73,184 research outputs found
PReServ: Provenance Recording for Services
The importance of understanding the process by which a result was generated in an experiment is fundamental to science. Without such information, other scientists cannot replicate, validate, or duplicate an experiment. We define provenance as the process that led to a result. With large scale in-silico experiments, it becomes increasingly difficult for scientists to record process documentation that can be used to retrieve the provenance of a result. Provenance Recording for Services (PReServ) is a software package that allows developers to integrate process documentation recording into their applications. PReServ has been used by several applications and its performance has been benchmarked
Interpreting two-photon imaging data of lymphocyte motility
Recently, using two-photon imaging it has been found that the movement of B and T cells in lymph nodes can be described by a random walk with persistence of orientation in the range of 2 minutes. We interpret this new class of lymphocyte motility data within a theoretical model. The model considers cell movement to be composed of the movement of subunits of the cell membrane. In this way movement and deformation of the cell are correlated to each other. We find that, indeed, the lymphocyte movement in lymph nodes can best be described as a random walk with persistence of orientation. The assumption of motility induced cell elongation is consistent with the data. Within the framework of our model the two-photon data suggest that T and B cells are in a single velocity state with large stochastic width. The alternative of three different velocity states with frequent changes of their state and small stochastic width is less likely. Two velocity states can be excluded
Using molecular mechanics to predict bulk material properties of fibronectin fibers
The structural proteins of the extracellular matrix (ECM) form fibers with finely tuned mechanical properties matched to the time scales of cell traction forces. Several proteins such as fibronectin (Fn) and fibrin undergo molecular conformational changes that extend the proteins and are believed to be a major contributor to the extensibility of bulk fibers. The dynamics of these conformational changes have been thoroughly explored since the advent of single molecule force spectroscopy and molecular dynamics simulations but remarkably, these data have not been rigorously applied to the understanding of the time dependent mechanics of bulk ECM fibers. Using measurements of protein density within fibers, we have examined the influence of dynamic molecular conformational changes and the intermolecular arrangement of Fn within fibers on the bulk mechanical properties of Fn fibers. Fibers were simulated as molecular strands with architectures that promote either equal or disparate molecular loading under conditions of constant extension rate. Measurements of protein concentration within micron scale fibers using deep ultraviolet transmission microscopy allowed the simulations to be scaled appropriately for comparison to in vitro measurements of fiber mechanics as well as providing estimates of fiber porosity and water content, suggesting Fn fibers are approximately 75% solute. Comparing the properties predicted by single molecule measurements to in vitro measurements of Fn fibers showed that domain unfolding is sufficient to predict the high extensibility and nonlinear stiffness of Fn fibers with surprising accuracy, with disparately loaded fibers providing the best fit to experiment. This work shows the promise of this microstructural modeling approach for understanding Fn fiber properties, which is generally applicable to other ECM fibers, and could be further expanded to tissue scale by incorporating these simulated fibers into three dimensional network models
In Silico screening of nonsteroidal anti-inflammatory drugs and their combined action on Prostaglandin H Synthase-1
The detailed kinetic model of Prostaglandin H Synthase-1 (PGHS-1) was applied to in silico screening of dose-dependencies for the different types of nonsteroidal anti-inflammatory drugs (NSAIDs), such as: reversible/irreversible, nonselective/selective to PGHS-1/PGHS-2 and time dependent/independent inhibitors (aspirin, ibuprofen, celecoxib, etc.) The computational screening has shown a significant variability in the IC50s of the same drug, depending on different in vitro and in vivo experimental conditions. To study this high heterogeneity in the inhibitory effects of NSAIDs, we have developed an in silico approach to evaluate NSAID action on targets under different PGHS-1 microenvironmental conditions, such as arachidonic acid, reducing cofactor, and peroxide concentrations. The designed technique permits translating the drug IC50, obtained in one experimental setting to another, and predicts in vivo inhibitory effects based on the relevant in vitro data. For the aspirin case, we elucidated the mechanism underlying the enhancement and reduction (aspirin resistance) of its efficacy, depending on PGHS-1 microenvironment in in vitro/in vivo experimental settings. We also present the results of the in silico screening of the combined action of sets of two NSAIDs (aspirin with ibuprofen, aspirin with celecoxib), and study the mechanism of the experimentally observed effect of the suppression of aspirin-mediated PGHS-1 inhibition by selective and nonselective NSAIDs. Furthermore, we discuss the applications of the obtained results to the problems of standardization of NSAID test assay, dependence of the NSAID efficacy on cellular environment of PGHS-1, drug resistance, and NSAID combination therapy
Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.
