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.

According to Robert Putnam, trust can be a proxy for social capital. Thus, a higher societal trust could be related to economic growth. To test this hypothesis, we simulated the association between trust and economic growth in two artificial societies. One artificial society (New Zealand) exhibited higher levels of initial trust, and the other (Argentina) had lower levels of trust. Initial starting points for simulations were set using representative survey data (using the global trust inventory). Computational simulation relied on a rule-based model (RBM), integrating time through a stochastic simulation algorithm implemented in PISKaS. Agents in the artificial societies were distributed according to proportions of four trust profiles, with more high trusters (HTs) in New Zealand. In each iteration, the agents played a prisoner's dilemma, earning or losing money according to different payoff matrices, cooperation probabilities, and interaction frequencies, modeling different conditions for economic exchange. We analyzed the economic performance of each country, together with the performance of each trust profile. Results support the notion that societies with high trust perform economically better, on average, than those with low trust, but only if interaction frequency is held constant. Despite the relevance of HTs for economic development, their performance is tightly linked to the type of society in which they interact: they prosper more in a Rule of Law society, and where HTs are more common, compared with a predators' paradise, where the sucker's payoff is more punitive.