examples
Natural GRNs representing data from E.coli were employed as examples and include the lactose, arabinose and fucose degradation operons (LacI, AraC and FucR regulons), the central carbon metabolism (Millard et al., 2017) and all E.coli transporters and enzymes from the BioCyc database (Karp et al., 2018b;Keseler et al., 2017). In addition, we employed the regulation of gene expression for the E.coli sigma factors (sigma factors model) (Perez-Acle et al., 2018). Primary data were completed with available information on genome architecture from Cho et al. (2009) and sigma factor specificity from Cho et al. (2014).
We modified the sigma factors model (Perez-Acle et al., 2018) to exemplify the modeling of synthetic designs prior to experimentation. These modifications include the knockout of each sigma factor modeled and the incorporation of a promoter and/or a terminator to modify the rpoBC operon (Cho et al., 2014). The two types of in silico modifications were made modifying the genome graph used as input for Atlas, adding a promoter or a terminator between the rpoB coding DNA sequence (CDS) and the rpoC ribosome binding site (RBS) or removing the CDS of each sigma factor preserving the natural promoters, RBS and terminators. In the case of the insertion of an rpoC promoter, the GRN was modified to incorporate the RNAP-r specificity of the rpoB promoter.

Draft, simulation, curation and analysis of RBMs
Draft sub-models were obtained using biological networks as input and combined later in a divide-and-conquer modeling strategy. These ensemble models were employed for simulation, curation and calibration.
Models were simulated with the PySB interfaces for the SciPy ODE integrator (Virtanen et al., 2020) and the Kappa Simulator v4.0 (KaSim) (Boutillier et al., 2018). The ODE integration requires the enumeration of all components and individual reactions (network generation) (Blinov et al., 2004(Blinov et al., , 2006Hlavacek et al., 2006). In any situation in which the network generation procedure took excessive time to finalize (set as a 5-min threshold), network-based simulations were replaced by network-free simulations employing KaSim.
Models were exported to the kappa language and analyzed with the Kappa Static Analyzer (KaSA) from the Kappa platform (Boutillier et al., 2018) to perform reachability analysis (Danos et al., 2008;Feret, 2007) after their reconstruction or any manual curation. In brief, RBMs describe a network of reactions, some of which could be dead rules due to the unavailability of preceding rules that synthesize reactants in the required form. Curation of the data was carried out manually, for instance, to remove duplications (e.g. gene products with two identical reactions but different metabolite names), ambiguities (e.g. names referring to a family of metabolites), lack of compartmental information [e.g. transport reactions in which substrate(s) and product(s) are the same metabolite but are located in different compartments], incorrect stoichiometry of reaction per enzymatic complex, missing gene regulations and others.
In the case of the sigma factor model and its in silico genetic modifications, we performed the following analysis. The dynamics of 1000 stochastic simulations for 100 units of time performed with KaSim were contrasted by employing the software edgeR (Chen et al., 2014;Robinson et al., 2010). Simulations were carried out with arbitrary rates at one event per unit time (also arbitrary). In the case of the addition of a promoter and/or a terminator to modify the rpoBC operon, the three resulting models were subject to calibration with the transcriptomics data of cold stress from Jozefczuk et al. (2010), GEO accession GSE20305, assuming the new networks describe the correct genome architecture. We calibrated the binding and unbinding rates of the RNAP-r complexes to promoters and the RNA decay rates of the new models and the reference model by employing the software Pleione and the described strategy 3 with the v 2 fitness function (Santibáñez et al., 2019): 100 iterations, 100 models per iteration, selecting two models to recombine with a probability inverse to the ranking (see the Supplementary Material online for more details). After calibration, co-expression networks were constructed with ExpressionCorrelation (http://www.baderlab.org/Software/ ExpressionCorrelation) for the average values of 1000 simulations. The ExpressionCorrelation employs Pearson's correlation coefficient and we selected absolute values higher than 0.95 for visualization.
The co-expression and other networks were visualized with the software Cytoscape v3.7.2 (Shannon et al., 2003;Su et al., 2014) and models were visualized within Jupyter notebooks with the software pyViPR (Ortega and Lopez, 2020).

