A fully open source software program for automated two‐dimensional and one‐dimensional data reduction and preliminary analysis of isotropic small‐angle X‐ray scattering (SAXS) data is presented. The program is freely distributed, following the open‐source philosophy, and does not rely on any commercial software packages. BioXTAS RAW is a fully automated program that, via an online feature, reads raw two‐dimensional SAXS detector output files and processes and plots data as the data files are created during measurement sessions. The software handles all steps in the data reduction. This includes mask creation, radial averaging, error bar calculation, artifact removal, normalization and q calibration. Further data reduction such as background subtraction and absolute intensity scaling is fast and easy via the graphical user interface. BioXTAS RAW also provides preliminary analysis of one‐dimensional data in terms of the indirect Fourier transform using the objective Bayesian approach to obtain the pair‐distance distribution function, PDDF, and is thereby a free and open‐source alternative to existing PDDF estimation software. Apart from the TIFF input format, the program also accepts ASCII‐format input files and is currently compatible with one‐dimensional data files from SAXS beamlines at a number of synchrotron facilities. BioXTAS RAW is written in Python with C++ extensions.

This manuscript presents, for the first time, the method of automated structural analysis of biomolecules in solution on a microfluidic chip. A polymer-based micrototal analysis system for high-throughput Small-Angle X-ray Scattering (SAXS) data collection from biological macromolecules has been developed. The bioXTAS chip features an integrated X-ray transparent 200 nL sample chamber and diffusion-based mixing of protein and buffer solutions. Software for fully automated fluidic control, data acquisition, and data analysis has been developed. The proof-of concept is based on data using bovine serum albumin as the model system. It confirms the quality of SAXS data generated from small sample volumes and furthermore validates the on-chip mixing capabilities. SAXS data on the gradual unfolding of BSA induced by an anionic surfactant exemplifies how the bioXTAS chip can be used to follow and identify structural changes and proves the feasibility of high-throughput structural analysis in solution. In total, this shows that the bioXTAS chip has the potential for becoming a powerful tool for automated high-throughput structural analysis of macromolecular systems.

With the rise in popularity of biological small-angle X-ray scattering (BioSAXS) measurements, synchrotron beamlines are confronted with an ever-increasing number of samples from a wide range of solution conditions. To meet these demands, an increasing number of beamlines worldwide have begun to provide automated liquid-handling systems for sample loading. This article presents an automated sample-loading system for BioSAXS beamlines, which combines single-channel disposable-tip pipetting with a vacuum-enclosed temperaturecontrolled capillary flow cell. The design incorporates an easily changeable capillary to reduce the incidence of X-ray window fouling and cross contamination. Both the robot-control and the data-processing systems are written in Python. The data-processing code, RAW, has been enhanced with several new features to form a user-friendly BioSAXS pipeline for the robot. The flow cell also supports efficient manual loading and sample recovery. An effective rinse protocol for the sample cell is developed and tested. Fluid dynamics within the sample capillary reveals a vortex ring pattern of circulation that redistributes radiation-damaged material. Radiation damage is most severe in the boundary layer near the capillary surface. At typical flow speeds, capillaries below 2 mm in diameter are beginning to enter the Stokes (creeping flow) regime in which mixing due to oscillation is limited. Analysis within this regime shows that single-pass exposure and multiple-pass exposure of a sample plug are functionally the same with regard to exposed volume when plug motion reversal is slow. The robot was tested on three different beamlines at the Cornell High-Energy Synchrotron Source, with a variety of detectors and beam characteristics, and it has been used successfully in several published studies as well as in two introductory short courses on basic BioSAXS methods.

