Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli. The prediction accuracy is approximately 72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies. The algorithm could also correctly predict the change in solubility for most of the point mutations reported in literature. This algorithm can be a useful tool in screening protein libraries to identify soluble variants of proteins.

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Researchers in different subject areas are tackling similar questions that require a complex systems approach: Can we develop indicators that serve as warning signals for impending regime shifts or critical thresholds in the system behavior? What is the characteristic observation scale that allows for an optimal description of the system dynamics in space and time? How can the resilience (return time to equilibrium) and stability (resistance to external forcing) of a system subjected to disturbance regimes be quantified? Can we derive generalities on how natural and man-made systems develop in time? Amongst interacting units of the system, which ones are the keystones for its global functioning? In this context, recurrence plots are useful tools as they provide a common language for the study of complex systems [Webber, Jr. et al., 2009]. Furthermore, since the method does not require fitting a specific model to data, recurrence plots logically preserve a maximum of the information that is embedded in the sequence of observations. Recurrence plot based methods are also powerful visual and quantitative tools for pattern detection and, as such, continue to birth novel research hypotheses as well as challenge established ones. The ability of these methods to cope with nonlinear and transient behaviors, as well as observational noise is of particular interest to experimentalists and empiricists.The recurrence plot (RP) was introduced more than twenty years ago in order to visualize the dynamics of complex systems by their recurrences. The first natural extension was the quantification of small-scale structures in an RP by means of recurrence quantification analysis (RQA). RQA has turned out to be a powerful tool for distinguishing different types of dynamics, detecting dynamical transitions, or describing changes in the complexity of spatial objects [Marwan et al., 2007;Marwan, 2008].