Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-statistics with Peptide Quantification Concordance,

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Highlights

• •Bayesian Beta-Binomial model integrates ion statistics with peptide ratio agreement.
• •Model appropriately interprets information from low signal peptides.
• •Confidence can be assigned even without replicates.
• •Model adds sensitivity to detection of small changes.

     

Setting up and running molecular dynamics simulations of membrane proteins

Molecular dynamics simulations have become a popular and powerful technique to study lipids and membrane proteins. We present some general questions and issues that should be considered prior to embarking on molecular dynamics simulation studies of membrane proteins and review common simulation methods. We suggest a practical approach to setting up and running simulations of membrane proteins, and introduce two new (related) methods to embed a protein in a lipid bilayer. Both methods rely on placing lipids and the protein(s) on a widely spaced grid and then 'shrinking' the grid until the bilayer with the protein has the desired density, with lipids neatly packed around the protein. When starting from a grid based on a single lipid structure, or several potentially different lipid structures (method 1), the bilayer will start well-packed but requires more equilibration. When starting from a pre-equilibrated bilayer, either pure or mixed, most of the structure of the bilayer stays intact, reducing equilibration time (method 2). The main advantages of these methods are that they minimize equilibration time and can be almost completely automated, nearly eliminating one time consuming step in MD simulations of membrane proteins.     

Pathway-based Analysis Tools for Complex Diseases:A Review

Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehen- sive understanding of the molecular mechanisms underlying complex diseases. Extensive studies uti- lizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available path- way-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are dis- cussed. This review will provide a useful guide to dissect complex diseases.     

Modes and cuts in metabolic networks: complexity and algorithms

Constraint-based approaches recently brought new insight into our understanding of metabolism. By making very simple assumptions such as that the system is at steady-state and some reactions are irreversible, and without requiring kinetic parameters, general properties of the system can be derived. A central concept in this methodology is the notion of an elementary mode (EM for short) which represents a minimal functional subsystem. The computation of EMs still forms a limiting step in metabolic studies and several algorithms have been proposed to address this problem leading to increasingly faster methods. However, although a theoretical upper bound on the number of elementary modes that a network may possess has been established, surprisingly, the complexity of this problem has never been systematically studied. In this paper, we give a systematic overview of the complexity of optimisation problems related to modes. We first establish results regarding network consistency. Most consistency problems are easy, i.e., they can be solved in polynomial time. We then establish the complexity of finding and counting elementary modes. We show in particular that finding one elementary mode is easy but that this task becomes hard when a specific EM (i.e. an EM containing some specified reactions) is sought. We then show that counting the number of elementary modes is musical sharpP-complete. We emphasize that the easy problems can be solved using currently existing software packages. We then analyse the complexity of a closely related task which is the computation of so-called minimum reaction cut sets and we show that this problem is hard. We then present two positive results which both allow to avoid computing EMs as a prior to the computation of reaction cuts. The first one is a polynomial approximation algorithm for finding a minimum reaction cut set. The second one is a test for verifying whether a set of reactions constitutes a reaction cut; this test can be readily included in existing algorithms to improve their performance. Finally, we discuss the complexity of other cut-related problems.     

Finite element method (FEM), mechanobiology and biomimetic scaffolds in bone tissue engineering

Techniques of bone reconstructive surgery are largely based on conventional, non-cell-based therapies that rely on the use of durable materials from outside the patient's body. In contrast to conventional materials, bone tissue engineering is an interdisciplinary field that applies the principles of engineering and life sciences towards the development of biological substitutes that restore, maintain, or improve bone tissue function. Bone tissue engineering has led to great expectations for clinical surgery or various diseases that cannot be solved with traditional devices. For example, critical-sized defects in bone, whether induced by primary tumor resection, trauma, or selective surgery have in many cases presented insurmountable challenges to the current gold standard treatment for bone repair. The primary purpose of bone tissue engineering is to apply engineering principles to incite and promote the natural healing process of bone which does not occur in critical-sized defects. The total market for bone tissue regeneration and repair was valued at $1.1 billion in 2007 and is projected to increase to nearly $1.6 billion by 2014.Usually, temporary biomimetic scaffolds are utilized for accommodating cell growth and bone tissue genesis. The scaffold has to promote biological processes such as the production of extra-cellular matrix and vascularisation, furthermore the scaffold has to withstand the mechanical loads acting on it and to transfer them to the natural tissues located in the vicinity. The design of a scaffold for the guided regeneration of a bony tissue requires a multidisciplinary approach. Finite element method and mechanobiology can be used in an integrated approach to find the optimal parameters governing bone scaffold performance.In this paper, a review of the studies that through a combined use of finite element method and mechano-regulation algorithms described the possible patterns of tissue differentiation in biomimetic scaffolds for bone tissue engineering is given. Firstly, the generalities of the finite element method of structural analysis are outlined; second, the issues related to the generation of a finite element model of a given anatomical site or of a bone scaffold are discussed; thirdly, the principles on which mechanobiology is based, the principal theories as well as the main applications of mechano-regulation models in bone tissue engineering are described; finally, the limitations of the mechanobiological models and the future perspectives are indicated.     

