On Robustness Computation and Optimization in BIOCHAM-4

Fages, François; Soliman, Sylvain

doi:10.1007/978-3-319-99429-1_18

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…Both models make biological sense. Another approach to studying epidemic models is to consider them as chemical reactions, as shown, for example, in [16]. As an illustration, the reaction between an infective and a susceptible produces two infectives.…”

Section: Sis Sir and Sirs Modelsmentioning

confidence: 99%

SIRS Epidemic Models with Delays, Partial and Temporary Immunity and Vaccination

Chen-Charpentier

2024

AppliedMath

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The basic reproduction, or reproductive number, is a useful index that indicates whether or not there will be an epidemic. However, it is also very important to determine whether an epidemic will eventually decrease and disappear or persist as an endemic. Different infectious diseases have different behaviors and mathematical models used to simulated them should capture the most important processes; however, the models also involve simplifications. Influenza epidemics are usually short-lived and can be modeled with ordinary differential equations without considering demographics. Delays such as the infection time can change the behavior of the solutions. The same is true if there is permanent or temporary immunity, or complete or partial immunity. Vaccination, isolation and the use of antivirals can also change the outcome. In this paper, we introduce several new models and use them to find the effects of all the above factors paying special attention to whether the model can represent an infectious process that eventually disappears. We determine the equilibrium solutions and establish the stability of the disease-free equilibrium using various methods. We also show that many models of influenza or other epidemics with a short duration do not have solutions with a disappearing epidemic. The main objective of the paper is to introduce different ways of modeling immunity in epidemic models. Several scenarios with different immunities are studied since a person may not be re-infected because he/she has total or partial immunity or because there were no close contacts. We show that some relatively small changes, such as in the vaccination rate, can significantly change the dynamics; for example, the existence and number of the disease-free equilibria. We also illustrate that while introducing delays makes the models more realistic, the dynamics have the same qualitative behavior.

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Section: Sis Sir and Sirs Modelsmentioning

confidence: 99%

SIRS Epidemic Models with Delays, Partial and Temporary Immunity and Vaccination

Chen-Charpentier

2024

AppliedMath

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“…The BIOCHAM system [170] is a modelling and analysis platform for rule-based systems. The tool allows modelling biochemical systems, simulating Boolean, differential and stochastic models, and verifying biological properties as well as 'developing, correcting, completing, and coupling models'.…”

Section: Play-enginementioning

confidence: 99%

“…KPW orkbench ’s verification component, supporting both ltl and ctl properties, allows the verification of kP models using the S pin and N u SMV model checkers [168,169]. In order to facilitate the formal specification, KPW orkbench integrates a property language using natural language statements, from which the S pin and N u SMV formulae are translated automatically.The B iocham system [170] is a modelling and analysis platform for rule-based systems. The tool allows modelling biochemical systems, simulating Boolean, differential and stochastic models, and verifying biological properties as well as ‘developing, correcting, completing, and coupling models’.…”

Section: Software Tools Used In Verifying Biological and Biochemical ...mentioning

confidence: 99%

Verifiable biology

Konur

Gheorghe

Krasnogor

2023

J. R. Soc. Interface.

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The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.

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“…Life Sciences typically deal with and generate large amounts of data, e.g., the flux of terabytes about genes and their expression produced by state of the art sequencing and microarray equipment, or data relating to the dynamics of cell biochemistry or organ functionality. Some modelling and simulation techniques require the investigation of large numbers of different (virtual) experiments, e.g., those addressing probabilistic, noise and robustness aspects [80][81][82][83][84], or based on statistical approaches. Curation and mining of large, typically multimedia, medical datasets for therapeutic and analytics purposes, are computationally expensive.…”

Section: Hpc-enabled Modelling and Simulation For Life Sciencesmentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

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Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned.

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On Robustness Computation and Optimization in BIOCHAM-4

Cited by 8 publications

References 18 publications

SIRS Epidemic Models with Delays, Partial and Temporary Immunity and Vaccination

SIRS Epidemic Models with Delays, Partial and Temporary Immunity and Vaccination

Verifiable biology

Why High-Performance Modelling and Simulation for Big Data Applications Matters

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