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Computational Biology: 1. Beyond the spherical cow 2. Segmentation in silico PowerPoint PPT Presentation

Computational Biology: 1. Beyond the spherical cow 2. Segmentation in silico Part 1 Computational Biology Beyond the spherical cow John Doyle Nature, 411, 151-152 (2001) For what? make sense of the huge amounts of data produced unravel how complex biochemical systems really work

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Computational Biology: 1. Beyond the spherical cow 2. Segmentation in silico

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Computational Biology:1. Beyond the spherical cow2. Segmentation in silico


Part 1 Computational Biology

Beyond the spherical cow

John Doyle

Nature, 411, 151-152 (2001)


For what?

  • make sense of the huge amounts of data produced

  • unravel how complex biochemical systems really work

http://www.biology.arizona.edu/cell_bio/tutorials/cell_cycle/cells3.html


Enablers

  • Discovery science

  • Acceptance that biology is now a cross-disciplinary science

  • Maturation of the internet as a forum for collaborations


Enablers

  • Notion: Biology is an information-based rather than qualitative science

  • High-throughput platforms capable of capturing global sets of information quickly and affordably

  • Medical imaging systems


Goal

“… the computational approaches discussed… were firmly focused on the dynamics and control of the networks of genes and proteins at work in cells.”


Developments

  • Gaining more access to technology

  • Mathematical modeling and computation

  • Design and implementation of synthetic gene networks


Considerations

  • Interaction between experiment and simulation

  • Fluctuations

  • Chemical dynamics

  • Mechanical dynamics

  • Interaction of chemical and mechanical dynamics


Applications

  • Cell division cycle

  • Virtual vs. Real mutated genes

  • Developmental principles

http://www.biology.arizona.edu/cell_bio/tutorials/cell_cycle/cells2.html


Applications

  • More efficient route to drug discovery and development

  • integrated biological circuits

    • “wet” nano-robots

    • engineered oncolytic adenovirus


Example

  • Computer modeling of individual ion channels in cardiac cells

    • Pacemaker activity

    • Genetic defects underlying arrythmic heartbeats

    • Mechanical-electrical feedbacks

    • Regional patterns of expression


Example

  • Model for cell motility

http://expmed.bwh.harvard.edu/projects/motility/motility.html


Example

  • Reaction-diffusion model

http://www.math.vanderbilt.edu/~morton/cs395/roth/fig2.gif


Example

  • Dynamics of calcium ions

http://www.compbiophysics.uni-hd.de/Signal_Transduction.html


Limitations

  • Biology needs more theory

  • Theory has a rather bad reputation among biologists


“It took Humpty Dumpty apart but left the challenge of putting him back together again”

- John Doyle


References

  • Doyle, J. 2001. “Computational Biology: Beyond the spherical cow.” In Nature, 411:151-152.

  • Hasty, J., McMillen, D., and Collins, J. J. 2002. “Engineered gene circuits. “ In Nature, 420:224-230.

  • http://www.the-scientist.com/yr2003/feb/feature_030224.html

  • http://www.the-scientist.com/yr2003/feb/prof4_030224.html

  • http://www.the-scientist.com/yr2003/feb/feature2_030224.html

  • http://www.the-scientist.com/yr2003/feb/feature1_030224.html


Part 2:Segmentation in silico

Peter Dearden and Michael Akam

Nature 406, 131-132 (2000)


Protocol (Von Dassow,et.al.)

Collection of data

Key interactions

Simplification

Final model


Final model


Results


Results


Results


Results

frequency of ‘solutions’ allowed the model to generate correct pattern of segmentation


Conclusion

“ It is the organization of the gene networks that provides stability, not the fine tuning of molecular interactions.”


Drosophila segmentation (Wolpert)]


Box 1 (von Dassow,et.al.)


Implications

  • Allows possibility to explore effects of variations in parameter values

  • Allows possibility of studying the effect of varying initial conditions

  • Allows possibility of making complex gene networks more understandable


Implications

  • Emergence of a new breed of biologist-mathematicians


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