Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm

Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein profiles from biological samples with the aim of discovering biomarkers for disease. However, the raw protein profiles suffer from several sources of bias or systematic variation which need to be removed via pre-processing before meaningful downstream analysis of the data can be undertaken. Baseline subtraction, an early pre-processing step that removes the non-peptide signal from the spectra, is complicated by the following: (i) each spectrum has, on average, wider peaks for peptides with higher mass-to-charge ratios (m/z), and (ii) the time-consuming and error-prone trial-and-error process for optimising the baseline subtraction input arguments. With reference to the aforementioned complications, we present an automated pipeline that includes (i) a novel `continuous' line segment algorithm that efficiently operates over data with a transformed m/z-axis to remove the relationship between peptide mass and peak width, and (ii) an input-free algorithm to estimate peak widths on the transformed m/z scale. The automated baseline subtraction method was deployed on six publicly available proteomic MS datasets using six different m/z-axis transformations. Optimality of the automated baseline subtraction pipeline was assessed quantitatively using the mean absolute scaled error (MASE) when compared to a gold-standard baseline subtracted signal. Near-optimal baseline subtraction was achieved using the automated pipeline. The advantages of the proposed pipeline include informed and data specific input arguments for baseline subtraction methods, the avoidance of time-intensive and subjective piecewise baseline subtraction, and the ability to automate baseline subtraction completely. Moreover, individual steps can be adopted as stand-alone routines.Comment: 50 pages, 19 figure

Growth states of catalytic reaction networks exhibiting energy metabolism

All cells derive nutrition by absorbing some chemical and energy resources from the environment; these resources are used by the cells to reproduce the chemicals within them, which in turn leads to an increase in their volume. In this study, we introduce a protocell model exhibiting catalytic reaction dynamics, energy metabolism, and cell growth. Results of extensive simulations of this model show the existence of four phases with regard to the rates of both the influx of resources and the cell growth. These phases include an active phase with high influx and high growth rates, an inefficient phase with high influx but low growth rates, a quasi-static phase with low influx and low growth rates, and a death phase with negative growth rate. A mean field model well explains the transition among these phases as bifurcations. The statistical distribution of the active phase is characterized by a power law and that of the inefficient phase is characterized by a nearly equilibrium distribution. We also discuss the relevance of the results of this study to distinct states in the existing cells.Comment: 21 pages, 5 figure

Calmodulin-Sensitive and Calmodulin-Insensitive Components of Adenylate Cyclase Activity in Rat Striatum Have Differential Responsiveness to Guanyl Nucleotides

The interaction between the Ca 2+ -binding protein, calmodulin, and guanyl nucleotides was investigated in a rat striatal paniculate fraction. We found that the ability of calmodulin to stimulate adenylate cyclase in the presence of guanyl nucleotides depends upon the type and concentration of the guanyl nucleotide. Adenylate cyclase activity measured in the presence of calmodulin and GTP reflected additivity at every concentration of these reactants. On the contrary, when the activating guanyl nucleotide was the nonhydrolyzable analog of GTP, guanosine-5′-(3,7-imido)triphosphate (GppNHp), calmodulin could further activate adenylate cyclase only at concentrations less than 0.2 p.M GppNHp. Kinetic analysis of adenylate cyclase by GppNHp was compatible with a model of two components of adenylate cyclase activity, with over a 100-fold difference in sensitivity for GppNHp. The component with the higher affinity for GppNHp was competitively stimulated by calmodulin. The additivity between calmodulin and GTP in the striatal particulate fraction suggests that they stimulate different components of cyclase activity. The cal-modulin-stimulatable component constituted 60% of the total activity. Our two-component model does not delineate, at this point, whether there are two separate catalytic subunits or one catalytic subunit with two GTP-binding proteins. The finding that GTP was unable to activate the calmodulin-sensitive component suggests that this component has either a different mode of binding to a GTP-binding protein or inherently higher GTPase activity than has the calmodulin-insensitive component. The results suggest there are two components of adenylate cyclase activity that can be differentiated by their sensitivities to calmodulin and guanyl nucleotides.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65160/1/j.1471-4159.1983.tb00838.x.pd

