RuleMonkey: software for stochastic simulation of rule-based models

<p>Abstract</p> <p>Background</p> <p>The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems.</p> <p>Results</p> <p>Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods.</p> <p>Conclusions</p> <p>RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application <url>http://public.tgen.org/rulemonkey</url>. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models.</p

Monetary and fiscal policies for a finite planet

Current macroeconomic policy promotes continuous economic growth. Unemployment, poverty and debt are associated with insufficient growth. Economic activity depends upon the transformation of natural materials, ultimately returning to the environment as waste. Current levels of economic throughput exceed the planet\u27s carrying capacity. As a result of poorly constructed economic institutions, society faces the unacceptable choice between ecological catastrophe and human misery. A transition to a steady-state economy is required, characterized by a rate of throughput compatible with planetary boundaries. This paper contributes to the development of a steady-state economy by addressing US monetary and fiscal policies. A steady-state monetary policy would support counter-cyclical, debt-free vertical money creation through the public sector, in ways that contribute to sustainable well-being. The implication for a steady-state fiscal policy is that any lending or spending requires a careful balance of recovery of money, not as a means of revenue, but as an economic imperative to meet monetary policy goals. A steady-state fiscal policy would prioritize targeted public goods investments, taxation of ecological bads and economic rent and implementation of progressive tax structures. Institutional innovations are considered, including common asset trusts, to regulate throughput, and a public monetary trust, to strictly regulate money supply

RISK ASSESSMENT OF AIR CANNONS AT SPORTING EVENTS

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