A comprehensive study on different modelling approaches to predict platelet deposition rates in a perfusion chamber

Thrombus formation is a multiscale phenomenon triggered by platelet deposition over a protrombotic surface (eg. a ruptured atherosclerotic plaque). Despite the medical urgency for computational tools that aid in the early diagnosis of thrombotic events, the integration of computational models of thrombus formation at different scales requires a comprehensive understanding of the role and limitation of each modelling approach. We propose three different modelling approaches to predict platelet deposition. Specifically, we consider measurements of platelet deposition under blood flow conditions in a perfusion chamber for different time periods (3, 5, 10, 20 and 30 minutes) at shear rates of 212 s(-1), 1390 s(-1) and 1690 s(-1). Our modelling approaches are: i) a model based on the mass-transfer boundary layer theory; ii) a machine-learning approach; and iii) a phenomenological model. The results indicate that the three approaches on average have median errors of 21%, 20.7% and 14.2%, respectively. Our study demonstrates the feasibility of using an empirical data set as a proxy for a real-patient scenario in which practitioners have accumulated data on a given number of patients and want to obtain a diagnosis for a new patient about whom they only have the current observation of a certain number of variables.Peer reviewe

Remission of obesity and insulin resistance is not sufficient to restore mitochondrial homeostasis in visceral adipose tissue

Metabolic plasticity is the ability of a biological system to adapt its metabolic phenotype to different environmental stressors. We used a whole-body and tissue-specific phenotypic, functional, proteomic, metabolomic and transcriptomic approach to systematically assess metabolic plasticity in diet-induced obese mice after a combined nutritional and exercise intervention. Although most obesity and overnutrition-related pathological features were successfully reverted, we observed a high degree of metabolic dysfunction in visceral white adipose tissue, characterized by abnormal mitochondrial morphology and functionality. Despite two sequential therapeutic interventions and an apparent global healthy phenotype, obesity triggered a cascade of events in visceral adipose tissue progressing from mitochondrial metabolic and proteostatic alterations to widespread cellular stress, which compromises its biosynthetic and recycling capacity. In humans, weight loss after bariatric surgery showed a transcriptional signature in visceral adipose tissue similar to our mouse model of obesity reversion. Overall, our data indicate that obesity prompts a lasting metabolic fingerprint that leads to a progressive breakdown of metabolic plasticity in visceral adipose tissue

Applications of analytic and geometry concepts of the theory of Calculus of Variations to the Intrinsic Reaction Coordinate model

17 pages, 6 figures.-- Dedicated in honor of Professor Peter Pulay on his 65th birthday.Printed version published Oct 2007.A mathematical analysis of several algorithms, for the integration of the differential equation associated to the Intrinsic Reaction Coordinate path, is performed. This analysis first shows that the Intrinsic Reaction Coordinate path can be derived from a variational problem, so that it has the properties of an extremal curve. Then, one may borrow the mathematical methods for the integration of extremal curves, to formulate new algorithms for the integration of the Intrinsic Reaction Coordinate path. One may use also this theoretical framework, to recast recently formulated algorithms based on direct minimization of an arbitrary curve, such as the Nudged Elastic Band Method or String Method. In this view a new algorithm is proposed. Finally, the theory of broken extremals is used to analyse an Intrinsic Reaction Coordinate path possessing a valley ridge inflection point.Financial support from the Spanish Ministerio de Ciencia y Tecnología, DGI project CTQ2005-01117/ BQU and, in part from the Generalitat de Catalunya projects 2005SGR-00111 and 2005SGR-00175 is fully acknowledged. Ramon Crehuet gratefully acknowledges the Ramón y Cajal Program. Antoni Aguilar-Mogas gratefully thanks Ministerio de Ciencia y Tecnología for a predoctoral fellowship.Peer reviewe

A Bayesian machine scientist to aid in the solution of challenging scientific problems

Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need “machine scientists� that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods

iMet: A Network-Based Computational Tool To Assist in the Annotation of Metabolites from Tandem Mass Spectra

Structural annotation of metabolites relies mainly on tandem mass spectrometry (MS/MS) analysis. However, approximately 90% of the known metabolites reported in metabolomic databases do not have annotated spectral data from standards. This situation has fostered the development of computational tools that predict fragmentation patterns <i>in silico</i> and compare these to experimental MS/MS spectra. However, because such methods require the molecular structure of the detected compound to be available for the algorithm, the identification of novel metabolites in organisms relevant for biotechnological and medical applications remains a challenge. Here, we present iMet, a computational tool that facilitates structural annotation of metabolites not described in databases. iMet uses MS/MS spectra and the exact mass of an unknown metabolite to identify metabolites in a reference database that are structurally similar to the unknown metabolite. The algorithm also suggests the chemical transformation that converts the known metabolites into the unknown one. As a proxy for the structural annotation of novel metabolites, we tested 148 metabolites following a leave-one-out cross-validation procedure or by using MS/MS spectra experimentally obtained in our laboratory. We show that for 89% of the 148 metabolites at least one of the top four matches identified by iMet enables the proper annotation of the unknown metabolites. To further validate iMet, we tested 31 metabolites proposed in the 2012–16 CASMI challenges. iMet is freely available at http://imet.seeslab.net

