MOTIVATION: Many biochemical systems require stochastic descriptions. Unfortunately these can only be solved for the simplest cases and their direct simulation can become prohibitively expensive, precluding thorough analysis. As an alternative, moment closure approximation methods generate equations for the time-evolution of the system's moments and apply a closure ansatz to obtain a closed set of differential equations; that can become the basis for the deterministic analysis of the moments of the outputs of stochastic systems. RESULTS: We present a free, user-friendly tool implementing an efficient moment expansion approximation with parametric closures that integrates well with the IPython interactive environment. Our package enables the analysis of complex stochastic systems without any constraints on the number of species and moments studied and the type of rate laws in the system. In addition to the approximation method our package provides numerous tools to help non-expert users in stochastic analysis. AVAILABILITY AND IMPLEMENTATION: https://github.com/theosysbio/means CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A hybrid imaging system for simultaneous fluorescence tomography and X-ray computed tomography (XCT) of small animals has been developed and presented. The system capitalizes on the imaging power of a 360 ( degrees )-projection free-space fluorescence tomography system, implemented within a microcomputed tomography scanner. Image acquisition is based on techniques that automatically adjust a series of imaging parameters to offer a high dynamic range dataset. Image segmentation further allows the incorporation of structural priors in the optical reconstruction problem to improve the imaging performance. The functional system characteristics are showcased, and images from a brain imaging study are shown, which are reconstructed using XCT-derived priors into the optical forward problem.