stochastic dynamics Dotaz Zobrazit nápovědu
The classical definition of evolutionary stability assumes that the fitness of each phenotype is fully determined by the composition of phenotypes in the population and by the strategies of each of these phenotypes. In natural populations, however, stochasticity often plays a crucial role in determining the fitness of an individual and a deterministic fitness function is probably rather rare. For example, choices of a new host plant, prey or oviposition patch are completely stochastic processes. Here we introduce a new definition of ESS that takes into account the effect of stochasticity on individual fitness. Then we show an application of this definition in a realistic system.
- MeSH
- biologická evoluce * MeSH
- fenotyp MeSH
- hmyz genetika MeSH
- modely genetické MeSH
- náhodné rozdělení MeSH
- nelineární dynamika MeSH
- predátorské chování MeSH
- selekce (genetika) * MeSH
- statistické modely MeSH
- stochastické procesy * MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
In the human, placental structure is closely related to placental function and consequent pregnancy outcome. Studies have noted abnormal placental shape in small-for-gestational-age infants which extends to increased lifetime risk of cardiovascular disease. The origins and determinants of placental shape are incompletely understood and are difficult to study in vivo. In this paper, we model the early development of the human placenta, based on the hypothesis that this is driven by a chemoattractant effect emanating from proximal spiral arteries in the decidua. We derive and explore a two-dimensional stochastic model, and investigate the effects of loss of spiral arteries in regions near to the cord insertion on the shape of the placenta. This model demonstrates that disruption of spiral arteries can exert profound effects on placental shape, particularly if this is close to the cord insertion. Thus, placental shape reflects the underlying maternal vascular bed. Abnormal placental shape may reflect an abnormal uterine environment, predisposing to pregnancy complications. Through statistical analysis of model placentas, we are able to characterize the probability that a given placenta grew in a disrupted environment, and even able to distinguish between different disruptions.
- Klíčová slova
- mathematical modelling, placental development, placental shape, spiral artery, stochastic dynamics,
- MeSH
- arteriae umbilicales fyziologie MeSH
- biologické modely * MeSH
- embryonální vývoj fyziologie MeSH
- fyziologická neovaskularizace fyziologie MeSH
- kyslík metabolismus MeSH
- lidé MeSH
- organogeneze fyziologie MeSH
- placenta embryologie MeSH
- placentace * MeSH
- počítačová simulace MeSH
- statistické modely MeSH
- stochastické procesy MeSH
- těhotenství MeSH
- Check Tag
- lidé MeSH
- těhotenství MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- kyslík MeSH
The authors describe a hybrid computer neurone model intended for the investigation of stochastic transformations effected by neurones in correlation to some of their physiological parameters. The model is designed so as to give the best possible characterization of the internal dynamics of a neurone with minimum limiting conditions. It allows the generation of suitable input stochastic processes or operates with input processes obtained experimentally in the living neurone and it carries out basic statistical tests of the output stochastic process.
- MeSH
- hybridní počítače * MeSH
- lidé MeSH
- modely neurologické * MeSH
- neurony fyziologie MeSH
- pravděpodobnost * MeSH
- stochastické procesy * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
We propose a new framework for rigorous robustness analysis of stochastic biochemical systems that is based on probabilistic model checking techniques. We adapt the general definition of robustness introduced by Kitano to the class of stochastic systems modelled as continuous time Markov Chains in order to extensively analyse and compare robustness of biological models with uncertain parameters. The framework utilises novel computational methods that enable to effectively evaluate the robustness of models with respect to quantitative temporal properties and parameters such as reaction rate constants and initial conditions. We have applied the framework to gene regulation as an example of a central biological mechanism where intrinsic and extrinsic stochasticity plays crucial role due to low numbers of DNA and RNA molecules. Using our methods we have obtained a comprehensive and precise analysis of stochastic dynamics under parameter uncertainty. Furthermore, we apply our framework to compare several variants of two-component signalling networks from the perspective of robustness with respect to intrinsic noise caused by low populations of signalling components. We have successfully extended previous studies performed on deterministic models (ODE) and showed that stochasticity may significantly affect obtained predictions. Our case studies demonstrate that the framework can provide deeper insight into the role of key parameters in maintaining the system functionality and thus it significantly contributes to formal methods in computational systems biology.
This paper presents a comparative analysis of deterministic and stochastic computational modeling approaches for the optimal control of COVID-19. We formulate a compartmental epidemic model with perturbation by white noise that incorporates various factors influencing disease transmission. By incorporating stochastic effects, the model accounts for uncertainties inherent in real-world epidemic data. We establish the mathematical properties of the model, such as well-posedness and the existence of stationary distributions, which are crucial for understanding long-term epidemic dynamics. Moreover, the study presents an optimal control strategies to mitigate the epidemic's impact, both in deterministic and stochastic sceneries. Reported data from Algeria are used to parameterize the model, ensuring its relevance and applicability to practical satiation. Through numerical simulations, the study provides insights into the effectiveness of different control measures in managing COVID-19 outbreaks. This research contributes to advancing our understanding of epidemic dynamics and informs decision-making processes for epidemic controlling interventions.
