Sequencing DNA from several organisms has revealed that duplication and drift of existing genes have primarily molded the contents of a given genome. Though the effect of knocking out or overexpressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network evolvability, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or σ-factor genes in Escherichia coli, added over a wild-type genetic background.Here we show that ~95% of new networks are tolerated by the bacteria, that very few alter growth, and that expression level correlates with factor position in the wild-type network hierarchy. Most importantly, we find that certain networks consistently survive over the wild-type under various selection pressures. Therefore new links in the network are rarely a barrier for evolution and can even confer a fitness advantage.The Escherichia coli genome contains ~300 transcription factors (TFs)1,2, organized hierarchically, with few master regulators3-5 (Fig. 1). Only nine regulatory proteins (CRP, FNR, IHF, FIS, ArcA, NarL, H-NS, Fur, and Lrp) control over half of all genes, through direct and indirect interactions6,7. Lower-tier nodes are more sparsely connected and the network structure has a scale-free power-law degree distribution8,9. It has been argued that such networks are particularly robust to random errors since only a few nodes are highlyconnected hubs, whose perturbation would affect the network drastically10. This conclusion is based on the effects of deleting or overexpressing individual nodes. However, the addition of new interactions is thought to be an equally important process for evolution, and the network responses to such changes remain to be systematically explored.Genomes are molded by gene duplication, transfer, mutation and loss. Duplication occurs rapidly in all species11,12 and through mutation serves as material for innovation. This drives cellular network evolution13,14, even though relatively few duplications become fixed in populations11,12. We therefore chose to reconstruct events where an open reading frame (ORF) or gene is duplicated and subsequently becomes linked to a new regulatory input. Thus, promoter region-ORF fusions were constructed on high copy number plasmids Author Information Microarray data are MIAME-compliant and have been deposited at ArrayExpress http://www.ebi.ac.uk/ microarray-as/aer/entry, Accession: E-MEXP-732. Reprints and permissions information is available at npg.nature.com/ reprintsandpermissions. Correspondence and requests for materials should be addressed to M.I. (e-mail: isalan@crg.es and a subset were stably integrated in the E. coli chromosome. Although evolution is unlikely to take such a direct approach, except in rare cases such as gene fusions in chromosomal rearrangements, our approach provides a systematic way to sample the viability of new connectivity. By adding new connection...

The massive acquisition of data in molecular and cellular biology has led to the renaissance of an old topic: simulations of biological systems. Simulations, increasingly paired with experiments, are being successfully and routinely used by computational biologists to understand and predict the quantitative behaviour of complex systems, and to drive new experiments. Nevertheless, many experimentalists still consider simulations an esoteric discipline only for initiates. Suspicion towards simulations should dissipate as the limitations and advantages of their application are better appreciated, opening the door to their permanent adoption in everyday research.

Pattern formation is essential in the development of higher eukaryotes. For example, in the Drosophila embryo, maternal morphogen gradients establish gap gene expression domain patterning along the anterior-posterior axis, through linkage with an elaborate gene network. To understand the evolution and behaviour of such systems better, it is important to establish the minimal determinants required for patterning. We have therefore engineered artificial transcription-translation networks that generate simple patterns, crudely analogous to the Drosophila gap gene system. The Drosophila syncytium was modelled using DNA-coated paramagnetic beads fixed by magnets in an artificial chamber, forming a gene expression network. Transient expression domain patterns were generated using various levels of network connectivity. Generally, adding more transcription repression interactions increased the “sharpness” of the pattern while reducing overall expression levels. An accompanying computer model for our system allowed us to search for parameter sets compatible with patterning. While it is clear that the Drosophila embryo is far more complex than our simplified model, several features of interest emerge. For example, the model suggests that simple diffusion may be too rapid for Drosophila-scale patterning, implying that sublocalisation, or “trapping,” is required. Second, we find that for pattern formation to occur under the conditions of our in vitro reaction-diffusion system, the activator molecules must propagate faster than the inhibitors. Third, adding controlled protease degradation to the system stabilizes pattern formation over time. We have reconstituted transcriptional pattern formation from purified substances, including phage RNA polymerases, ribonucleotides, and an eukaryotic translation extract. We anticipate that the system described here will be generally applicable to the study of any biological network with a spatial component.

SmartCell has been developed to be a general framework for modelling and simulation of diffusion-reaction networks in a whole-cell context. It supports localisation and diffusion by using a mesoscopic stochastic reaction model. The SmartCell package can handle any cell geometry, considers different cell compartments, allows localisation of species, supports DNA transcription and translation, membrane diffusion and multistep reactions, as well as cell growth. Moreover, different temporal and spatial constraints can be applied to the model. A GUI interface that facilitates model making is also available. In this work we discuss limitations and advantages arising from the approach used in SmartCell and determine the impact of localisation on the behaviour of simple well-defined networks, previously analysed with differential equations. Our results show that this factor might play an important role in the response of networks and cannot be neglected in cell simulations.

Recent technological and theoretical advances are only now allowing the simulation of detailed kinetic models of biological systems that reflect the stochastic movement and reactivity of individual molecules within cellular compartments. The behavior of many systems could not be properly understood without this level of resolution, opening up new perspectives of using computer simulations to accelerate biological research. We review the modeling methodology applied to stochastic spatial models, also to the attention of non-expert potential users. Modeling choices, current limitations and perspectives of improvement of current general-purpose modeling/simulation platforms for biological systems are discussed.