scholarly journals Control mechanisms for stochastic biochemical systems via computation of reachable sets

2016 ◽  
Author(s):  
Eszter Lakatos ◽  
Michael P.H. Stumpf
Keyword(s):  
Synthetic Biology ◽  
Parameter Uncertainty ◽  
Control Measures ◽  
Model Parameters ◽  
Control Mechanisms ◽  
Molecular Processes ◽  
Biochemical Systems ◽  
Mrna And Protein Expression ◽  
Engineered Systems ◽  
The Impact

AbstractControlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently non-linear. We present an approach to studying the impact of control measures on motifs of molecular interactions, that addresses the problems faced in biological systems: stochasticity, parameter uncertainty, and non-linearity. We show that our reachability analysis formalism can describe the potential behaviour of biological (naturally evolved as well as engineered) systems, and provides a set of bounds on their dynamics at the level of population statistics: for example, we can obtain the possible ranges of means and variances of mRNA and protein expression levels, even in the presence of uncertainty about model parameters.

scholarly journals Control mechanisms for stochastic biochemical systems via computation of reachable sets

2017 ◽  
Vol 4 (8) ◽  
pp. 160790 ◽  
Author(s):  
Eszter Lakatos ◽  
Michael P. H. Stumpf
Keyword(s):  
Synthetic Biology ◽  
Parameter Uncertainty ◽  
Control Measures ◽  
Model Parameters ◽  
Control Mechanisms ◽  
Molecular Processes ◽  
Biochemical Systems ◽  
Mrna And Protein Expression ◽  
Engineered Systems ◽  
The Impact

Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently nonlinear. We present an approach to studying the impact of control measures on motifs of molecular interactions that addresses the problems faced in many biological systems: stochasticity, parameter uncertainty and nonlinearity. We show that our reachability analysis formalism can describe the potential behaviour of biological (naturally evolved as well as engineered) systems, and provides a set of bounds on their dynamics at the level of population statistics: for example, we can obtain the possible ranges of means and variances of mRNA and protein expression levels, even in the presence of uncertainty about model parameters.

Modelling the Impact of Administrative Access Controls on Technical Access Control Measures

2017 ◽  
Vol 30 (4) ◽  
pp. 53-70
Author(s):  
Winfred Yaokumah
Keyword(s):  
Access Control ◽  
Structural Equation ◽  
Partial Least Square ◽  
Least Square ◽  
Control Measures ◽  
Control Mechanisms ◽  
Control Policies ◽  
Access Control Policies ◽  
Access Controls ◽  
The Impact

Almost all computing systems and applications in organizations include some form of access control mechanisms. Managing secure access to computing resources is an important but a challenging task, requiring both administrative and technical measures. This study examines the influence of administrative access control measures on technical access control mechanisms. Based on the four access control clauses defined by ISO/IEC27002, this study develops a model to empirically test the impact of access control policies on systems and applications control activities. The study employs Partial Least Square Structural Equation Modelling (PLS-SEM) to analyze data collected from 223 samples through a survey questionnaire. The results show that the greatest significant impact on applications and systems access control measures is through access control policies mediated by users' responsibilities and accountability and user access management activities. But the direct impact of access control policies on applications and systems access control measures is not significant.

scholarly journals Agent-based modelling of complex factors impacting malaria prevalence

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Miracle Amadi ◽  
Anna Shcherbacheva ◽  
Heikki Haario
Keyword(s):  
Malaria Transmission ◽  
Field Data ◽  
Control Measures ◽  
Model Parameters ◽  
Economic Factors ◽  
Agent Based ◽  
Wide Range ◽  
The Impact ◽  
Socio Economic Factors