Software overview and basic workflow
An overview of the Atlas software is depicted in Figure 1. The atlas module has functions to reconstruct independent models from genome graphs and protein-protein, protein-metabolites and protein-DNA interaction and metabolic networks. In addition, a specialized function could simultaneously employ data from the genome graph and from a sigma-specificity network (RNAPr-promoters interaction network) to produce a model of bacterial regulation of gene expression. As models are independent, the module also provides a function to combine them, and functions to add regulatory relationships to gene expression rules; to get, remove, modify and add rules; and remove and get the current value of a parameter. After reconstruction, models require to set their parameters (if they were not provided as metadata in the networks) and to define the initial condition. The user of Atlas could choose from a variety of simulators and, finally, plot the results of simulations. In the case of stochastic simulations, the results include every simulation along with the mean and standard deviation. The user could export the model at Fig. 1. Overview of the Atlas software and a typical workflow from gathering data to plot simulation results. (Left) Atlas is a python3 software divided into four modules. The atlas module has functions to reconstruct RBMs from biological networks in plain text. The utils module has functions to read and check networks (uniqueness of reactions and uniqueness of interactions), analyze the models produced by Atlas with KaSA and get information from locally installed BioCyc databases with the help of the PathwayTools software. The simulation module has functions to set parameters, observables and initials, simulate the model with a variety of software and plot the results. Finally, the export module has functions to export the model through any supported format in the PySB framework. Functions that require external software are highlighted: PathwayTools (green), Docker (blue) and BioNetGen/KappaTools (yellow). (Right) A typical workflow is divided into the following steps: (i) review and compile data from the literature using a spreadsheet software, (ii) obtain protein complex composition from PathwayTools with 'utils.interactionNetwork.FromGeneList', (iii) obtain metabolic data from PathwayTools with 'utils.metabolicNetwork.FromGeneList' or 'utils.metabolicNetwork.FromEnzymeList', (iv) expand the metabolic network (enzymes, substrates, products) to a source-target format for visualization, (v) reconstruct models matching the type of network and, optionally, add regulatory interactions of protein-DNA interactions to gene expression rules or correct rules if necessary, (vi) combine sub-models, (vii) set parameters, observables and the initial condition of model components, (viii) simulate the model and (ix) plot variables. Optionally (Step 10), export the model for simulation, analysis, or curation with external tools. Light blue denotes a manual step. A.U., arbitrary unit any stage in a variety of formats, and employ external tools to simulate, curate or analyze the reconstructed model. Complementarily, Atlas provides utilitarian functions that are able to read and check the different networks, analyze the connectivity of the model and obtain data from the BioCyc databases (Caspi et al., 2016;Karp et al., 2018a). Data could be transformed and exported for visualization with Cytoscape (Shannon et al., 2003;Su et al., 2014) and models could be visualized within Jupyter notebooks with pyViPR (Ortega and Lopez, 2020).
An important note is the definition of the different components of the model (or agents). We defined five distinct agents: Proteins ('prot'), Metabolites ('met'), DNA ('dna'), RNA ('rna') and Complexes ('cplx'). A 'Complex' agent is an alias for complexes, such as the RNAP or the bacterial ribosome. All agents have a 'name' and a 'location' site for identification purposes. In addition, all components have interaction sites named 'dna', 'met', 'prot' and 'rna' that allow interaction with another agent of the matching type. The DNA and RNA agents have an additional identification site called 'type' to define their nature: promoter, RBS, CDS, terminator or binding site. Finally, proteins, DNA and RNA agents have two sites, named 'up' and 'dw', that enable the automated description of complexes of the same type. Two proteins interact in their 'up' and 'dw' sites because the unique 'prot' site per agent allows only dimerization. Following the definition of agents, Atlas is capable of writing complexes of any size and determining the correct internal links of components. See the Supplementary Material online for more details.

The lactose operon: modeling regulation of gene expression, transcription, translation and metabolism
We modeled a variety of metabolic networks of different sizes and complexities. The lactose model is composed of three enzyme-coding genes and one regulator, the arabinose-fucose model is composed of 13 enzyme-coding genes and two regulators, the E.coli central carbon metabolism model is composed of 200 enzyme-coding genes and the genome-scale metabolic model of E.coli is composed of 3596 transport and enzymatic reactions. To highlight the capabilities of Atlas, we describe in detail the modeling of the lactose metabolism because it is a common model of gene regulation with more than 50 years of biochemical information (Lewis, 2011).