CliqueMS Validation Data

CliqueMSData Data used for the validation of CliqueMS (https://CRAN.R-project.org/package=cliqueMS) This repository contains the mzXML files corresponding to the novel LC-MS1 spectra used to validate CliqueMS. The raw data corresponding to these files has been obtained as explained below. The corresponding mzXML files have been obtained using the msconvert tool in ProteoWizard. This repository includes 3 files: standards.mzXML positive.mzXML negative.mzXML standards.mzXML - Mixture of standards Materials. MS1 grade acetonitrile (ACN), ammonium acetate (NH4Ac) and NH4OH were purchased from SDS (Peypin, France). Water was produced in an in-house Milli-Q purification system (Millipore, Molsheim, France). Formic acid and ammonium fluoride were purchased from Sigma-Aldrich (Steinheim, Germany). Standards: (-)riboflavine, 1,2-distearoyl-sn-glycero-3-phosphocholine, biotin, cholic acid, deoxycholic acid, L-methionine sulfoxide, thymine and uracil were purchased from Sigma-Aldrich (Steinheim, Germany). Standard mix preparation. All standards were pooled to a final concentration of 1ppm in H2O:ACN (5:95) with 0.1% formic acid. MS1 analyses. MS1 analyses were performed using an UHPLC system (1290 series, Agilent Technologies) coupled to a 6550 ESI-QTOF MS (Agilent Technologies) operated in positive (ESI+) electrospray ionization mode. A vial containing the standard mix was kept at -20ºC prior to MS1 analysis. Metabolites were separated using an Acquity UPLC BEH HILIC column (2.1 x 150 mm, 1.8 \mu m) and the solvent system was A1 = 20mM ammonium acetate and 15 mM NH4OH in water and B1 = 95% ACN and 5% H2O. The linear gradient elution started at 100% B (time 0--2 min) and finished at 75% A (10-15 min). The injection volume was 5 \mu L. ESI conditions: gas temperature, 150ºC; drying gas, 13 L min--1; nebulizer, 35 psig; fragmentor, 400 V; and skimmer, 65 V. The instrument was set to acquire over the m/z range 100--1500 in full-scan mode with an acquisition rate of 4 spectra/sec. MS/MS was performed in targeted mode, and the instrument was set to acquire over the m/z range 50--1000, with a default isolation width (the width half-maximum of the quadrupole mass bandpass used during MS/MS precursor isolation) of 4 m/z. The collision energy was fixed at 20 V. positive.mzXML & negative.mzXML - Complex Samples Materials. MS1 grade methanol (MeOH) and acetonitrile (ACN) and analytical grade chloroform (CHCl3) were purchased from SDS (Peypin, France). Water was produced in an in-house Milli-Q purification system (Millipore, Molsheim, France). Formic acid and ammonium fluoride were purchased from Sigma-Aldrich (Steinheim, Germany). Experimental Animals. Irs-2-deficient mice were generated initially on a C57BL6/J: SV129 background and then backcrossed to establish a pure C57BL6/J background. Thus, the offspring resulting from the breeding of Irs-2(2/2) with RIP-Irs-2 line were C57BL6/J. Metabolite extraction method. Retinas were first lyophilized and metabolites were extracted adding 190 uL of MeOH and 120 uL of H2O, then vortex during 30 seconds. Afterwards, samples were frozen during 1 min in liquid nitrogen (N2) and thawed by cold sonication during 30 seconds. This step was applied three times. Then 380 uL of chloroform were added and vortexed during 30 seconds. Finally, samples were centrifuged (15000 rpm, 15 min a 4ºC). The supernatant was extracted and dried. The sample was suspended in 100 uL of H2O:MeOH (1:1) and stored at -80ºC until further analysis. MS1 analyses. MS1 analyses were performed using an UHPLC system (1290 series, Agilent Technologies) coupled to a 6550 ESI-QTOF MS (Agilent Technologies) operated in positive (ESI+) or negative (ESI-) electrospray ionization mode. Vials containing extracted metabolites were kept at -20ºC prior to MS1 analysis. When the instrument was operated in positive ionization mode, metabolites were separated using an Acquity UPLC (HSS T3) C18 reverse phase (RP) column (2.1 x 150 mm, 1.8 \mu m) and the solvent system was A1 = 0.1% formic acid in water and B1 = 0.1% formic acid in acetonitrile. When the instrument was operated in negative ionization mode, metabolites were separated using an Acquity UPLC (BEH) C18 RP column (2.1 x 150 mm, 1.7 \mu m) and the solvent system was A2 = 1 mM ammonium fluoride in water and B2 = acetonitrile. The linear gradient elution started at 100% A (time 0--2 min) and finished at 100% B (10-15 min). The injection volume was 5 \mu L. ESI conditions: gas temperature, 150ºC; drying gas, 13 L min--1; nebulizer, 35 psig; fragmentor, 400 V; and skimmer, 65 V. The instrument was set to acquire over the m/z range 100--1500 in full-scan mode with an acquisition rate of 4 spectra/sec. MS/MS was performed in targeted mode, and the instrument was set to acquire over the m/z range 50--1000, with a default isolation width (the width half-maximum of the quadrupole mass bandpass used during MS/MS precursor isolation) of 4 m/z. The collision energy was fixed at 20 V. positive.mzXML and negative.mzXML files correspond to LC-MS1 spectra obtained for positive and negative ionization, respectively

A Bayesian machine scientist to aid in the solution of challenging scientific problems

Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need "machine scientists" that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods