We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.

SUMMARY: StochKit2 is the first major upgrade of the popular StochKit stochastic simulation software package. StochKit2 provides highly efficient implementations of several variants of Gillespie's stochastic simulation algorithm (SSA), and tau-leaping with automatic step size selection. StochKit2 features include automatic selection of the optimal SSA method based on model properties, event handling, and automatic parallelism on multicore architectures. The underlying structure of the code has been completely updated to provide a flexible framework for extending its functionality. AVAILABILITY: StochKit2 runs on Linux/Unix, Mac OS X and Windows. It is freely available under GPL version 3 and can be downloaded from http://sourceforge.net/projects/stochkit/. CONTACT: [email protected].

Collection size is an important feature to represent the content summaries of a collection, and plays a vital role in collection selection for distributed search. In uncooperative environments, collection size estimation algorithms are adopted to estimate the sizes of collections with their search interfaces. This paper proposes heterogeneous capture (HC) algorithm, in which the capture probabilities of documents are modeled with logistic regression. With heterogeneous capture probabilities, HC algorithm estimates collection size through conditional maximum likelihood. Experimental results on real web data show that our HC algorithm outperforms both multiple capture-recapture and capture history algorithms.