Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets

Motivation: The combination of liquid chromatography and mass spectrometry (LC/MS) has been widely used for large-scale comparative studies in systems biology, including proteomics, glycomics and metabolomics. In almost all experimental design, it is necessary to compare chromatograms across biological or technical replicates and across sample groups. Central to this is the peak alignment step, which is one of the most important but challenging preprocessing steps. Existing alignment tools do not take into account the structural dependencies between related peaks that co-elute and are derived from the same metabolite or peptide. We propose a direct matching peak alignment method for LC/MS data that incorporates related peaks information (within each LC/MS run) and investigate its effect on alignment performance (across runs). The groupings of related peaks necessary for our method can be obtained from any peak clustering method and are built into a pairwise peak similarity score function. The similarity score matrix produced is used by an approximation algorithm for the weighted matching problem to produce the actual alignment result.<p></p> Results: We demonstrate that related peak information can improve alignment performance. The performance is evaluated on a set of benchmark datasets, where our method performs competitively compared to other popular alignment tools.<p></p> Availability: The proposed alignment method has been implemented as a stand-alone application in Python, available for download at http://github.com/joewandy/peak-grouping-alignment.<p></p&gt

Methods to accelerate the learning of bayesian network structures

Bayesian networks have become a standard technique in the representation of uncertain knowledge. This paper proposes methods that can accelerate the learning of a Bayesian network structure from a data set. These methods are applicable when learning an equivalence class of Bayesian network structures whilst using a score and search strategy. They work by constraining the number of validity tests that need to be done and by caching the results of validity tests. The results of experiments show that the methods improve the performance of algorithms that search through the space of equivalence classes multiple times and that operate on wide data sets. The experiments were performed by sampling data from six standard Bayesian networks and running an ant colony optimization algorithm designed to learn a Bayesian network equivalence class.

Learning Bayesian network equivalence classes with ant colony optimization

Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classe

Using ant colony optimisation in learning Bayesian network equivalence classes

Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed on the ACO construction graph. Secondly, moves can be given in terms of arbitrary identifiers. The algorithm is implemented and tested. The results show that ACO performs better than a greedy search whilst searching in the space of equivalence classes

A multiscale model for the rupture of linear polymers in strong flows

Abstract Polymer-containing solutions used across research and industry are commonly exposed to mechanically harsh fluid processes, for example shear and extensional forces during flow through porous media or rapid micro-dispensing of biopharmaceutical molecules. These forces are strong enough to break the covalent bonds in the polymer backbone. As this scission phenomenon can change the functional and fluid-flow properties as well as introduce reactive radicals into the solution, it must be understood and controlled. Experiments and models to-date have only provided partial or qualitative insights into this behaviour. Here we build a link between the molecular-scale degradation models and the macro-scale laminar flow of dilute solutions in any given geometry. A free-draining bead-rod model is used to investigate rupture events at the molecular scale. It is shown by uniaxial extension simulations of an ensemble of chains that scission can be conveniently described at the macroscopic scale as a first order reaction whose rate is a function of the conformation tensor of the macromolecules and the velocity gradient of the flow. This approach is implemented in the finite volume code OpenFOAM by elaborating an appropriate constitutive equation for the conformation tensor. The macroscopic model is run and analysed for ultra-dilute solutions of poly(methyl methacrylate) in ethyl acetate and polyethylene oxide in water, using the geometry of an abrupt contraction flow and neglecting any viscoelastic effect. This multi-scale approach bridges the gap between phenomenological observations of mechanically-induced chemical degradation in large scale applications and the rich field of molecular-scale models of macromolecules under flow.King Abdulaziz City for Science and Technology (KACST

Gene to Diagnostic: Self immobilizing protein for silica microparticle biosensor, modelled with sarcosine oxidase.

A rational design approach is proposed for a multifunctional enzyme reagent for point-of-care diagnostics. The biomaterial reduces downstream isolation steps and eliminates immobilization coupling chemicals for integration in a diagnostic platform. Fusion con-structs combined the central functional assay protein (e.g. monomeric sarcosine oxidase, mSOx, horseradish peroxidase, HRP), a visualizing protein (e.g. mCherry) and an in-built immobilization peptide (e.g. R5). Monitoring protein expression in E.coli was facilitated by following the increase in mCherry fluorescence, which could be matched to a color card, indicating when good protein expression has occurred. The R5 peptide (SSKKSGSYSGSKGSKRRIL) provided inbuilt affinity for silica and an immobilization capability for a silica based diagnostic, without requiring additional chemical coupling reagents. Silica particles extracted from beach sand were used to collect protein from crude protein extract with 85-95% selective uptake. The silica immobilized R5 pro-teins were stable for more than 2 months at room temperature. The Km for the silica-R52-mCh-mSOx-R5-6H was 16.5±0.9mM (com-pared with 16.5±0.4 mM, 16.3±0.3 mM, and 16.1±0.4 mM for R52-mCh-mSOx-R5-6H, mSOx-R5-6H and mSOx-6H respectively in solution). The use of the “silica-enzymes” in sarcosine and peroxide assays was shown, and a design using particle sedimentation through the sample was examined. Using shadowgraphy and particle image velocimetry the particle trajectory through the sample was mapped and an hourglass design with a narrow waist shown to give good control of particle position. The hourglass biosensor was demonstrated for sarcosine assay in the clinically useful range of 2.5 to 10 µM in both a dynamic and end point measurement regime.Royal Society IC160089 The Gates Cambridge Trust The Cambridge Trus