Evaluation and Calibration of In Silico Models of Thrombin Generation Using Experimental Data from Healthy and Haemophilic Subjects

2018 ◽  
Vol 80 (8) ◽  
pp. 1989-2025 ◽  
Author(s):  
Pierre Chelle ◽  
Claire Morin ◽  
Aurélie Montmartin ◽  
Michèle Piot ◽  
Michel Cournil ◽  
...  
2016 ◽  
Vol 106 ◽  
pp. 229-235 ◽  
Author(s):  
D.A. Winkler ◽  
M. Breedon ◽  
P. White ◽  
A.E. Hughes ◽  
E.D. Sapper ◽  
...  

Physiology ◽  
2006 ◽  
Vol 21 (4) ◽  
pp. 289-296 ◽  
Author(s):  
Sriram M. Ajay ◽  
Upinder S. Bhalla

Synaptic plasticity provides a record of neuronal activity and is a likely basis for memory. The early apparent simplicity of the process of synaptic plasticity has been lost in a flood of experimental data that now implicates some 200 signaling molecules in cellular memory. It is now clear that these signaling networks perform surprisingly sophisticated cellular decisions that weigh factors such as input patterns, location of stimulus, history of activity, and context. Computer models have followed experiments into this maze of molecular detail, often matching closely with their experimental counterparts, but perhaps losing simplicity in the process. Here, we suggest that the merger of models and experiment have begun to restore the earlier simplicity by outlining a few key functional roles for signaling networks in synaptic plasticity. In this review, we discuss the current state of understanding of synaptic plasticity in terms of models and experiments.


2021 ◽  
Vol 350 ◽  
pp. S64-S65
Author(s):  
K. Kopanska ◽  
J.C. Gómez-Tamayo ◽  
J. Llopis-Lorente ◽  
B.A. Trenor-Gomis ◽  
J. Sáiz ◽  
...  

Author(s):  
Juri A. Steiner ◽  
Urs A.T. Hofmann ◽  
Patrik Christen ◽  
Jean M. Favre ◽  
Stephen J. Ferguson ◽  
...  

2020 ◽  
Author(s):  
Kobi Felton ◽  
Daniel Wigh ◽  
Alexei Lapkin

Recent work has shown how Bayesian optimization (BO) is an efficient method for optimizing expensive experiments such as chemical reactions. However, in previous studies, each optimization has been started from scratch with no information about previous or similar chemical optimization studies. Therefore, BO can still require more iterations than many experimental budgets provide. Here, we overcome this challenge using multi-task BO. Through<i> in silico</i> benchmarking studies, we show how past experimental data can be leveraged to improve the quality and speed of reaction optimization.


2019 ◽  
Author(s):  
Rubén Laplaza ◽  
Julia Contreras-García ◽  
Franck Fuster ◽  
François Volatron ◽  
Patrick Chaquin

<div>This article dwells on the nature of “inverted bonds”, which make reference to the σ interaction between two s-p hybrids by their smaller lobes, and their presence in [1.1.1]propellane <b>1</b>. Firstly we study H 3 C-C models of C-C bonds with frozen HCC angles reproducing the constraints of various degrees of “inversion”. Secondly, the molecular orbital (MO) properties of [1.1.1]propellane <b>1</b> and [1.1.1]bicyclopentane <b>2</b> are analyzed with the help of orbital forces as a criterion of bonding/antibonding character and as a basis to evaluate bond energies. Triplet and cationic state of <b>1</b> species are also considered to confirm the bonding/antibonding character of MOs in the parent molecule. These approaches show an essentially non-bonding character of the σ central CC interaction in propellane. Within MO theory, this bonding is thus only due to π-type MOs (also called ‘banana’ MOs or ‘bridge’ MOs) and its total energy is evaluated to ca. 50 kcal/mol. In bicyclopentane <b>2</b>, despite a strong σ-type repulsion, a weak bonding (15-20 kcal/mol) exists between both central CC, also due to π-type interactions, though no bond is present in the Lewis structure. Overall, the so-called ‘inverted’ bond, as resulting from a σ overlap of the two s-p hybrids by their smaller lobes, appears highly questionable.</div>


2022 ◽  
Vol 9 (1) ◽  
pp. 28
Author(s):  
Henry Sutanto

The excitation, contraction, and relaxation of an atrial cardiomyocyte are maintained by the activation and inactivation of numerous cardiac ion channels. Their collaborative efforts cause time-dependent changes of membrane potential, generating an action potential (AP), which is a surrogate marker of atrial arrhythmias. Recently, computational models of atrial electrophysiology emerged as a modality to investigate arrhythmia mechanisms and to predict the outcome of antiarrhythmic therapies. However, the individual contribution of atrial ion channels on atrial action potential and reentrant arrhythmia is not yet fully understood. Thus, in this multiscale in-silico study, perturbations of individual atrial ionic currents (INa, Ito, ICaL, IKur, IKr, IKs, IK1, INCX and INaK) in two in-silico models of human atrial cardiomyocyte (i.e., Courtemanche-1998 and Grandi-2011) were performed at both cellular and tissue levels. The results show that the inhibition of ICaL and INCX resulted in AP shortening, while the inhibition of IKur, IKr, IKs, IK1 and INaK prolonged AP duration (APD). Particularly, in-silico perturbations (inhibition and upregulation) of IKr and IKs only minorly affected atrial repolarization in the Grandi model. In contrast, in the Courtemanche model, the inhibition of IKr and IKs significantly prolonged APD and vice versa. Additionally, a 50% reduction of Ito density abbreviated APD in the Courtemanche model, while the same perturbation prolonged APD in the Grandi model. Similarly, a strong model dependence was also observed at tissue scale, with an observable IK1-mediated reentry stabilizing effect in the Courtemanche model but not in the Grandi atrial model. Moreover, the Grandi model was highly sensitive to a change on intracellular Ca2+ concentration, promoting a repolarization failure in ICaL upregulation above 150% and facilitating reentrant spiral waves stabilization by ICaL inhibition. Finally, by incorporating the previously published atrial fibrillation (AF)-associated ionic remodeling in the Courtemanche atrial model, in-silico modeling revealed the antiarrhythmic effect of IKr inhibition in both acute and chronic settings. Overall, our multiscale computational study highlights the strong model-dependent effects of ionic perturbations which could affect the model’s accuracy, interpretability, and prediction. This observation also suggests the need for a careful selection of in-silico models of atrial electrophysiology to achieve specific research aims.


2020 ◽  
Author(s):  
Claudio Tomi-Andrino ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Philippe Soucaille ◽  
Klaus Winzer ◽  
...  

AbstractMetabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.Author summaryBiotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.


Sign in / Sign up

Export Citation Format

Share Document