Abstract Background Increasingly complex models have been developed to characterize the transmission dynamics of malaria. The multiplicity of malaria transmission factors calls for a realistic modelling approach that incorporates various complex factors such as the effect of control measures, behavioural impacts of the parasites to the vector, or socio-economic variables. Indeed, the crucial impact of household size in eliminating malaria has been emphasized in previous studies. However, increasing complexity also increases the difficulty of calibrating model parameters. Moreover, despite the availability of much field data, a common pitfall in malaria transmission modelling is to obtain data that could be directly used for model calibration. Methods In this work, an approach that provides a way to combine in situ field data with the parameters of malaria transmission models is presented. This is achieved by agent-based stochastic simulations, initially calibrated with hut-level experimental data. The simulation results provide synthetic data for regression analysis that enable the calibration of key parameters of classical models, such as biting rates and vector mortality. In lieu of developing complex dynamical models, the approach is demonstrated using most classical malaria models, but with the model parameters calibrated to account for such complex factors. The performance of the approach is tested against a wide range of field data for Entomological Inoculation Rate (EIR) values. Results The overall transmission characteristics can be estimated by including various features that impact EIR and malaria incidence, for instance by reducing the mosquito–human contact rates and increasing the mortality through control measures or socio-economic factors. Conclusion Complex phenomena such as the impact of the coverage of the population with long-lasting insecticidal nets (LLINs), changes in behaviour of the infected vector and the impact of socio-economic factors can be included in continuous level modelling. Though the present work should be interpreted as a proof of concept, based on one set of field data only, certain interesting conclusions can already be drawn. While the present work focuses on malaria, the computational approach is generic, and can be applied to other cases where suitable in situ data is available.

scholarly journals Mathematical Approach to Investigate Stress due to Control Measures to Curb COVID-19

2022 ◽  
Vol 2022 ◽  
pp. 1-23
Author(s):  
James Nicodemus Paul ◽  
Silas Steven Mirau ◽  
Isambi Sailon Mbalawata
Keyword(s):  
Equilibrium Points ◽  
Rank Correlation ◽  
The Body ◽  
Endemic Equilibrium ◽  
Least Square ◽  
Control Measures ◽  
Model Parameters ◽  
Asymptotically Stable ◽  
Globally Asymptotically Stable ◽  
The Impact

COVID-19 is a world pandemic that has affected and continues to affect the social lives of people. Due to its social and economic impact, different countries imposed preventive measures that are aimed at reducing the transmission of the disease. Such control measures include physical distancing, quarantine, hand-washing, travel and boarder restrictions, lockdown, and the use of hand sanitizers. Quarantine, out of the aforementioned control measures, is considered to be more stressful for people to manage. When people are stressed, their body immunity becomes weak, which leads to multiplying of coronavirus within the body. Therefore, a mathematical model consisting of six compartments, Susceptible-Exposed-Quarantine-Infectious-Hospitalized-Recovered (SEQIHR) was developed, aimed at showing the impact of stress on the transmission of COVID-19 disease. From the model formulated, the positivity, bounded region, existence, uniqueness of the solution, the model existence of free and endemic equilibrium points, and local and global stability were theoretically proved. The basic reproduction number ( R 0 ) was derived by using the next-generation matrix method, which shows that, when R 0 < 1 , the disease-free equilibrium is globally asymptotically stable whereas when R 0 > 1 the endemic equilibrium is globally asymptotically stable. Moreover, the Partial Rank Correlation Coefficient (PRCC) method was used to study the correlation between model parameters and R 0 . Numerically, the SEQIHR model was solved by using the Rung-Kutta fourth-order method, while the least square method was used for parameter identifiability. Furthermore, graphical presentation revealed that when the mental health of an individual is good, the body immunity becomes strong and hence minimizes the infection. Conclusively, the control parameters have a significant impact in reducing the transmission of COVID-19.

scholarly journals Accounting for regional transmission variability and the impact of malaria control interventions in Ghana: A population level mathematical modelling approach.

