Abstract

Biochemical reaction systems may be viewed as discrete event processes characterized by a number of states and state transitions. These systems may be modeled as state transition systems with transitions representing individual reaction events. Since they often involve a large number of interactions, it can be difficult to construct such a model for a system, and since the resulting state-level model can involve a huge number of states, model analysis can be difficult or impossible. Here, we describe methods for the high-level specification of a system using hypergraphs, for the automated generation of a state-level model from a high-level model, and for the exact reduction of a state-level model using information from the high-level model. Exact reduction is achieved through the automated application to the high-level model of the symmetry reduction technique and reduction by decomposition by independent subsystems, allowing potentially significant reductions without the need to generate a full model. The application of the method to biochemical reaction systems is illustrated by models describing a hypothetical ion-channel at several levels of complexity. The method allows for the reduction of the otherwise intractable example models to a manageable size.

Highlights

  • A system model aims to predict the integrated behavior of a number of interacting system components

  • The qualitative behavior of a biochemical network may be modeled as a labeled transition system (LTS), consisting of a set of states and labeled transitions between the states, where the transitions correspond to biochemical reaction events

  • The basic approach used by many of these tools to deal with exploding state-spaces is to define a high-level model (HLM) describing the reaction rules according to some high-level specification method (HLSM), and to use the HLM along with a kinetic Monte Carlo (KMC) algorithm to simulate sample trajectories of a reaction

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Summary

INTRODUCTION

A system model aims to predict the integrated behavior of a number of interacting system components. Of several tools designed for this purpose.[1–4] The basic approach used by many of these tools to deal with exploding state-spaces is to define a high-level model (HLM) describing the reaction rules according to some high-level specification method (HLSM), and to use the HLM along with a kinetic Monte Carlo (KMC) algorithm to simulate sample trajectories of a reaction. A large focus on the rule-based modeling of biochemical systems has been geared toward simulation using KMC algorithms, there exists a variety of powerful numerical approaches for the analysis of a HLM that have been developed and applied to many different problems ( in the field of operations) over the last few decades These approaches may be divided into two major classes: (1) those designed to tolerate largeness; and (2) those designed to avoid largeness.

RELATED METHODS
State-level model
High-level model
Component state space graph
System domain
Dependency graph
Generating the state-level model
Simple example
Bisimilarity
Symmetry reduction method
Decomposition of independent subsystems
Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
Example 7
Summary
Improvements and future directions
Full Text
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