The lactose operon from E.coli consists of three genes: the b-galactosidase gene lacZ, the lactose permease gene lacY (also known as lactose-proton symporter) and the galactoside O-acetyltransferase gene lacA. The EcoCyc database indicates that LacY can incorporate a-lactose, melibiose, lactulose, 3-O-galactosylarabinose and melibionate into the cell cytoplasm. Interestingly, the common colorimetric substrate ONPG (o-nitrophenyl-b-galactoside) is mentioned in the description for the lactose transport, but there is no inclusion of the reaction for LacY. Next, LacZ could metabolize lactose into b-galactose and glucose, lactulose into b-galactose and fructofuranose, and 3-O-galactosylarabinose into b-galactose and arabinose. Data from the literature (e.g. Huber et al., 1981;Juers et al., 2012) were used to complete the data derived from the EcoCyc database and were added manually to the network (Supplementary Tables S1 and S2 and Fig. S1) and the final network is depicted in Figure 2A (labels were omitted for visualization purposes). The modeling of similar corrections for other enzymes could be useful to understand the dynamic properties of metabolic pathways before experimental validation of the kinetics properties of each enzyme. In the case of lactose degradation, simulations of the curated metabolic network are shown in Figure 2B for the two anomers of glucose, galactose and allolactose produced from a source of 100 molecules (or an arbitrary concentration unit) of b-lactose. As expected, the degradation of lactose into glucose and galactose is complete, while mutarotation allows for the equilibrium of anomers. Although sugar mutarotations are very slow reactions, they are spontaneous and we included, in Atlas, the capacity to model non-enzymatic reactions as EcoCyc reports 145 'spontaneous' and three transport reactions without an identified gene.
Once we curated the metabolic network, we modeled the protein-protein interaction network that connects gene expression to the metabolism for reactions performed by protein complexes (Supplementary Fig. S2 and Table S4). For the E.coli lactose metabolism, the b-galactosidase is a homotetramer, the galactoside O-acetyltransferase is a homotrimer and the lactose-proton symporter acts as a monomer. We employed the collapsed hyper-network representation to describe the protein-protein interactions from the literature or assumptions and automated the modeling of assembly processes. For instance, the assembly process for the b-galactosidase tetramer comes from dimers (Matsuura et al., 2011), and we supposed the existence of a galactoside O-acetyltransferase dimer as pre-complex (Fowler et al., 1985). Additionally, we took into consideration the reaction stoichiometry for each enzymatic complex. In this curation step, we identified whether reactions could happen independent of complex assembly (i.e. monomers are catalytically active) or if the protein complex is necessary for the catalytic activity in vivo (i.e. monomers are inactive). For the b-galactosidase complex, each subunit is catalytically active only when the tetramer is assembled (Li et al., 2018). Similarly, the galactoside O-acetyltransferase active sites act independently of each other and because they are formed with residues from two adjacent monomers (Lewendon et al., 1995;Wang et al., 2002), the trimer was assumed to be the only active catalytic form. Figure 2C shows deterministic and stochastic simulations for an RBM including the assembly of protein complexes and the metabolic reactions. Interestingly, the deterministic and stochastic simulations disagree at the beginning of the dynamics, although they reached a similar steady-state. Because the stochastic simulation requires the assembly of enzyme complexes before performing any metabolic reaction, simulations showed a lagphase, which is in contrast to the deterministic simulation. Next, the model was coupled to a representation for transcription and translation in addition to the activity of transcription factors. We employed the Kappa BioBrick Framework (KBF) (Stewart and Wilson-Kanamori, 2011) and automated the modeling of rules describing bacterial transcription and translation. The KBF describes transcription and translation as a succession of rules. These rules describe the reversible docking of RNAP (ribosome) to a promoter (RBS), the sliding of the RNAP through the DNA and sliding of the ribosome through the RNA, and fall off from the terminator (RNAP) or the stop codon (ribosome). Atlas considers all promoters and terminators to write the rules described in the KBF. The transcription from the lactose operon is initiated at four promoters and terminated by two Rho-independent terminators (Fig. 3). Moreover, we modeled an internal promoter that drives transcription from the lacYA operon, although its importance in vivo is not clear (Zaslaver et al., 2006). Employing the rules defined in KBF, we reconstructed a model for transcription and translation that considers the genomic architecture of the lactose operons and coupled it to the metabolic model presented previously (including protein assembly). Therefore, the resulting ensemble model requires only DNA (and the transcription/translation machinery) to produce the necessary proteins for metabolic activity.
