scholarly journals WebMaBoSS: A Web Interface for Simulating Boolean Models Stochastically

2021 ◽  
Vol 8 ◽  
Author(s):  
Vincent Noël ◽  
Marco Ruscone ◽  
Gautier Stoll ◽  
Eric Viara ◽  
Andrei Zinovyev ◽  
...  
Keyword(s):  
Continuous Time ◽  
Current Model ◽  
Stochastic Approach ◽  
Web Interface ◽  
Boolean Models ◽  
Specific Knowledge ◽  
Modeling Tools ◽  
Minimum Number ◽  
Quantitative Results ◽  
Cell Collective

WebMaBoSS is an easy-to-use web interface for conversion, storage, simulation and analysis of Boolean models that allows to get insight from these models without any specific knowledge of modeling or coding. It relies on an existing software, MaBoSS, which simulates Boolean models using a stochastic approach: it applies continuous time Markov processes over the Boolean network. It was initially built to fill the gap between Boolean and continuous formalisms, i.e., providing semi-quantitative results using a simple representation with a minimum number of parameters to fit. The goal of WebMaBoSS is to simplify the use and the analysis of Boolean models coping with two main issues: 1) the simulation of Boolean models of intracellular processes with MaBoSS, or any modeling tool, may appear as non-intuitive for non-experts; 2) the simulation of already-published models available in current model databases (e.g., Cell Collective, BioModels) may require some extra steps to ensure compatibility with modeling tools such as MaBoSS. With WebMaBoSS, new models can be created or imported directly from existing databases. They can then be simulated, modified and stored in personal folders. Model simulations are performed easily, results visualized interactively, and figures can be exported in a preferred format. Extensive model analyses such as mutant screening or parameter sensitivity can also be performed. For all these tasks, results are stored and can be subsequently filtered to look for specific outputs. This web interface can be accessed at the address: https://maboss.curie.fr/webmaboss/ and deployed locally using docker. This application is open-source under LGPL license, and available at https://github.com/sysbio-curie/WebMaBoSS.

On identifiability and order of continuous-time aggregated Markov chains, Markov-modulated Poisson processes, and phase-type distributions

1996 ◽  
Vol 33 (3) ◽  
pp. 640-653 ◽  
Author(s):  
Tobias Rydén
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Continuous Time ◽  
Poisson Processes ◽  
Hitting Times ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Minimum Number ◽  
Markov Modulated ◽  
Two Parameters

An aggregated Markov chain is a Markov chain for which some states cannot be distinguished from each other by the observer. In this paper we consider the identifiability problem for such processes in continuous time, i.e. the problem of determining whether two parameters induce identical laws for the observable process or not. We also study the order of a continuous-time aggregated Markov chain, which is the minimum number of states needed to represent it. In particular, we give a lower bound on the order. As a by-product, we obtain results of this kind also for Markov-modulated Poisson processes, i.e. doubly stochastic Poisson processes whose intensities are directed by continuous-time Markov chains, and phase-type distributions, which are hitting times in finite-state Markov chains.

Wave Setup in Inlets: Some Practical Considerations

2007 ◽  
Author(s):  
Dale Kerper ◽  
Christian M. Appendini ◽  
Henrik Kofoed-Hansen ◽  
Ida Bro̸ker
Keyword(s):  
Numerical Models ◽  
Current Model ◽  
Wave Model ◽  
Total Water ◽  
Wave Runup ◽  
Modeling Tools ◽  
Mean Sea Level ◽  
Wave Setup ◽  
Astronomical Tide ◽  
Total Water Level

For the determination maximum flood elevations, a number of components contributing to the total water level need to be considered. For instance, astronomical tide, storm surge, relative changes in mean sea level, wave setup, wave runup and wave splash. In this study, numerical models were used to evaluate under which conditions wave setup penetrates into an idealized inlet. A number of idealized inlet/lagoon configurations were tested. A coupled wave-current model was used to assess the static component of the wave setup. A Boussinesq wave model was used to assess the influence of the dynamic oscillating component of the wave setup. This study demonstrates how numerical modeling tools can be effectively used to assess how wave setup develops depending on a specific inlet configuration.

scholarly journals On the steady state of continuous-time stochastic opinion dynamics with power-law confidence

2021 ◽  
Vol 58 (3) ◽  
pp. 746-772
Author(s):  
François Baccelli ◽  
Sriram Vishwanath ◽  
Jae Oh Woo
Keyword(s):  
Continuous Time ◽  
Mean Field ◽  
Opinion Dynamics ◽  
Dynamics Model ◽  
Negative Power ◽  
State Dependent ◽  
Quantitative Results ◽  
Non Local ◽  
Model Features ◽  
Dependent Interaction

AbstractThis paper introduces a non-linear and continuous-time opinion dynamics model with additive noise and state-dependent interaction rates between agents. The model features interaction rates which are proportional to a negative power of the opinion distances. We establish a non-local partial differential equation for the distribution of opinion distances and use Mellin transforms to provide an explicit formula for the stationary solution of the latter, when it exists. Our approach leads to new qualitative and quantitative results on this type of dynamics. To the best of our knowledge these Mellin transform results are the first quantitative results on the equilibria of opinion dynamics with distance-dependent interaction rates. The closed-form expressions for this class of dynamics are obtained for the two-agent case. However, the results can be used in mean-field models featuring several agents whose interaction rates depend on the empirical average of their opinions. The technique also applies to linear dynamics, namely with a constant interaction rate, on an interaction graph.

