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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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part 1 computational biology
Part 1 Computational Biology

Beyond the spherical cow

John Doyle

Nature, 411, 151-152 (2001)

for what
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
Enablers
  • Discovery science
  • Acceptance that biology is now a cross-disciplinary science
  • Maturation of the internet as a forum for collaborations
enablers5
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
slide6
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
Developments
  • Gaining more access to technology
  • Mathematical modeling and computation
  • Design and implementation of synthetic gene networks
considerations
Considerations
  • Interaction between experiment and simulation
  • Fluctuations
  • Chemical dynamics
  • Mechanical dynamics
  • Interaction of chemical and mechanical dynamics
applications
Applications
  • Cell division cycle
  • Virtual vs. Real mutated genes
  • Developmental principles

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

applications10
Applications
  • More efficient route to drug discovery and development
  • integrated biological circuits
    • “wet” nano-robots
    • engineered oncolytic adenovirus
example
Example
  • Computer modeling of individual ion channels in cardiac cells
      • Pacemaker activity
      • Genetic defects underlying arrythmic heartbeats
      • Mechanical-electrical feedbacks
      • Regional patterns of expression
example12
Example
  • Model for cell motility

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

example13
Example
  • Reaction-diffusion model

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

example14
Example
  • Dynamics of calcium ions

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

limitations
Limitations
  • Biology needs more theory
  • Theory has a rather bad reputation among biologists
slide16
“It took Humpty Dumpty apart but left the challenge of putting him back together again”

- John Doyle

references
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

Part 2:Segmentation in silico

Peter Dearden and Michael Akam

Nature 406, 131-132 (2000)

protocol von dassow et al
Protocol (Von Dassow,et.al.)

Collection of data

Key interactions

Simplification

Final model

results24
Results

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

conclusion
Conclusion

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

implications
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
implications29
Implications
  • Emergence of a new breed of biologist-mathematicians
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