Finally, the RBM representing the lactose metabolism was completed with a representation of transcriptional control. To model gene regulation, we added to the interaction network the LacIallolactose and LacI-DNA binding site complexes. In the case of LacI, each dimer binds in tandem to one DNA-binding site, and two dimers could dimerize, forming a DNA loop that impedes the binding of RNAP-r to promoters or initiates transcription (Rutkauskas et al., 2009). To inactivate LacI, free proteins bind allolactose, which seems to impede the binding of LacI-allolactose to DNAbinding sites (Lewis, 2005). In principle, the modeling of DNAbinding protein interactions requires one rule per transcription factor, as we could ignore differences in the rates of DNA-protein kinetics. However, the different affinities, the genomic architecture and the transcription factor mechanisms [reviewed in van Hijum et al. (2009)] encouraged the development of another approach. To do so, overlapping DNA-binding sites and other genomic features (represented in Fig. 3) were defined as a collapsed hyper-network similar to what was done for protein complexes (Supplementary  Table S5). Figure 2D shows the results of simulations in which allolactose binds free lacI proteins, while Fig. 2E shows the simulation from a hypothetical situation in which free lacI proteins cannot bind allolactose. The difference between both situations was modest and showed an earlier rise of the glucose and galactose concentration around 100 units of time when allolactose could bind lacI proteins ( Supplementary Fig. S3). Because allolactose binds free lacI proteins, the release of lacI proteins freeing the promoter occurred in both models.
Although system parameters could be found in databases or calibrated [e.g. with pyBioNetFit (Mitra et al., 2019) or Pleione (Santibáñez et al., 2019)], the results show that modeling of RBMs for metabolism, protein complex assembly, transcription, translation and regulation of gene expression can be done in an automated manner, facilitating deterministic and stochastic simulation. Parameters employed for simulation are detailed in Supplementary Tables S2-S5 and a benchmark is detailed in Supplementary Table  S6.

Modeling natural and synthetic transcriptional control: the sigma factors model
We later addressed the modeling of RNAP-r assembly and transcriptional control of its expression mediated by the activity of sigma factors. Compared to eukaryotes, bacteria have only one RNAP and different sigma factors that confer promoter specificity (Mauri and Klumpp, 2014). The bacterium E.coli has seven sigma factors that interact physically with the core RNAP to form holoenzymes. The purpose here is to present how to model transcription control as the RBM is presented and calibrated elsewhere (Perez-Acle et al., 2018;Santibáñez et al., 2019) and to employ it to model synthetic transcriptional control. Also, Atlas models a molecular step in bacterial transcription disregarded in KBF (Stewart and Wilson-Kanamori, 2011): the sigma factor is released from holoenzymes when transcription is initiated (Mauri and Klumpp, 2014).
We modeled the holoenzymes binding to promoters as if those interactions were the binding of any transcription factor to their cognate DNA-binding sites. To do so, we considered the RNAP-r specificity (Supplementary Table S7) and the genome architecture (Supplementary Table S8) simultaneously, two features that we employed separately for the modeling of DNA-protein interactions and transcription. Both networks are represented in Figure 4A (a canonical GRN) and B (an extended network to show the considered genome architecture). The resulting model describes holoenzymes explicitly as a complex of five proteins instead of a unique agent modeling the RNAP complex employed in the lactose model. Results for the dynamics of the described GRN are shown in Figure 4C and D for a hypothetical case of only RNA synthesis without mRNA degradation. It can be seen that gene expression shows The use of Atlas is not restricted to natural networks and allows for the modeling of a different genomic arrangement of genes. One purpose of such a procedure is to assess differences in mRNA and other cell component dynamics with the final goal of computational-aided design before experimental evaluation. For the sigma model, we modeled three variants that modified the rpoBC operon architecture. Those variants are: (i) the incorporation of an internal promoter between rpoB and rpoC genes allowing for the interaction of an RNAP-r complex, (ii) the incorporation of an internal terminator allowing for the falloff of the RNAP and (iii) the incorporation of both. Our simulations showed that the incorporation of a promoter for rpoC reduced the synthesis rate for rpoB due to reduced RNAP availability for its promoter (Fig. 4E). This, in turn, is determined by the model parameters and initial condition. On the other hand, the addition of the internal terminator reduced the synthesis rate for rpoC (Fig. 4F) due to a reduced probability of continuing RNA elongation from rpoB into rpoC. Finally, simultaneous modifications showed no changes in RNA synthesis rates due to compensation of falling off RNAPs from the rpoB terminator and the interaction of RNAP-r holoenzymes to the synthetic rpoC promoter ( Supplementary Fig. S4B), and showed similar expression rates as the situation of independent rpoB and rpoC operons ( Supplementary Fig. S4D).