A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline
Iterative design of dynamic experiments in modeling for optimization of innovative bioprocesses
Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. In this paper, a novel iterative methodology for the model-based design of dynamic experiments in modeling for optimization is developed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 (rIL-11) whose DNA sequence has been cloned in an Escherichia coli strain. At each iteration, the proposed methodology resorts to a library of tendency models to increasingly bias bioreactor operating conditions towards an optimum. By selecting the ‘most informative’ tendency model in the sequel, the next dynamic experiment is defined by re-optimizing the input policy and calculating optimal sampling times. Model selection is based on minimizing an error measure which distinguishes between parametric and structural uncertainty to selectively bias data gathering towards improved operating conditions. The parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA) to pinpoint which parameters are keys for estimating the objective function. Results obtained after just a few iterations are very promising.Fil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Grau, Ricardo José Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin
Tubulin bond energies and microtubule biomechanics determined from nanoindentation in silico
Microtubules, the primary components of the chromosome segregation machinery,
are stabilized by longitudinal and lateral non-covalent bonds between the
tubulin subunits. However, the thermodynamics of these bonds and the
microtubule physico-chemical properties are poorly understood. Here, we explore
the biomechanics of microtubule polymers using multiscale computational
modeling and nanoindentations in silico of a contiguous microtubule fragment. A
close match between the simulated and experimental force-deformation spectra
enabled us to correlate the microtubule biomechanics with dynamic structural
transitions at the nanoscale. Our mechanical testing revealed that the
compressed MT behaves as a system of rigid elements interconnected through a
network of lateral and longitudinal elastic bonds. The initial regime of
continuous elastic deformation of the microtubule is followed by the transition
regime, during which the microtubule lattice undergoes discrete structural
changes, which include first the reversible dissociation of lateral bonds
followed by irreversible dissociation of the longitudinal bonds. We have
determined the free energies of dissociation of the lateral (6.9+/-0.4
kcal/mol) and longitudinal (14.9+/-1.5 kcal/mol) tubulin-tubulin bonds. These
values in conjunction with the large flexural rigidity of tubulin
protofilaments obtained (18,000-26,000 pN*nm^2), support the idea that the
disassembling microtubule is capable of generating a large mechanical force to
move chromosomes during cell division. Our computational modeling offers a
comprehensive quantitative platform to link molecular tubulin characteristics
with the physiological behavior of microtubules. The developed in silico
nanoindentation method provides a powerful tool for the exploration of
biomechanical properties of other cytoskeletal and multiprotein assemblie
Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle
Time-series of high throughput gene sequencing data intended for gene
regulatory network (GRN) inference are often short due to the high costs of
sampling cell systems. Moreover, experimentalists lack a set of quantitative
guidelines that prescribe the minimal number of samples required to infer a
reliable GRN model. We study the temporal resolution of data vs quality of GRN
inference in order to ultimately overcome this deficit. The evolution of a
Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and
metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is
sampled at a number of different rates. For each time-series we infer a linear
regression model of the GRN using the LASSO method. The inferred network
topology is evaluated in terms of the area under the precision-recall curve
AUPR. By plotting the AUPR against the number of samples, we show that the
trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples
corresponds to values on the ridge of the sigmoid
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