Noninherited antibiotic resistance is a phenomenon whereby a subpopulation of genetically identical bacteria displays phenotypic tolerance to antibiotics. We show here that compared to Escherichia coli, the clinically relevant genus Burkholderia displays much higher levels of cells that tolerate ceftazidime. By measuring the dynamics of the formation of drug-tolerant cells under conditions that mimic in vivo infections, we show that in Burkholderia bacteria, oxygen levels affect the formation of these cells. The drug-tolerant cells are characterized by an anaerobic metabolic signature and can be eliminated by oxygenating the system or adding nitrate. The transcriptome profile suggests that these cells are not dormant persister cells and are likely to be drug tolerant as a consequence of the upregulation of anaerobic nitrate respiration, efflux pumps, β-lactamases, and stress response proteins. These findings have important implications for the treatment of chronic bacterial infections and the methodologies and conditions that are used to study drug-tolerant and persister cells in vitro.

We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman’s Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. Insilico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks.

We study regulatory networks of N genes giving rise to a vector expression profile v(t) in which each gene is Boolean; either on or off at any time. We require a network to produce a particular time sequence v(t) for t ∈ 1, ..., T and parameterize the complexity of such a genetic function by its duration T . We establish a number of new results regarding how functional complexity constrains genetic regulatory networks and their evolution. We find that the number of networks which generate a function decreases approximately exponentially with its complexity T and show there is a corresponding weakening of the robustness of those networks to mutations. These results suggest a limit on the functional complexity T of typical networks that is polynomial in N. However, we are also able to prove the existence of a, presumably small, class of networks in which this scales exponentially with N. We demonstrate that an increase in functional complexity T drives what we describe as a metagraph disintegration effect, breaking up the space of networks previously connected by neutral mutations and contrast this with what is found with less restrictive definitions of functionality. Our findings show how functional complexity could be a factor in shaping the evolutionary landscape and how the evolutionary history of a species constrains its future functionality. Finally we extend our analysis to functions with more exotic topologies in expression space, including "stars" and "trees". We quantify how the properties of networks that give rise to these functions differ from those that produce linear functional paths with the same overall duration T .