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.

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

“…However, with a sufficiently simple model, network-based simulation can be performed far more efficiently using BNGL, keeping in mind that more complex models will have a higher runtime with network-based simulation (Sneddon et al, 2011). When generating large-scale models, the type of RB model simulation can be scheduled on a case-by-case basis (Santibáñez et al, 2020).…”

“…Compared to the ODE-based paradigm, which represents the molecular system as concentrations of molecular species and focuses on their reaction kinetics, RB modelling is an agent-centred method in which the distribution of molecular compositions can be studied along with their abundance (Faeder et al, 2003; Blinov et al, 2004; Danos and Laneve, 2004). The potential of the RB paradigm has been extensively discussed (Danos, 2007; Chylek et al, 2013, 2014, 2015) and demonstrated with examples (Danos, 2007) or with models answering novel biological questions across signalling, regulatory and metabolic networks (Wilson-Kanamori et al 2015; Antunes et al 2016; Di Camillo et al 2016; Santibáñez et al 2020; Chattaraj et al 2021; Nosbisch et al 2022; see reviews for earlier models published before 2006 - Hlavacek et al 2006; and from 2007 to 2013 - Chylek et al 2014). These models are often based on chemical reaction networks previously developed for ODEs.…”

merlinv4.0: an updated platform for the reconstruction of high-quality genome-scale metabolic models

“…Since its launch in 2016, merlin's website was visited more than eleven thousand times and over three thousand downloads were performed. Merlin has gained notoriety over the years and is nowadays recognised as a reference software for the reconstruction of high-quality GSMM by the scientific community through multiple research articles (54)(55)(56)(57)(58)(59)(60)(61)(62), reviews, and book chapters (1,4,(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76)(77)(78)(79)(80).…”

Construction of a tunable promoter library to optimize gene expression in Methylomonas sp. DH-1, a methanotroph, and its application to cadaverine production

“…In addition, the non-optimized expression of enzyme genes may facilitate the accumulation of toxic intermediate metabolites, which decreases the production yield. To resolve this problem, promoters with different strengths are widely employed to fine-tune the expression levels of enzyme genes [ 8 , 9 ].…”