scholarly journals Metastable regimes and tipping points of biochemical networks with potential applications in precision medicine

2018 ◽  
Author(s):  
Satya Swarup Samal ◽  
Jeyashree Krishnan ◽  
Ali Hadizadeh Esfahani ◽  
Christoph Lüders ◽  
Andreas Weber ◽  
...  
Keyword(s):  
Invariant Manifolds ◽  
Tropical Geometry ◽  
Cell Types ◽  
Biochemical Networks ◽  
Model Parameters ◽  
Tipping Points ◽  
Cancer Disease ◽  
Cell Alterations ◽  
Low Dimensional ◽  
Sensitive Parameters

AbstractThe concept of attractor of dynamic biochemical networks has been used to explain cell types and cell alterations in health and disease. We have recently proposed an extension of the notion of attractor to take into account metastable regimes, defined as long lived dynamical states of the network. These regimes correspond to slow dynamics on low dimensional invariant manifolds of the biochemical networks. Methods based on tropical geometry allow to compute the metastable regimes and represent them as polyhedra in the space of logarithms of the species concentrations. We are looking for sensitive parameters and tipping points of the networks by analyzing how these polyhedra depend on the model parameters. Using the coupled MAPK and PI3K/Akt signaling networks as an example, we test the idea that large changes of the metastable states can be associated to cancer disease specific alterations of the network. In particular, we show that for model parameters representing protein concentrations, the protein differential level between tumors of different types is reasonably reflected in the sensitivity scores, with sensitive parameters corresponding to differential proteins.

scholarly journals Geometry of gene regulatory dynamics

2021 ◽  
Vol 118 (38) ◽  
pp. e2109729118 ◽  
Author(s):  
David A. Rand ◽  
Archishman Raju ◽  
Meritxell Sáez ◽  
Francis Corson ◽  
Eric D. Siggia
Keyword(s):  
Gene Networks ◽  
Dynamic Models ◽  
Network Models ◽  
Time Lapse ◽  
Cell Types ◽  
Model Parameters ◽  
Formal Representation ◽  
Riemann Metric ◽  
Low Dimensional ◽  
Two Parameters

Embryonic development leads to the reproducible and ordered appearance of complexity from egg to adult. The successive differentiation of different cell types that elaborate this complexity results from the activity of gene networks and was likened by Waddington to a flow through a landscape in which valleys represent alternative fates. Geometric methods allow the formal representation of such landscapes and codify the types of behaviors that result from systems of differential equations. Results from Smale and coworkers imply that systems encompassing gene network models can be represented as potential gradients with a Riemann metric, justifying the Waddington metaphor. Here, we extend this representation to include parameter dependence and enumerate all three-way cellular decisions realizable by tuning at most two parameters, which can be generalized to include spatial coordinates in a tissue. All diagrams of cell states vs. model parameters are thereby enumerated. We unify a number of standard models for spatial pattern formation by expressing them in potential form (i.e., as topographic elevation). Turing systems appear nonpotential, yet in suitable variables the dynamics are low dimensional and potential. A time-independent embedding recovers the original variables. Lateral inhibition is described by a saddle point with many unstable directions. A model for the patterning of the Drosophila eye appears as relaxation in a bistable potential. Geometric reasoning provides intuitive dynamic models for development that are well adapted to fit time-lapse data.

Preliminary uncertainty analysis – a prerequisite for assessing the predictive uncertainty of hydrologic models

1996 ◽  
Vol 33 (2) ◽  
pp. 79-90 ◽  
Author(s):  
Jian Hua Lei ◽  
Wolfgang Schilling
Keyword(s):  
Uncertainty Analysis ◽  
Model Parameters ◽  
Observation Data ◽  
Calibration Data ◽  
Model Output ◽  
Predictive Uncertainty ◽  
Runoff Volume ◽  
Output Uncertainty ◽  
Sensitive Parameters ◽  
Calculated Model

Physically-based urban rainfall-runoff models are mostly applied without parameter calibration. Given some preliminary estimates of the uncertainty of the model parameters the associated model output uncertainty can be calculated. Monte-Carlo simulation followed by multi-linear regression is used for this analysis. The calculated model output uncertainty can be compared to the uncertainty estimated by comparing model output and observed data. Based on this comparison systematic or spurious errors can be detected in the observation data, the validity of the model structure can be confirmed, and the most sensitive parameters can be identified. If the calculated model output uncertainty is unacceptably large the most sensitive parameters should be calibrated to reduce the uncertainty. Observation data for which systematic and/or spurious errors have been detected should be discarded from the calibration data. This procedure is referred to as preliminary uncertainty analysis; it is illustrated with an example. The HYSTEM program is applied to predict the runoff volume from an experimental catchment with a total area of 68 ha and an impervious area of 20 ha. Based on the preliminary uncertainty analysis, for 7 of 10 events the measured runoff volume is within the calculated uncertainty range, i.e. less than or equal to the calculated model predictive uncertainty. The remaining 3 events include most likely systematic or spurious errors in the observation data (either in the rainfall or the runoff measurements). These events are then discarded from further analysis. After calibrating the model the predictive uncertainty of the model is estimated.

