Resource Balance Analysis (RBA) is a computational method based on resource allocation, which performs accurate quantitative predictions of whole-cell states (i.e. growth rate, metabolic fluxes, abundances of molecular machines including enzymes) across growth conditions. We present an integrated workflow of RBA together with the Python package RBApy. RBApy builds bacterial RBA models from annotated genome-scale metabolic models by adding descriptions of cellular processes relevant for growth and maintenance. The package includes functions for model simulation and calibration and for interfacing to Escher maps and Proteomaps for visualization. We demonstrate that RBApy faithfully reproduces results obtained by a hand-curated and experimentally validated RBA model for Bacillus subtilis. We also present a calibrated RBA model of Escherichia coli generated from scratch, which obtained excellent fits to measured flux values and enzyme abundances. RBApy makes whole-cell modeling accessible for a wide range of bacterial wild-type and engineered strains, as illustrated with a CO2-fixing Escherichia coli strain.Availability: RBApy is available at /https://github.com/SysBioInra/RBApy, under the licence GNU GPL version 3, and runs on Linux, Mac and Windows distributions.On a standard laptop, the initial creation (including Uniprot querying) of the E. coli model took less than 30 seconds, and model updating through helper files took approximately 5 seconds ( Supplementary Table S3).This subpackage uses the libsbml, biopython and pandas libraries. RBApy.xml: maintaining models in XML formatRBApy.prerba is primarily designed to generate a minimal working RBA model, containing default processes such as translation and chaperoning. RBApy.xml stores these models in an XML-rba format that was designed to facilitate model extension, in particular by adding new macromolecular processes. The user may do this by changing the XML-rba files directly. Alternatively, RBApy.xml provides an Application Programming Interface (API) in which every XML-rba entity can be accessed through a Python class with identical name.This subpackage uses the lxml library. RBApy.core: running simulationsRBApy.core imports an XML-rba model and converts it into the final LP optimization problem, specified by sparse matrices as described in [11,12]. For a given medium composition, the solver solves a series of LP feasibility problem for different growth rates, and computes in fine the maximal possible growth rate, reaction fluxes and abundances of molecular machines at maximal growth rate. The optimization problem is solved by using the CPLEX Linear Programming solver (https://www.ibm.com/analytics/cplex-optimizer). The procedure has been optimized to return results in less than one minute on a standard laptop, even for large systems such as the RBA model of E. coli that contains 1807 metabolites, 2583 metabolic reactions and 3906 enzyme complexes ( Supplementary Table S3).This subpackage uses the scipy and cplex libraries. RBApy.estim: estimating parametersRB...

BackgroundHigh-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and methods such as bio-ontologies to extract and share valuable information. In parallel, the development of recent whole-cell models using a systemic cell description opened alternatives for data integration. Integrating a systemic cell description within a bio-ontology would help to progress in whole-cell data integration and modeling synergistically.ResultsWe present BiPON, an ontology integrating a multi-scale systemic representation of bacterial cellular processes. BiPON consists in of two sub-ontologies, bioBiPON and modelBiPON. bioBiPON organizes the systemic description of biological information while modelBiPON describes the mathematical models (including parameters) associated with biological processes. bioBiPON and modelBiPON are related using bridge rules on classes during automatic reasoning. Biological processes are thus automatically related to mathematical models. 37% of BiPON classes stem from different well-established bio-ontologies, while the others have been manually defined and curated. Currently, BiPON integrates the main processes involved in bacterial gene expression processes.ConclusionsBiPON is a proof of concept of the way to combine formally systems biology and bio-ontology. The knowledge formalization is highly flexible and generic. Most of the known cellular processes, new participants or new mathematical models could be inserted in BiPON. Altogether, BiPON opens up promising perspectives for knowledge integration and sharing and can be used by biologists, systems and computational biologists, and the emerging community of whole-cell modeling.Electronic supplementary materialThe online version of this article (10.1186/s13326-017-0165-6) contains supplementary material, which is available to authorized users.

Detailed whole-cell modeling requires an integration of heterogeneous cell processes having different modeling formalisms, for which whole-cell simulation could remain tractable. Here, we introduce BiPSim, an open-source stochastic simulator of template-based polymerization processes, such as replication, transcription and translation. BiPSim combines an efficient abstract representation of reactions and a constant-time implementation of the Gillespie’s Stochastic Simulation Algorithm (SSA) with respect to reactions, which makes it highly efficient to simulate large-scale polymerization processes stochastically. Moreover, multi-level descriptions of polymerization processes can be handled simultaneously, allowing the user to tune a trade-off between simulation speed and model granularity. We evaluated the performance of BiPSim by simulating genome-wide gene expression in bacteria for multiple levels of granularity. Finally, since no cell-type specific information is hard-coded in the simulator, models can easily be adapted to other organismal species. We expect that BiPSim should open new perspectives for the genome-wide simulation of stochastic phenomena in biology.