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

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

2. Part 1 Computational Biology Beyond the spherical cow John Doyle Nature, 411, 151-152 (2001)

3. 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

4. Enablers • Discovery science • Acceptance that biology is now a cross-disciplinary science • Maturation of the internet as a forum for collaborations

5. 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

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

7. Developments • Gaining more access to technology • Mathematical modeling and computation • Design and implementation of synthetic gene networks

8. Considerations • Interaction between experiment and simulation • Fluctuations • Chemical dynamics • Mechanical dynamics • Interaction of chemical and mechanical dynamics

9. Applications • Cell division cycle • Virtual vs. Real mutated genes • Developmental principles http://www.biology.arizona.edu/cell_bio/tutorials/cell_cycle/cells2.html

10. Applications • More efficient route to drug discovery and development • integrated biological circuits • “wet” nano-robots • engineered oncolytic adenovirus

11. Example • Computer modeling of individual ion channels in cardiac cells • Pacemaker activity • Genetic defects underlying arrythmic heartbeats • Mechanical-electrical feedbacks • Regional patterns of expression

12. Example • Model for cell motility http://expmed.bwh.harvard.edu/projects/motility/motility.html

13. Example • Reaction-diffusion model http://www.math.vanderbilt.edu/~morton/cs395/roth/fig2.gif

14. Example • Dynamics of calcium ions http://www.compbiophysics.uni-hd.de/Signal_Transduction.html

15. Limitations • Biology needs more theory • Theory has a rather bad reputation among biologists

16. “It took Humpty Dumpty apart but left the challenge of putting him back together again” - John Doyle

17. 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

18. Part 2:Segmentation in silico Peter Dearden and Michael Akam Nature 406, 131-132 (2000)

19. Protocol (Von Dassow,et.al.) Collection of data Key interactions Simplification Final model

20. Final model

21. Results

22. Results

23. Results

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

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

26. Drosophila segmentation (Wolpert)]

27. Box 1 (von Dassow,et.al.)

28. 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

29. Implications • Emergence of a new breed of biologist-mathematicians