scholarly journals Mixed-Lipid Storage Disorder Induced in Macrophages and Fibroblasts by Oritavancin (LY333328), a New Glycopeptide Antibiotic with Exceptional Cellular Accumulation

2005 ◽  
Vol 49 (5) ◽  
pp. 1695-1700 ◽  
Author(s):  
Françoise Van Bambeke ◽  
Jennifer Saffran ◽  
Marie-Paule Mingeot-Leclercq ◽  
Paul M. Tulkens
Keyword(s):  
Close Relation ◽  
Cell Types ◽  
Lipid Storage ◽  
Eukaryotic Cells ◽  
Cholesterol Accumulation ◽  
Giant Vesicles ◽  
Molar Ratios ◽  
Storage Disorder ◽  
Cell Alterations ◽  
Lipid Storage Disorder

ABSTRACT Oritavancin, a semisynthetic derivative of vancomycin endowed with a cationic amphiphilic character, accumulates to large extent in the lysosomes of eukaryotic cells (F. Van Bambeke, S. Carryn, C. Seral, H. Chanteux, D. Tyteca, M. P. Mingeot-Leclercq, and P. M. Tulkens, Antimicrob. Agents Chemother. 48:2853-2860, 2004). In the present study, we examined whether this accumulation could cause cell alterations in phagocytic (J774 mouse macrophages) and nonphagocytic (rat embryo fibroblasts) cells exposed to clinically meaningful (0- to 40-mg/liter) concentrations of oritavancin. Optical and electronic microscopy evidenced conspicuous alterations of the vacuolar apparatus in both cell types, characterized by the deposition of concentric lamellar structures, finely granular material, or other less-defined osmiophilic material, often deposed in giant vesicles. Biochemical studies showed an accumulation of phospholipids (1.5× control values) and free and esterified cholesterol (3 to 4× control values for total cholesterol). Accumulation of these lipids was in close relation to that of oritavancin (excess phospholipid/oritavancin and excess cholesterol/oritavancin molar ratios of 2 to 3 and 3 to 5, respectively). Cholesterol accumulation was rapid and reversible, and that of phospholipids was slower and poorly reversible. Vancomycin and teicoplanin, used as controls (50 and 100 mg/liter, respectively), did not cause any significant change in the lipid content of fibroblasts. The data therefore suggest that oritavancin has the potential to cause a mixed-lipid storage disorder in eukaryotic cells.

scholarly journals Climate change impacts model parameter sensitivity – What does this mean for calibration?

2020 ◽  
Author(s):  
Lieke Anna Melsen ◽  
Björn Guse
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
The United States ◽  
Parameter Sensitivity ◽  
Model Parameters ◽  
Hydrological Models ◽  
The Future ◽  
Influence Parameter ◽  
Sensitivity Changes ◽  
Sensitive Parameters

Abstract. Hydrological models are useful tools to explore the hydrological impact of climate change. Many of these models require calibration. A frequently employed strategy is to calibrate the five parameters that were found to be most relevant as identified in a sensitivity analysis. However, parameter sensitivity varies over climate, and therefore climate change could influence parameter sensitivity. In this study we explore the change in parameter sensitivity within a plausible climate change rate, and investigate if changes in sensitivity propagate into the calibration strategy. We employed three frequently used hydrological models (SAC, VIC, and HBV), and explored parameter sensitivity changes across 605 catchments in the United States by comparing a GCM-forced historical and future period. Consistent among all models is that the sensitivity of snow parameters decreases in the future. Which parameters increase in sensitivity is less consistent among the models. In 43 % to 49 % of the catchments, dependent on the model, at least one parameter changes in the future in the top-5 most sensitive parameters. The maximum number of changes in the parameter top-5 is two, in 2–4 % of the investigated catchments. The value of the parameters that enter the top-5 cannot easily be identified based on historical data, because the model is not yet sensitive to these parameters. This requires an adapted calibration strategy for long-term projections, for which we provide several suggestions. The disagreement among the models on processes becoming relevant in future projections also calls for a strict evaluation of the adequacy of the model structure and the model parameters implemented therein.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

Reduced-order modelling of the flow around a high-lift configuration with unsteady Coanda blowing

2016 ◽  
Vol 800 ◽  
pp. 72-110 ◽  
Author(s):  
Richard Semaan ◽  
Pradeep Kumar ◽  
Marco Burnazzi ◽  
Gilles Tissot ◽  
Laurent Cordier ◽  
...  
Keyword(s):  
Transient Flow ◽  
Stokes Equations ◽  
Lift Coefficient ◽  
Mean Field ◽  
Galerkin Projection ◽  
Validation Dataset ◽  
Model Parameters ◽  
High Lift ◽  
Modal Expansion ◽  
Low Dimensional