scholarly journals Continuous Time Bayesian Networks in Prognosis and Health Management of Centrifugal Pumps

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Tyler Forrester ◽  
Mark Harris ◽  
Jacob Senecal ◽  
John Sheppard
Keyword(s):  
Continuous Time ◽  
Graphical Model ◽  
Health Management ◽  
Fault Tree Analysis ◽  
Centrifugal Pumps ◽  
Modeling Tools ◽  
Pump System ◽  
Novel Method ◽  
Continuous Time Bayesian Network ◽  
The Impact

This paper presents a novel method for performing risk-based prognosis and health management (rPHM) on centrifugal pumps. We present the rPHM framework and apply common modeling tools used in reliability and testability analysis---dependency (D) matrices and fault tree analysis---as a basis for constructing an underlying predictive model. We then introduce the mathematics of the Continuous Time Bayesian Network (CTBN), which is a probabilistic graphical model based on a factored Markov process that is designed to capture system evolution through time, and we explain how to apply a CTBN derived from D-matrices and fault trees to consider the impact of a set of faults common to centrifugal pumps on emerging hazards in the pump system. We demonstrate the utility of using CTBNs for rPHM analysis with two experiments showing the descriptive power of our modeling approach.

STOCHASTIC APPROACH TO DIVIDEND EQUALIZATION FUND MODELLING AND SOLVENCY

2005 ◽  
Vol 15 (12) ◽  
pp. 1795-1810 ◽  
Author(s):  
M. A. PETERSEN ◽  
H. RAUBENHEIMER ◽  
M. VAN DER WALT
Keyword(s):  
Stochastic Models ◽  
Continuous Time ◽  
Stochastic Approach ◽  
Insurance Companies ◽  
Dividend Payment ◽  
Related Notion ◽  
The World ◽  
Net Assets ◽  
Equalization Fund ◽  
Solvency Ratio

In this contribution, the nonlinear dynamics of the surplus (net assets or reserve) process for a dividend-distributing company is studied in conjunction with the dynamics of its dividend equalization fund. The latter type of fund is maintained by leading insurance companies throughout the world and pays a special dividend for income that the investors lost because the dividend payment process was adversely affected for some or other reason. In our paper, a stochastic model for the related notion of a dividend equalization solvency ratio is derived. The ambient value of this ratio is an indication of the capacity of the insurer to pay dividends to shareholders especially when profit is low. The aforementioned analysis is, in turn, based on the construction of continuous time stochastic models for the dynamics of the surplus and total liabilities processes of an insurer. The discussions are reliant on principles arising within the asset-liability modelling paradigm.

scholarly journals Exact calculation of stationary solution and parameter sensitivity analysis of stochastic continuous time Boolean models

2019 ◽  
Author(s):  
Mihály Koltai ◽  
Vincent Noel ◽  
Andrei Zinovyev ◽  
Laurence Calzone ◽  
Emmanuel Barillot
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Calculation Method ◽  
Continuous Time ◽  
Stationary Solutions ◽  
Supplementary Information ◽  
Exact Calculation ◽  
Transition Rates ◽  
Boolean Models ◽  
Matlab Package

AbstractMotivationSolutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all asymptotic solutions. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways that have not been analyzed yet in the literature. These two fundamental uncertainties call for an exact calculation method for this class of models.ResultsWe show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses an exact matrix calculation method previously applied in the context of chemical kinetics. Using this approach, we also analyze the under-explored question of the effect of transition rates on the stationary solutions and show the latter can be sensitive to parameter changes. The analysis distinguishes processes that are robust or, alternatively, sensitive to parameter values, providing both methodological and biological [email protected] or [email protected] informationSupplementary data available at bioRxiv online.Availability and implementationThe calculation method described in the article is available as the ExaStoLog MATLAB package on GitHub at https://github.com/sysbio-curie/exact-stoch-log-mod

scholarly journals Exact solving and sensitivity analysis of stochastic continuous time Boolean models

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Mihály Koltai ◽  
Vincent Noel ◽  
Andrei Zinovyev ◽  
Laurence Calzone ◽  
Emmanuel Barillot
Keyword(s):  
Sensitivity Analysis ◽  
Continuous Time ◽  
Boolean Models

On identifiability and order of continuous-time aggregated Markov chains, Markov-modulated Poisson processes, and phase-type distributions

1996 ◽  
Vol 33 (03) ◽  
pp. 640-653 ◽  
Author(s):  
Tobias Rydén
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Continuous Time ◽  
Poisson Processes ◽  
Hitting Times ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Minimum Number ◽  
Markov Modulated ◽  
Two Parameters

An aggregated Markov chain is a Markov chain for which some states cannot be distinguished from each other by the observer. In this paper we consider the identifiability problem for such processes in continuous time, i.e. the problem of determining whether two parameters induce identical laws for the observable process or not. We also study the order of a continuous-time aggregated Markov chain, which is the minimum number of states needed to represent it. In particular, we give a lower bound on the order. As a by-product, we obtain results of this kind also for Markov-modulated Poisson processes, i.e. doubly stochastic Poisson processes whose intensities are directed by continuous-time Markov chains, and phase-type distributions, which are hitting times in finite-state Markov chains.

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