Modelling cellular behaviour

  title={Modelling cellular behaviour},
  author={Drew Endy and Roger Brent},
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. 

Modelling the dynamics of biosystems

The need for a more formal handling of biological information processing with stochastic and mobile process algebras is addressed and new computational models inspired by nature are obtained.

Hybrid simulation of cellular behavior

A novel approach to building hybrid simulations in which some processes are simulated discretely, while other processes are handled in a continuous simulation by differential equations, which preserves the stochastic behavior of cellular pathways, yet enables scaling to large populations of molecules.

Modeling the Nonlinear Dynamics of Cellular Signal Transduction

It is shown that based on time resolved measurements it is possible to quantitatively model the nonlinear dynamics of signal transduction and suggest a new model including a delayed feedback for JAK-STAT signalling pathway.

Parallel implementation of stochastic simulation for large-scale cellular processes

  • T. TianK. Burrage
  • Computer Science, Biology
    Eighth International Conference on High-Performance Computing in Asia-Pacific Region (HPCASIA'05)
  • 2005
Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes.

Tackling the Stochastic Simulation of Biochemical Networks with Real Computing Power

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.

Intracellular signaling: spatial and temporal control.

Cell physiology has passed the threshold: the time to begin modeling is now, and new tools to probe large sets of unknown interactions, and enough detailed information to quantitatively describe many functional modules are amassed.

Dynamic cellular automata: an alternative approach to cellular simulation.

This work shows how the DCA approach can be used to easily and accurately model diffusion, viscous drag, enzyme rate processes, metabolism, and complex genetic circuits and demonstrates how DCA approaches are able to accurately capture the stochasticity of many biological processes.

In silico simulation of biological network dynamics

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.

Kinetic modeling of biological systems.

This chapter discusses the implications for simulation of models involving interacting species with very low copy numbers, which often occur in biological systems and give rise to significant relative fluctuations.



A synthetic oscillatory network of transcriptional regulators

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.

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

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
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.

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