Efficient algorithms for Life Cycle Assessment,Input-Output Analysis,and Monte-Carlo Analysis

Goal, Scope, and Background As Life Cycle Assessment (LCA) and Input-Output Analysis (IOA) systems increase in size, computation times and memory usage can increase rapidly. The use of efficient methods of solution allows the use of a wide range of analysis techniques. Some techniques, such as Monte-Carlo Analysis, may be limited if computational times are too slow. Discussion of Methods In this article, I describe algorithms that substantially reduce computation times and memory usage for solving LCA and IOA systems and performing Monte-Carlo analysis. The algorithms are based on well-established iterative methods of solving linear systems and exploit the power series expansion of the Leontief inverse. The algorithms are further enhanced by using sparse matrix algebra. Results and Discussion The algorithms presented in this article reduce computational time and memory usage by orders of magnitude, while still retaining a high degree of accuracy. For a 3225×3225 LCA system, the algorithm reduced computation time from 70s to 0.06s while retaining an accuracy of 10⁻³%. Storage was reduced from 166 megabytes to 1.8 megabytes. The algorithm was used to perform a Monte-Carlo analysis on the same system with 1,000 samples in 90s. I also discuss various issues of power series convergence for general LCA and IOA systems and show that convergence will generally hold due to the mathematical structure of LCA and IOA systems. Conclusions By exploiting the mathematical structure of LCA and IOA iterative techniques substantially reduced the computational times required for solving LCA and IOA systems and for performing Monte-Carlo simulations. This allows more wide-spread implementation analysis techniques, such as Monte-Carlo analysis, in LCA and IOA. Recommendations and Perspectives It is suggested that algorithms, such as the ones described in this article, should be implemented in LCA packages. Various checks can be used to verify that computational errors are kept to a minimum.     

Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning

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Highlights

• •Deep learning models for prediction of LC-MS/MS properties.

     

Consistent Sets of Spectrophotometric Chlorophyll Equations for Acetone, Methanol and Ethanol Solvents

A set of equations for determining chlorophyll a (Chl a) and accessory chlorophylls b, c ₂ , c ₁ + c ₂ and the special case of Acaryochloris marina, which uses Chl d as its primary photosynthetic pigment and also has Chl a, have been developed for 90% acetone, methanol and ethanol solvents. These equations for different solvents give chlorophyll assays that are consistent with each other. No algorithms for Chl c compounds (c ₂ , c ₁ + c ₂) in the presence of Chl a have previously been published for methanol or ethanol. The limits of detection (and inherent error, ± 95% confidence limit), for chlorophylls in all organisms tested, was generally less than 0.1 μg/ml. The Chl a and b algorithms for green algae and land plants have very small inherent errors (< 0.01 μg/ml). Chl a and d algorithms for Acaryochloris marina are consistent with each other, giving estimates of Chl d/a ratios which are consistent with previously published estimates using HPLC and a rarely used algorithm originally published for diethyl ether in 1955. The statistical error structure of chlorophyll algorithms is discussed. The relative error of measurements of chlorophylls increases hyperbolically in diluted chlorophyll extracts because the inherent errors of the chlorophyll algorithms are constants independent of the magnitude of absorbance readings. For safety reasons, efficient extraction of chlorophylls and the convenience of being able to use polystyrene cuvettes, the algorithms for ethanol are recommended for routine assays of chlorophylls. The methanol algorithms would be convenient for assays associated with HPLC work.     

Assessing Protein Sequence Database Suitability Using De Novo Sequencing

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Highlights

• •An automated de novo sequencing program is evaluated with respect to different types of data.
• •The number of unique high scoring de novo sequences that can be assigned to a data set provides a metric of overall data quality.
• •A database suitability metric is presented for situations when the database choice is not obvious, or when the database quality is uncertain.