The effect of RO3201195 and a pyrazolyl ketone P38 MAPK inhibitor library on the proliferation of Werner syndrome cells

No description supplie

A protein conformational change associated with the photoreduction of the primary and secondary quinones in the bacterial reaction center

AbstractA comparison is made between the PQA → P+Q−A and PQAQB → P+QAQ−B transitions in Rps. viridis and Rb. sphaeroides reaction centers (RCs) by the use of light-induced Fourier transform infrared (FTIR) difference spectroscopy. In Rb. sphaeroides RCs, we identify a signal at 1650 cm−1 which is present in the P+QA-minus-PQA spectrum and not in the P+QAQ−B-minus-PQAQB spectrum. In contrast, this signal is present in both P+Q−A-minus-PQ−A and P+QAQ−B-minus-PQAQB spectra of Rps. viridis RCs. These data are interpreted in terms of a conformational change of the protein backbone near QA (possible at the peptide CO of a conserved alanine residue in the QA pocket) and of the different bonding interactions of QB with the protein in the RC of the two species

Retarding Sub- and Accelerating Super-Diffusion Governed by Distributed Order Fractional Diffusion Equations

We propose diffusion-like equations with time and space fractional derivatives of the distributed order for the kinetic description of anomalous diffusion and relaxation phenomena, whose diffusion exponent varies with time and which, correspondingly, can not be viewed as self-affine random processes possessing a unique Hurst exponent. We prove the positivity of the solutions of the proposed equations and establish the relation to the Continuous Time Random Walk theory. We show that the distributed order time fractional diffusion equation describes the sub-diffusion random process which is subordinated to the Wiener process and whose diffusion exponent diminishes in time (retarding sub-diffusion) leading to superslow diffusion, for which the square displacement grows logarithmically in time. We also demonstrate that the distributed order space fractional diffusion equation describes super-diffusion phenomena when the diffusion exponent grows in time (accelerating super-diffusion).Comment: 11 pages, LaTe

The meaning of life in a developing universe

The evolution of life on Earth has produced an organism that is beginning to model and understand its own evolution and the possible future evolution of life in the universe. These models and associated evidence show that evolution on Earth has a trajectory. The scale over which living processes are organized cooperatively has increased progressively, as has its evolvability. Recent theoretical advances raise the possibility that this trajectory is itself part of a wider developmental process. According to these theories, the developmental process has been shaped by a larger evolutionary process that involves the reproduction of universes. This evolutionary process has tuned the key parameters of the universe to increase the likelihood that life will emerge and develop to produce outcomes that are successful in the larger process (e.g. a key outcome may be to produce life and intelligence that intentionally reproduces the universe and tunes the parameters of ‘offspring’ universes). Theory suggests that when life emerges on a planet, it moves along this trajectory of its own accord. However, at a particular point evolution will continue to advance only if organisms emerge that decide to advance the evolutionary process intentionally. The organisms must be prepared to make this commitment even though the ultimate nature and destination of the process is uncertain, and may forever remain unknown. Organisms that complete this transition to intentional evolution will drive the further development of life and intelligence in the universe. Humanity’s increasing understanding of the evolution of life in the universe is rapidly bringing it to the threshold of this major evolutionary transition

Schumpeterian economic dynamics as a quantifiable minimum model of evolution

We propose a simple quantitative model of Schumpeterian economic dynamics. New goods and services are endogenously produced through combinations of existing goods. As soon as new goods enter the market they may compete against already existing goods, in other words new products can have destructive effects on existing goods. As a result of this competition mechanism existing goods may be driven out from the market - often causing cascades of secondary defects (Schumpeterian gales of destruction). The model leads to a generic dynamics characterized by phases of relative economic stability followed by phases of massive restructuring of markets - which could be interpreted as Schumpeterian business `cycles'. Model timeseries of product diversity and productivity reproduce several stylized facts of economics timeseries on long timescales such as GDP or business failures, including non-Gaussian fat tailed distributions, volatility clustering etc. The model is phrased in an open, non-equilibrium setup which can be understood as a self organized critical system. Its diversity dynamics can be understood by the time-varying topology of the active production networks.Comment: 21 pages, 11 figure