Automatic generation of cellular reaction networks with Moleculizer 1.0

@article{Lok2005AutomaticGO,
  title={Automatic generation of cellular reaction networks with Moleculizer 1.0},
  author={Larry Lok and Roger Brent},
  journal={Nature Biotechnology},
  year={2005},
  volume={23},
  pages={131-136}
}
  • L. Lok, R. Brent
  • Published 1 January 2005
  • Computer Science
  • Nature Biotechnology
Accurate simulation of intracellular biochemical networks is essential to furthering our understanding of biological system behavior. The number of protein complexes and of chemical interactions among them has traditionally posed significant problems for simulation algorithms. Here we describe an approach to the exact stochastic simulation of biochemical networks that emphasizes the contribution of protein complexes to these systems. This simulation approach starts from a description of… 

Modeling stochasticity in biochemical reaction networks

TLDR
This review discusses the approach of solving CME by a set of differential equations of probability moments, called moment equations, and presents different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme.

An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems

TLDR
This study compares the efficiency and limitations of several available implementations of network-based and -free stochastic simulation approaches, to allow for an informed selection of the implementation and methodology for specific biochemical modeling applications.

Rule-based modeling of signal transduction: a primer.

TLDR
A self-contained tutorial on modeling signal transduction networks using the BNG Language and related software tools and shows how biochemical knowledge can be articulated using reaction rules, which can be used to capture a broad range of biochemical and biophysical phenomena in a concise and modular way.

Leveraging modeling approaches: reaction networks and rules.

TLDR
A case is made for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.

Tackling the Stochastic Simulation of Biochemical Networks with Real Computing Power

TLDR
A recurring theme in this project is the application of analytical techniques – as opposed to blind simulation – in order to characterize the behavior of biochemical reactions, and to tailor the analysis to the questions at hand.

Detailed Simulations of Cell Biology with Smoldyn 2.1

TLDR
This model showed that secreted Bar1 protease might help a cell identify the fittest mating partner by sharpening the pheromone concentration gradient, and found that Smoldyn was in many cases more accurate, more computationally efficient, and easier to use.

SMGen: A generator of synthetic models of biochemical reaction networks

TLDR
The computational performance of SMGen is analysed by generating batches of symmetric and asymmetric Reaction-based Models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time.

Mathematical Modeling of Biochemical Signal Transduction Pathways in Mammalian Cells – A Domain-Oriented Approach to Reduce Combinatorial Complexity

Mathematical models of biological processes are becoming more and more important in biology. The goal of mathematical modeling is a holistic understanding of how biological processes like cellular

Using the SRSim Software for Spatial and Rule-Based Modeling of Combinatorially Complex Biochemical Reaction Systems

TLDR
The simulator software SRSim is presented here, constructed from the molecular dynamics simulator LAMMPS and a set of extensions for modeling rule-based reaction systems and features a stochastic, particle based, spatial simulation of Brownian Dynamics in three dimensions of a rule- based reaction system.

Simulation of large-scale rule-based models

TLDR
DYNSTOC enables the simulation of rule-based models that cannot be simulated by conventional methods and is demonstrated to simulate models accounting for multisite phosphorylation and multivalent binding processes that are characterized by large numbers of reactions.
...

References

SHOWING 1-10 OF 36 REFERENCES

Computational methods for stochastic biological systems

TLDR
An efficient, exact stochastic simulation algorithm to generate trajectories of mesoscopic biological systems, a sensitivity analysis algorithm to quantify how a model's predictions depend on the exact values of parameters used, and a parameter estimation algorithm to estimate the values of model parameters from observed trajectories are developed.

Computational modeling of genetic and biochemical networks

TLDR
This book provides specific examples of how modeling techniques can be used to explore functionally relevant molecular and cellular relationships, applicable to cell, developmental, structural, and mathematical biology; genetics; and computational neuroscience.

A compiled accelerator for biological cell signaling simulations

TLDR
It is shown that an alternative algorithm to the conventional approaches based on the Gillespie algorithm reveals a fine-grained parallel structure that is amenable to realization in FPGA hardware.

Computer-based analysis of the binding steps in protein complex formation.

  • D. BrayS. Lay
  • Biology, Chemistry
    Proceedings of the National Academy of Sciences of the United States of America
  • 1997
TLDR
It is suggested that the prozone phenomenon will occur widely in living cells and that it could be a crucial factor in the regulation of protein complex formation.

Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels

TLDR
The Next Reaction Method is presented, an exact algorithm to simulate coupled chemical reactions that uses only a single random number per simulation event, and is also efficient.

Regulation of G protein-initiated signal transduction in yeast: paradigms and principles.

TLDR
This review describes the signal transduction pathway used by budding yeast to respond to its peptide mating pheromones, comprised by receptors, a heterotrimeric G protein, and a protein kinase cascade all remarkably similar to counterparts in multicellular organisms.

In silico simulation of biological network dynamics

TLDR
The parallel architecture of FPGAs, which can simulate the basic reaction steps of biological networks, attains simulation rates at least an order of magnitude greater than currently available microprocessors.

Stochastic mechanisms in gene expression.

  • H. McAdamsA. Arkin
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 1997
TLDR
This work has analyzed the chemical reactions controlling transcript initiation and translation termination in a single such "genetically coupled" link as a precursor to modeling networks constructed from many such links.

Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm

TLDR
Using the QSSA, stochastic Michaelis–Menten rate expressions for simple enzymatic reactions are derived and it is illustrated how theQSSA is applied when modeling and simulating a simple genetic circuit.

Kinetic analysis of a molecular model of the budding yeast cell cycle.

TLDR
Using standard techniques of biochemical kinetics, a mechanism for the principal molecular interactions controlling cyclin synthesis and degradation is converted into a set of differential equations, which describe the time courses of three major classes of cyclin-dependent kinase activities.