Dynamical Systems and Stochastic Programming: To Ordinary Differential Equations and Back

DOI10.1007/978-3-642-04186-0_11zbMath1260.92020OpenAlexW1867777259MaRDI QIDQ3642958

Luca Bortolussi, Alberto Policriti

Publication date: 10 November 2009

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-04186-0_11

Mathematics Subject Classification ID

Stochastic programming (90C15) Biochemistry, molecular biology (92C40) Models and methods for concurrent and distributed computing (process algebras, bisimulation, transition nets, etc.) (68Q85) Qualitative investigation and simulation of ordinary differential equation models (34C60) Systems biology, networks (92C42)

Related Items (5)

Checking Individual Agent Behaviours in Markov Population Models by Fluid Approximation ⋮ Exact fluid lumpability in Markovian process algebra ⋮ Mean-Field Limits Beyond Ordinary Differential Equations ⋮ Model checking single agent behaviours by fluid approximation ⋮ Hybrid behaviour of Markov population models

Uses Software

• PRISM
• Bio-PEPA
• Cellerator
• Dizzy

Cites Work

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• On process rate semantics
• Stochastic Concurrent Constraint Programming and Differential Equations
• Bio-PEPA: An Extension of the Process Algebra PEPA for Biochemical Networks
• Stochastic Programs and Hybrid Automata for (Biological) Modeling
• Markov Chains
• Stochastic Petri Nets
• The Importance of Being (A Little Bit) Discrete
• On the Approximation of Stochastic Concurrent Constraint Programming by Master Equation
• A Compositional Approach to Performance Modelling
• Solutions of ordinary differential equations as limits of pure jump markov processes
• Limit theorems for sequences of jump Markov processes approximating ordinary differential processes
• Transactions on Computational Systems Biology IV

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