Modelling cellular behaviour

@article{Endy2001ModellingCB,
  title={Modelling cellular behaviour},
  author={Drew Endy and Roger Brent},
  journal={Nature},
  year={2001},
  volume={409},
  pages={391-395}
}
Representations of cellular processes that can be used to compute their future behaviour would be of general scientific and practical value. But past attempts to construct such representations have been disappointing. This is now changing. Increases in biological understanding combined with advances in computational methods and in computer power make it possible to foresee construction of useful and predictive simulations of cellular processes. 

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References

SHOWING 1-10 OF 65 REFERENCES

A synthetic oscillatory network of transcriptional regulators

TLDR
This work used three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network, termed the repressilator, in Escherichia coli, which periodically induces the synthesis of green fluorescent protein as a readout of its state in individual cells.

Predicting temporal fluctuations in an intracellular signalling pathway.

TLDR
A newly developed stochastic-based program was used to predict the fluctuations in numbers of molecules in a chemotactic signalling pathway of coliform bacteria and found that CheYp molecules were found to undergo random fluctuations in number about an average corresponding to the deterministically calculated concentration.

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.

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 kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells.

TLDR
The fraction of infected cells selecting the lysogenic pathway at different phage:cell ratios, predicted using a molecular-level stochastic kinetic model of the genetic regulatory circuit, is consistent with experimental observations.

The chemical Langevin equation

The stochastic dynamical behavior of a well-stirred mixture of N molecular species that chemically interact through M reaction channels is accurately described by the chemical master equation. It is
...