We propose a hierarchy of low-dimensional proper orthogonal decomposition (POD) models for the transient and post-transient flow around a high-lift airfoil with unsteady Coanda blowing over the trailing edge. The modal expansion comprises actuation modes as a lifting method for wall actuation following Graham et al. (Intl J. Numer. Meth. Engng, vol. 44 (7), 1999, pp. 945–972) and Kasnakoğlu et al. (Intl J. Control, vol. 81 (9), 2008, pp. 1475–1492). A novel element is separate actuation modes for different frequencies. The structure of the dynamic model rests on a Galerkin projection using the Navier–Stokes equations, simplifying mean-field considerations, and a stochastic term representing the background turbulence. The model parameters are identified with a data assimilation (4D-Var) method. We propose a model hierarchy from a linear oscillator explaining the suppression of vortex shedding by blowing to a fully nonlinear model resolving unactuated and actuated transients with steady and high-frequency modulation of blowing. The models’ accuracy is assessed through the mode amplitudes and an estimator for the lift coefficient. The robustness of the model is physically justified, and then observed for the training and the validation dataset.

scholarly journals Discovering a sparse set of pairwise discriminating features in high-dimensional data

2020 ◽  
Author(s):  
Samuel Melton ◽  
Sharad Ramanathan
Keyword(s):  
Single Cell ◽  
Dimensional Space ◽  
Cell Types ◽  
Dimensional Subspace ◽  
Supplementary Information ◽  
High Dimensional ◽  
Technological Advances ◽  
Data Points ◽  
Low Dimensional ◽  
Sparse Set

Abstract Motivation Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions. Results We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional space. We propose an unsupervised method to identify the subset of features that define a low-dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single-cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low-dimensional subspace. Availability and implementation https://github.com/smelton/SMD. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Bayesian inference of distributed time delay in transcriptional and translational regulation

2019 ◽  
Vol 36 (2) ◽  
pp. 586-593
Author(s):  
Boseung Choi ◽  
Yu-Yu Cheng ◽  
Selahattin Cinar ◽  
William Ott ◽  
Matthew R Bennett ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Stochastic Systems ◽  
Imaging Techniques ◽  
Reaction Rates ◽  
Biochemical Networks ◽  
Supplementary Information ◽  
Mcmc Methods ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Cell Functions

Abstract Motivation Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks. Results We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth–death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy. Availability and implementation Accompanying code in R is available at https://github.com/cbskust/DDE_BD. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Inference of a Mesoscopic Population Model from Population Spike Trains

2020 ◽  
Vol 32 (8) ◽  
pp. 1448-1498 ◽  
Author(s):  
Alexandre René ◽  
André Longtin ◽  
Jakob H. Macke
Keyword(s):  
Population Model ◽  
Single Neuron ◽  
Simulated Data ◽  
Population Data ◽  
Model Parameters ◽  
Population Activity ◽  
Mcmc Sampling ◽  
Wide Range ◽  
Low Dimensional ◽  
Aggregate Population

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

scholarly journals Integro-differential equations for the self-organisation of liver zones by competitive exclusion of cell-types

1987 ◽  
Vol 29 (2) ◽  
pp. 156-194 ◽  
Author(s):  
L. Bass ◽  
A. J. Bracken ◽  
K. Holmåker ◽  
B. R. F. Jefferies
Keyword(s):  
Differential Equations ◽  
Enzymatic Activity ◽  
Cell Types ◽  
Stationary Solutions ◽  
Stable Set ◽  
Model Parameters ◽  
Asymptotically Stable ◽  
Hepatic Sinusoid ◽  
Jump Discontinuities ◽  
Cell Densities

AbstractA model is developed for the seif-organisation of zones of enzymatic activity along a liver capillary (hepatic sinusoid) lined with cells of two types, which contain different enzymes and compete for sites on the wall of the sinusoid. An effectively non-local interaction between the cells arises from local consumption of oxygen from blood flowing throug1 the sinusoid, which gives rise to gradients of oxygen concentration in turn influencing rates of division and of death of the two cell-types. The process is modelled by a pair of coupled non-linear integro-differential equations for the cell-densities as functions of time and position along the sinusoid. Existence of a unique, bounded, non-negative solution of the equations is proved, for prescribed initial values. The equations admit infinitely many stationary solutions, but it is shown that all except one are unstable, for any given set of the model parameters. The remaining solution is shown to be asymptotically stable against a large class of perturbations. For certain ranges of the model parameters, the asymptotically stable stationaxy solution has a zonal structure, with cells of one type located entirely upstream of cells of the other type, and with jump discontinuities in the cell densities at a certain distance along the sinusoid. Such sinusoidal zones can account for zones of enzymatic activity observed in the intact liver. Exceptional cases are found for singular choices of model parameters, such that stationary cell-densities cannot be asymptotically stable individually, but together form an asymptotically stable set. Certain mathematical questions are left open, notably the behaviour of large deviations from stationary solutions, and the global stability of such solutions. Possible generalisations of the model are described.

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