More realistic stochastic simulations were performed with the sigma factors model extended to model RNA degradation as unimolecular decay (Perez-Acle et al., 2018). In contrast to the simulation results shown in Figure 4E and F, and Supplementary Figure S4, the in silico variants were calibrated as if the new models represent the natural genomic architectures. Also, we performed an indirect comparison of mRNA quantities using Pearson's correlation coefficient to compare mRNA dynamics for the average of simulations. We report correlations higher than 0.95 as the absolute value in a co-expression network ( Supplementary Fig. S5). The expression profiles for rpoB and rpoC remained correlated in all variants, in contrast to a correlation coefficient of 0.57 determined from the original data. A complete explanation is tailored to the ability of the performed calibration to find parameter values that most closely resemble the experimental data for an unnatural transcriptional network and delineates the need for (cell-free) experiments to accurately measure RNA synthesis rates in modified genomic contexts.
Finally, we performed in silico knockout experiments. Comparisons employing the edgeR software (Chen et al., 2014;Robinson et al., 2010) and a threshold for the false discovery rate (FDR) of 0.05 showed that the deletion of rpoD and rpoS had the most impact in mRNA synthesis, while the other deletions did not show differential expressed genes. The knockout of rpoD impedes the expression of rpoS (Fig. 4B) while we observed lower expression for fliA and fecI and higher expression for rpoA, rpoE, rpoH and rpoN compared to the reference model. In turn, the knockout of rpoS showed lower expression for rpoB and fecI and higher expression for fliA. The determined fold change and FDR values are presented in Supplementary Tables S9 and S10, respectively. However, simulations were done to highlight the capability of Atlas to model different genetic modifications and parameters did not reflect any experimentally determined rate. Also, the models did not incorporate degradation rules for mRNAs, and an extension of the model to synthesize and degrade proteins will allow for the detailed modeling of in silico designs and the comparison of simulations to

Conclusion
Mathematical and computational modeling is often viewed as a specialized task. To facilitate modeling, we automated the development of RBMs, as these types of models show simulation flexibility, a reasonable degree of readability, modularity for integrative modeling and good simulation scalability.
Atlas produces sub-models from genome graphs, and proteinprotein, protein-metabolites and protein-DNA interaction and metabolic networks. We developed, in this work, a divide-andconquer strategy supported by the modularity of RBMs, as it is the pathway for the development of whole-cell models (Szigeti et al., 2018). The software produces RBMs for the PySB framework (Lopez et al., 2013) and rules can be added in any order while PySB checks on whether new rules are compatible with the current model. In addition, PySB could export to kappa language and we employed the KaSA software (Boutillier et al., 2018) to further assess the coherence of the developed RBMs. Simulation of RBMs could be done within PySB and calibration of exported models could be performed with pyBioNetFit (Mitra et al., 2019, only BNGL models) or Pleione (Santibáñez et al., 2019, BNGL and kappa models) to compare the reconstructed models with experimental data or available models.
Atlas contrasts with available software because it lacks a graphical interface [e.g. RuleBender (Smith et al., 2012) and VirtualCell (Blinov et al., 2017)], although the user could employ Atlas within a Jupyter notebook and use pyViPR (Ortega and Lopez, 2020) to visualize the model structure. Also, Atlas relies on the user to obtain formatted data to model interactions, in contrast to INDRA (Gyori et al., 2017), which can use natural language processing to read information and reconstruct models. In turn, Atlas can model metabolism, transcription and translation, as well as widespread proteinprotein interactions found in signaling pathways that INDRA (Gyori et al., 2017) and KAMI (Harmer et al., 2019) can model.
Finally, the models and the Atlas software are extensible, for instance, to model cooperative behavior not currently supported. The utilization of the law of mass action for the metabolic network (and other reactions) limits the utility of the resulting RBMs in the current form, but exporting to BNGL or kappa leverages this imposition, as they support mathematical expressions as reaction rates. However, we expect to extend Atlas to consider enzyme-metabolite interactions and describe the detailed mechanisms of enzyme reactions (Saa and Nielsen, 2017) and allosteric regulations of metabolic activity, as well as to model the assembly of ribosomes (Davis et al., 2016;Gupta and Culver, 2014;Shajani et al., 2011). Notably, Atlas is already compatible with metabolic and interaction data from eukaryotes and we obtained a model from data for 1991 metabolic reactions of the yeast Saccharomyces cerevisiae from BioCyc. In addition, we expect further interoperability with INDRA models of signaling pathways to model protein modifications, such as phosphorylation and the collaboration from researchers. With collaboration in mind, we shared the developed models in this work at https://github.com/networkbiolab/atlas/tree/master/examples.