2020 ◽  
Author(s):  
Timothy Awine ◽  
Sheetal P Silal
Keyword(s):  
Malaria Control ◽  
Malaria Incidence ◽  
Protective Efficacy ◽  
Indoor Residual Spraying ◽  
Information Management System ◽  
Control Measures ◽  
Bed Nets ◽  
Model Parameters ◽  
Insecticide Treated Bed Nets ◽  
The Impact

Abstract Background Assessing the effectiveness of malaria control measures in Ghana will require taking transmission dynamics of the disease into account given the influence of climate variability in the region of interest. The impact of preventative interventions on malaria incidence and the prospects of meeting program timelines in Ghana have been investigated using mathematical models based on regionally diverse climatic zones. Methods An ordinary non-linear differential equation model with its associated rate parameters was developed incorporating the transitions between various disease compartments for three ecological zones in Ghana. Model parameters were estimated using data captured on the District Health Information Management System in Ghana from 2008 to 2017.The impact of insecticide treated bed nets and indoor residual spraying on the incidence of malaria were simulated at various levels of coverage and protective effectiveness in each ecological zone. To fit the model, Approximate Bayesian Computational sampling approach was adopted. Results Increasing the coverage levels of both long lasting insecticide treated bed nets or indoor residual spraying activities without a corresponding increase in their proper use or patronage does not impact highly on averting predicted incidence of malaria in Ghana. Improving on the protective efficacy of long lasting insecticide treated bed nets through proper usage could lead to substantial reductions in the predicted incidence of malaria. Similar results were obtained with indoor residual spraying across all zones. Conclusions Projected goals set in the National Strategic plan for malaria control 2014-2020 as well as WHO targets for malaria pre-elimination by 2030 are only likely be achieved if a substantial improvement in treated bed net usage is achieved coupled with targeted deployment of indoor residual spraying with high efficacy.

scholarly journals Accounting for regional transmission variability and the impact of malaria control interventions in Ghana: A population level mathematical modelling approach.

2020 ◽  
Author(s):  
Timothy Awine ◽  
Sheetal P Silal
Keyword(s):  
Malaria Control ◽  
Malaria Incidence ◽  
Protective Efficacy ◽  
Indoor Residual Spraying ◽  
Information Management System ◽  
Control Measures ◽  
Bed Nets ◽  
Model Parameters ◽  
Insecticide Treated Bed Nets ◽  
The Impact

Abstract Background Assessing the effectiveness of malaria control measures in Ghana will require taking transmission dynamics of the disease into account given the influence of climate variability in the region of interest. The impact of preventative interventions on malaria incidence and the prospects of meeting program timelines in Ghana were investigated using mathematical models based on regionally diverse climatic zones. Methods An ordinary non-linear differential equation models with their associated rate parameters were developed incorporating the transitions between various disease compartments for three ecological zones in Ghana. Models were fitted using data from the District Health Information Management System in Ghana from 2008 to 2017 and historical intervention coverage levels. To calibrate the models, Approximate Bayesian Computational sampling approach with a distance based rejection criteria was adopted. A leave-one-out approach was used to validate model parameters and the most sensitive evaluated using a multivariate regression sensitivity analysis. The impact of insecticide treated bed nets and their usage and indoor residual spraying as well as their protective efficacy on the incidence of malaria were simulated at various levels of coverage and protective effectiveness in each ecological zone to investigate the prospects of achieving goals of the malaria control strategy for 2014-2020. Results Increasing the coverage levels of both long lasting insecticide treated bed nets and indoor residual spraying activities without a corresponding increase in their recommended usage does not impact highly on averting predicted incidence of malaria. Improving upon the protective efficacy of long lasting insecticide treated bed nets through proper usage could lead to substantial reductions in the predicted incidence of malaria. Similar results were obtained with indoor residual spraying across all zones.Conclusions Projected goals set in the national strategic plan for malaria control 2014-2020 as well as WHO targets for malaria pre-elimination by 2030 are only likely to be achieved if a substantial improvement in treated bed net usage is achieved coupled with targeted deployment of indoor residual spraying with high community acceptability and efficacy. Key words: model, malaria, interventions, long lasting insecticide bednets, indoor residual spraying