- Klíčová slova
- COVID-19 stochastic modeling, Extinction, Simulation, Stationary distribution, Stochastic optimized control,
- MeSH
- COVID-19 * epidemiologie prevence a kontrola přenos MeSH
- lidé MeSH
- počítačová simulace MeSH
- SARS-CoV-2 MeSH
- stochastické procesy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- srovnávací studie MeSH
The recurrence plot method is described together with its application to the analysis of inner time relations in the sequences of interspike intervals. The depicted method was employed to analyze results from the computer model of the neuronal stochastic activity. It can be also used to analyze sequences of interspike intervals in the records from life neurons.
Signatures of chemical exchange and spectral diffusion in 2D photon-echo line shapes of molecular aggregates are studied using model calculations for a dimer whose Hamiltonian parameters are stochastically modulated. Cross peaks induced by chemical exchange and by exciton transport have different dynamics and distinguish two models which have the same absorption spectrum (a two-state jump bath modulation model of a dimer and a four-state jump bath model of a single chromophore). Slow Gaussian-Markovian spectral diffusion of a symmetric dimer induces new peaks which are damped as the dipole moment is equilibrated. These effects require an explicit treatment of the bath and may not be described by lower-level theories such as the Redfield equations, which eliminate the bath.
We study a stochastic version of the dynamical Casimir effect, computing the particle creation inside a cavity produced by a random motion of one of its walls. We first present a calculation perturbative in the amplitude of the motion. We compare the stochastic particle creation with the deterministic counterpart. Then, we go beyond the perturbative evaluation using a stochastic version of the multiple scale analysis, that takes into account stochastic parametric resonance. We stress the relevance of the coupling between the different modes induced by the stochastic motion. In the single-mode approximation, the equations are formally analogous to those that describe the stochastic particle creation in a cosmological context, that we rederive using multiple scale analysis.
- Klíčová slova
- cosmology, dynamical Casimir effect, stochastic particle creation,
- Publikační typ
- časopisecké články MeSH
Cell cycle is controlled by the activity of protein family of cyclins and cyclin-dependent kinases that are periodically expressed during cell cycle and that are conserved among different species. Genome-wide location analysis found that cyclins are controlled by a small number of transcription factors that form closed network of genes controlling each other. To investigate gene expression dynamics of this network, we developed a general procedure for stochastic simulation of gene expression process. Using the binding data, we simulated gene expression of all genes of the network for all possible combinations of regulatory interactions and by statistical comparison with experimentally measured time series excluded those interactions that formed gene expression temporal profiles significantly different from the measured ones. These experiments led to a new definition of the cyclins regulatory network coherent with the binding experiments which are kinetically plausible. Level of influence of individual regulators in control of the regulated genes is defined. Simulation results indicate particular mechanism of regulatory activity of protein complexes involved in the control of cyclins.
- MeSH
- cyklin-dependentní kinasy genetika MeSH
- cykliny biosyntéza genetika MeSH
- genetická transkripce MeSH
- genové regulační sítě * MeSH
- regulace genové exprese u hub * MeSH
- Saccharomyces cerevisiae genetika MeSH
- stochastické procesy MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- cyklin-dependentní kinasy MeSH
- cykliny MeSH
A new approach to computer modelling of neuronal stochastic activity is described. The output dynamic activity which depends on the types and the number of input synapses, weights of the synaptic efficacy, the absolute refractory phase duration and threshold level is evaluated on this model in some types of Gaussian input processes. The behaviour of this model for one excitatory and one inhibitory synapse is described in dependence on the changes of excitation weight. The neuronal behaviour presented depends on the number of interspike intervals and the excitation weight and interspike interval density distribution. A novel concept of the e-curve is being introduced, which shows the dependence of the number of output interspike intervals on the weight of excitation on a stable inhibition level, the absolute refractory phase value and the threshold level. The properties of e-curves are discussed. Furthermore, examples of transformations of input stochastic processes are mentioned from the aspect of density distribution changes of interspike intervals.
- MeSH
- elektrofyziologie * MeSH
- membránové potenciály fyziologie MeSH
- modely neurologické MeSH
- neurony fyziologie MeSH
- počítačová simulace MeSH
- Poissonovo rozdělení MeSH
- refrakterní doba elektrofyziologická fyziologie MeSH
- stochastické procesy MeSH
- synapse fyziologie MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