A small-angle X-ray scattering (SAXS) set-up has recently been developed at beamline I711 at the MAX II storage ring in Lund (Sweden). An overview of the required modifications is presented here together with a number of application examples. The accessible q range in a SAXS experiment is 0.009-0.3 A(-1) for the standard set-up but depends on the sample-to-detector distance, detector offset, beamstop size and wavelength. The SAXS camera has been designed to have a low background and has three collinear slit sets for collimating the incident beam. The standard beam size is about 0.37 mm x 0.37 mm (full width at half-maximum) at the sample position, with a flux of 4 x 10(10) photons s(-1) and lambda = 1.1 A. The vacuum is of the order of 0.05 mbar in the unbroken beam path from the first slits until the exit window in front of the detector. A large sample chamber with a number of lead-throughs allows different sample environments to be mounted. This station is used for measurements on weakly scattering proteins in solutions and also for colloids, polymers and other nanoscale structures. A special application supported by the beamline is the effort to establish a micro-fluidic sample environment for structural analysis of samples that are only available in limited quantities. Overall, this work demonstrates how a cost-effective SAXS station can be constructed on a multipurpose beamline.

A new microfluidic sample-preparation system is presented for the structural investigation of proteins using small-angle X-ray scattering (SAXS) at synchrotrons. The system includes hardware and software features for precise fluidic control, sample mixing by diffusion, automated X-ray exposure control, UV absorbance measurements and automated data analysis. As little as 15 ml of sample is required to perform a complete analysis cycle, including sample mixing, SAXS measurement, continuous UV absorbance measurements, and cleaning of the channels and X-ray cell with buffer. The complete analysis cycle can be performed in less than 3 min. Bovine serum albumin was used as a model protein to characterize the mixing efficiency and sample consumption of the system. The N2 fragment of an adaptor protein (p120-RasGAP) was used to demonstrate how the device can be used to survey the structural space of a protein by screening a wide set of conditions using high-throughput techniques.research papers

Glutaminase C is a key metabolic enzyme, which is unregulated in many cancer systems and believed to play a central role in the Warburg effect, whereby cancer cells undergo changes to an altered metabolic profile. A long-standing hypothesis links enzymatic activity to the protein oligomeric state, hence the study of the solution behavior in general and the oligomer state in particular of glutaminase C is important for the understanding of the mechanism of protein activation and inhibition. In this report, this is extensively investigated in correlation to enzyme concentration or phosphate level, using a high-throughput microfluidic-mixing chip for the SAXS data collection, and we confirm that the oligomeric state correlates with activity. The in-depth solution behavior analysis further reveals the structural behavior of flexible regions of the protein in the dimeric, tetrameric and octameric state and investigates the C-terminal influence on the enzyme solution behavior. Our data enable SAXS-based rigid body modeling of the full-length tetramer states, thereby presenting the first ever experimentally derived structural model of mitochondrial glutaminase C including the N- and C-termini of the enzyme.

A scenario‐based, multistage stochastic programming model is developed for the management of the Highland Lakes by the Lower Colorado River Authority (LCRA) in Central Texas. The model explicitly considers two objectives: (1) maximize the expected revenue from the sale of interruptible water while reliably maintaining firm water supply, and (2) maximize recreational benefits. Input data can be represented by a scenario tree, built empirically from a segment of the historical flow record. Thirty‐scenario instances of the model are solved using both a primal simplex method and Benders decomposition, and results show that the first‐stage (‘here and now’) decision of how much interruptible water to contract for the coming year is highly dependent on the initial (current) reservoir storage levels. Sensitivity analysis indicates that model results can be improved by using a scenario generation technique which better preserves the serial correlation of flows. Ultimately, it is hoped that use of the model will improve the LCRA's operational practices by helping to identify flexible policies that appropriately hedge against unfavorable inflow scenarios.

We develop four iterative algorithms for the solution of separable, convex nonlinear optimization problems with a single linear constraint and bounded variables. The design of the algorithms makes them suitable for implementation on massively parallel computers of the SIMD (i.e., Single Instruction, Multiple Data) class. The algorithms are specialized for the solution of network problems whereby the linear constraint reflects conservation of flow. Details of implementations on a Connection Machine CM-2 are reported. The numerical results indicate that all algorithms are very effective, and can solve very large problems. Three of the algorithms are also very efficient when implemented on the massively parallel system. Interestingly, the most effective algorithm (in number of steps required to solve the test problems) is the least efficient (in solution time) when implemented in parallel. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.