ECONOMIC SCENARIO GENERATOR AND PARAMETER UNCERTAINTY: A BAYESIAN APPROACH

2019 ◽  
Vol 49 (2) ◽  
pp. 335-372
Author(s):  
Jean-François Bégin
Keyword(s):  
Parameter Uncertainty ◽  
Inflation Rate ◽  
Risk Measures ◽  
Model Parameters ◽  
The Us ◽  
Study Parameter ◽  
Consistent Manner ◽  
The Right ◽  
The Impact

AbstractIn this article, we study parameter uncertainty and its actuarial implications in the context of economic scenario generators. To account for this additional source of uncertainty in a consistent manner, we cast Wilkie’s four-factor framework into a Bayesian model. The posterior distribution of the model parameters is estimated using Markov chain Monte Carlo methods and is used to perform Bayesian predictions on the future values of the inflation rate, the dividend yield, the dividend index return and the long-term interest rate. According to the US data, parameter uncertainty has a significant impact on the dispersion of the four economic variables of Wilkie’s framework. The impact of such parameter uncertainty is then assessed for a portfolio of annuities: the right tail of the loss distribution is significantly heavier when parameters are assumed random and when this uncertainty is estimated in a consistent manner. The risk measures on the loss variable computed with parameter uncertainty are at least 12% larger than their deterministic counterparts.

scholarly journals The impact of model and rainfall forcing errors on characterizing soil moisture uncertainty in land surface modeling

2012 ◽  
Vol 16 (10) ◽  
pp. 3499-3515 ◽  
Author(s):  
V. Maggioni ◽  
E. N. Anagnostou ◽  
R. H. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Parameter Uncertainty ◽  
Land Surface ◽  
Accurate Estimation ◽  
Surface Model ◽  
Model Parameters ◽  
Model Parameter ◽  
Parameter Perturbations ◽  
The Impact

Abstract. The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

scholarly journals Parametrization consequences of constraining soil organic matter models by total carbon and radiocarbon using long-term field data

2016 ◽  
Author(s):  
L. Menichetti ◽  
T. Kätterer ◽  
J. Leifeld
Keyword(s):  
Data Streams ◽  
Parameter Uncertainty ◽  
Total Carbon ◽  
Model Parameters ◽  
Model Structure ◽  
Relative Importance ◽  
Organic C ◽  
Control Feedback ◽  
The Impact ◽  
Model Structures

Abstract. Soil organic carbon (SOC) dynamics result from different interacting processes and controls on spatial scales from sub-aggregate to pedon to the whole ecosystem. These complex dynamics are translated into models as abundant degrees of freedom. This high number of not directly measurable variables and, on the other hand, very limited data at disposal result in equifinality and parameter uncertainty. Carbon radioisotope measurements are a proxy for SOC age both at annual to decadal (bomb peak based) and centennial to millennial time scales (radio decay based), and thus can be used in addition to total organic C for constraining SOC models. By considering this additional information, uncertainties in model structure and parameters may be reduced. To test this hypothesis we studied SOC dynamics and their defining kinetic parameters in the ZOFE experiment, a >60-years old controlled cropland experiment in Switzerland, by utilising SOC and SO14C time-series. To represent different processes we applied five model structures, all stemming from a simple mother model (ICBM): I) two decomposing pools, II) an inert pool added, III) three decomposing pools, IV) two decomposing pools with a substrate control feedback on decomposition, V) as IV but with also an inert pool. These structures were extended to explicitly represent total SOC and 14C pools. The use of different model structures allowed us to explore model structural uncertainty and the impact of 14C on kinet ic parameters. We considered parameter uncertainty by calibrating in a formal Bayesian framework. By varying the relative importance of total SOC and SO14C data in the calibration, we could quantify the effect of the information from these two data streams on estimated model parameters. The weighing of the two data streams was crucial for determining model outcomes, and we suggest including it in future modelling efforts whenever SO14C data are available. The measurements and all model structures indicated a dramatic decline in SOC in the ZOFE experiment after an initial land use change in 1949 from grass- to cropland, followed by a constant but smaller decline. According to all structures, the three treatments (control, mineral fertilizer, farmyard manure) we considered were still far from equilibrium. The estimates of mean residence time (MRT) of the C pools defined by our models were sensitive to the consideration of the SO14C data stream. Model structure had a smaller effect on estimated MRT, which ranged between 5.91 and 4.22 years and 78.93 and 98.85 years for young and old pool, respectively, for structures without substrate interactions. The simplest model structure performed the best according to information criteria, validating the idea that we still lack data for mechanistic SOC models. Although we could not exclude any of the considered processes possibly involved in SOC decomposition, it was not possible to discriminate their relative importance.

