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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 cow 2 segmentation in silico l.jpg

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


Part 1 computational biology l.jpg

Part 1 Computational Biology

Beyond the spherical cow

John Doyle

Nature, 411, 151-152 (2001)


For what l.jpg

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


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Enablers

  • Discovery science

  • Acceptance that biology is now a cross-disciplinary science

  • Maturation of the internet as a forum for collaborations


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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


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Goal

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


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Developments

  • Gaining more access to technology

  • Mathematical modeling and computation

  • Design and implementation of synthetic gene networks


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Considerations

  • Interaction between experiment and simulation

  • Fluctuations

  • Chemical dynamics

  • Mechanical dynamics

  • Interaction of chemical and mechanical dynamics


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Applications

  • Cell division cycle

  • Virtual vs. Real mutated genes

  • Developmental principles

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


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Applications

  • More efficient route to drug discovery and development

  • integrated biological circuits

    • “wet” nano-robots

    • engineered oncolytic adenovirus


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Example

  • Computer modeling of individual ion channels in cardiac cells

    • Pacemaker activity

    • Genetic defects underlying arrythmic heartbeats

    • Mechanical-electrical feedbacks

    • Regional patterns of expression


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Example

  • Model for cell motility

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


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Example

  • Reaction-diffusion model

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


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Example

  • Dynamics of calcium ions

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


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Limitations

  • Biology needs more theory

  • Theory has a rather bad reputation among biologists


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

- John Doyle


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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


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Part 2:Segmentation in silico

Peter Dearden and Michael Akam

Nature 406, 131-132 (2000)


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Protocol (Von Dassow,et.al.)

Collection of data

Key interactions

Simplification

Final model


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Final model


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Results


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Results


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Results


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Results

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


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Conclusion

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


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Drosophila segmentation (Wolpert)]


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Box 1 (von Dassow,et.al.)


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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


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Implications

  • Emergence of a new breed of biologist-mathematicians


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