scholarly journals Parametrization consequences of constraining soil organic matter models by total carbon and radiocarbon using long-term field data

2016 ◽  
Vol 13 (10) ◽  
pp. 3003-3019 ◽  
Author(s):  
Lorenzo Menichetti ◽  
Thomas Kätterer ◽  
Jens Leifeld
Keyword(s):  
Kinetic Parameters ◽  
Data Streams ◽  
Parameter Uncertainty ◽  
Total Carbon ◽  
Model Parameters ◽  
Model Structure ◽  
Relative Importance ◽  
Control Feedback ◽  
The Impact ◽  
Model Structures

Abstract. Soil organic carbon (SOC) dynamics result from different interacting processes and controls on spatial scales from sub-aggregate to pedon to the whole ecosystem. These complex dynamics are translated into models as abundant degrees of freedom. This high number of not directly measurable variables and, on the other hand, very limited data at disposal result in equifinality and parameter uncertainty. Carbon radioisotope measurements are a proxy for SOC age both at annual to decadal (bomb peak based) and centennial to millennial timescales (radio decay based), and thus can be used in addition to total organic C for constraining SOC models. By considering this additional information, uncertainties in model structure and parameters may be reduced. To test this hypothesis we studied SOC dynamics and their defining kinetic parameters in the Zürich Organic Fertilization Experiment (ZOFE) experiment, a > 60-year-old controlled cropland experiment in Switzerland, by utilizing SOC and SO14C time series. To represent different processes we applied five model structures, all stemming from a simple mother model (Introductory Carbon Balance Model – ICBM): (I) two decomposing pools, (II) an inert pool added, (III) three decomposing pools, (IV) two decomposing pools with a substrate control feedback on decomposition, (V) as IV but with also an inert pool. These structures were extended to explicitly represent total SOC and 14C pools. The use of different model structures allowed us to explore model structural uncertainty and the impact of 14C on kinetic parameters. We considered parameter uncertainty by calibrating in a formal Bayesian framework. By varying the relative importance of total SOC and SO14C data in the calibration, we could quantify the effect of the information from these two data streams on estimated model parameters. The weighing of the two data streams was crucial for determining model outcomes, and we suggest including it in future modeling efforts whenever SO14C data are available. The measurements and all model structures indicated a dramatic decline in SOC in the ZOFE experiment after an initial land use change in 1949 from grass- to cropland, followed by a constant but smaller decline. According to all structures, the three treatments (control, mineral fertilizer, farmyard manure) we considered were still far from equilibrium. The estimates of mean residence time (MRT) of the C pools defined by our models were sensitive to the consideration of the SO14C data stream. Model structure had a smaller effect on estimated MRT, which ranged between 5.9 ± 0.1 and 4.2 ± 0.1 years and 78.9 ± 0.1 and 98.9 ± 0.1 years for young and old pools, respectively, for structures without substrate interactions. The simplest model structure performed the best according to information criteria, validating the idea that we still lack data for mechanistic SOC models. Although we could not exclude any of the considered processes possibly involved in SOC decomposition, it was not possible to discriminate their relative importance.

Sign in / Sign up

Export Citation Format

Share Document