Computational Systems Biology

1. Computational Systems Biology Prepared by: Rhia Trogo Rafael Cabredo Levi Jones Monteverde

2. What are Biological Systems? Popular Notion: It is a complex system consisting of very many simple and identical elements interacting to produce what appears to be complex behavior Example: Cells, Proteins

3. What are Biological Systems? Realistic Notion: It is a system composed of many different kinds of multifunctional elements interacting selectively and nonlinearly with others to produce coherent behavior.

4. What are Biological Systems? • Complex systems of simple elements have functions that emerge from the properties of the networks they form. • Biological systems have functions that rely on a combination of the network and the specific elements involved.

5. Molecular vs. Systems Biology Biology • In molecular biology, gene structure and function is studied at the molecular level. • In systems biology, specific interactions of components in the biological system are studied – cells, tissues, organs, and ecological webs.

6. From Systems Biology to Computational Biology Biological Systems are complex, thus, a combination of experimental and computational approaches are needed. Linkages need to be made between molecular characteristics and systems biology results

7. Databases and Tools • Languages • Systems Biology Markup Language • CellML • Systems Biology Workbench • Databases • Kyoto Encyclopedia of Genes and Genomes • Alliance for Cellular Signaling • Signal Transduction Knowledge Environment

8. p53 • Protein 53 • Produces 53 proteins kiloDaltons • Guardian of the genome • Detects DNA damages • Halts the cell cycle if damage is detected to give DNA time to repair itself

9. p53 If (damage equals true and repairable = true) halt cell cycle else if(damage equals true and repairable = false) induce apoptosis (suicide)

10. The Cell Cycle • G1 - Growth and preparation of the chromosome replication • S - DNA replication • G2 - Preparation for Mitosis • M - Chromosomes separate

11. Checkpoints for DNA Double Strand Breakage ataxia-telangiectasia mutated

12. Cancer Cell Network

13. p53 activates deactivates p53 p21 CDK No cell cycle!

14. p53

15. Cancer Drugs • Alkylating agents - interfere with cell division and affect the cancer cells in all phases of their life cycle. They confuse the DNA by directly reacting with it. • Antimetabolites - interfere with the cell's ability for normal metabolism. They either give the cells wrong information or block the formation of "building block" chemical reactions one phase of the cell's life cycle. • Vinca alkaloids - (plant alkaloids) are naturally-occurring chemicals that stop cell division in a specific phase. • Taxanes - are derived from natural substances in yew trees. They disrupt a network inside cancer cells that is needed for the cells to divide and grow. all inhibit the cell cycle

16. The Cost of Robustness • Robustness is not a good characteristic for all types of cells. Example: The robust cancer cell! • Systems that are robust against common perturbations are often fragile to new perturbations (vulnerability of complex networks)

17. Advantages of Computational Systems Biology • It is highly relevant in discovering more complex relationships involving multiple genes • This may create new opportunities for drug discovery • Better medical therapies for individual treatments

18. What’s to come? • Current work is on small sub-networks within cells. • Feedback circuit of bacteria chemotaxis • Circadian Rhythm • Parts of signal-transduction pathways • Simplified models of the cell cycle • Models of the Red blood cells

19. What’s to come? • Research has begun on larger-scale simulations • Biochemical network level • Simulation of Epidermal Growth Factor (EGF) signal-transduction cascade • The Physiome Project

20. Biochemical Networks Problem: The behavior of cells is governed and coordinated by biochemical signaling networks that translate external cues (hormones, growth factors, stress, etc.) into adequate biological responses such as cell proliferation, specialization or death, and metabolic control. Motivation: Deep understanding of cell malfunction is crucial for drug development and other therapies. Available: [online] http://www.brc.dcs.gla.ac.uk/projects/bps/bps_slides/bps_slides.pdf

21. Biochemical Networks

22. Biochemical Networks

23. Interpreting Biochemical Networks as Concurrent Communicating Systems • Biochemical networks are analogous to concurrent computer systems in many respects. • Concurrent systems are built up using basic concepts such as choice, recursion, modularity, synchronization, and mobility. • By exploiting these analogies, the existing tools and formalisms for computing systems can be applied to biochemical networks.

24. Concurrency Theory • Concurrent, communicating systems have been the subject of intense study by Computing Scientists. Rich theories and tools have been developed to aid in design, analysis and verification of such systems. • Concurrent systems are inherently complex. To manage complexity, theories and tools have been developed to allow programmers to simulate behaviour. Simulators allow the analysis of traces through concurrent executions and provide a testbed for experimentation. • At a more abstract level, temporal analysis involves proving that a concurrent system adheres to a temporal property, i. e. it can be shown that a network protocol always delivers data packets in the same order they were sent.

25. Concurrency A concurrent system is one where multiple processes exist at the same time. These processes execute in parallel and potentially interact with each other. As an example of a concurrent system, consider an internet banking site. The server and multiple client processes exist at the same time, with interactions occurring between the individual clients and the server.

26. Concurrency in Biochemical Networks Biochemical networks are also concurrent communicating systems. Pathways consist of sequences of interactions which sometimes affect other parallel pathways. As an example, consider two pathways involved in cell division. The Ras- Raf pathway which triggers the cell division and the PI- 3K- Akt pathway which keeps the cell alive are both triggered by the same growth factor. The sequences of interactions in both pathways run concurrently with some interaction i. e. Akt inhibits Raf.

27. Complex modeling of concurrent systems • Asynchronous circuits have been used to simplify circuit analysis • Perhaps they could be used to examining concurrent biological systems. • http://www.async.ece.utah.edu/

28. lambda DNA is initially transcribed from the promoters PL and PR, which direct synthesis of RNA in opposite directions (left and right respectively). Transcription is initially terminated at sites tL and tR, but expression of the N gene (in green) leads to "antitermination" and production of longer transcripts

29. If PR wins and the protein cro is made, then production of cI will be repressed. If on the other hand promoter PRM wins and the protein cI is made, then production of cro will be repressed.

30. If cro predominates, it hogs the operator region and prevents cI from being made. On the other hand if cI predominates, it hogs the operator region, causing more of itself to be made (from the PRM promoter).

31. There is a promoter called PRE that is activated by cII and cIII (which are produced after the anti-terminator N is made).

32. either the cI or the cro will predominate, and one of the following two patterns of gene expression will result:

33. Overview • the first pattern leads to the growth of the virus (only a fraction of the genes involved in lysis are actually shown) and the death of the cell. • The second pattern is the more interesting for our purposes. • In the lower panel, cI is the only gene that is being expressed in the virus, and it is involved in a positive feedback loop to induce more of its own expression.This explains why the lysogenic state is stable. The genome of the virus is essentially shut down during lysogeny, except for a single repressor protein. If another lambda happens to come along it's out of luck! The cI repressor from the first lambda simply prevents expression of the second lambda genome, and it fails to enter a lytic cycle.

34. Immunity • That explains bacteriophage immunity!One allele of cI that is important in the laboratory is cI857, which is temperature sensitive (the protein is active at 32 degrees centigrade but inactivated at 39 degrees centigrade). • We may therefore grow a lambda phage carrying cI857 as a lysogen at low temperature, then induce lytic growth by simply moving it to a warmer incubator.

35. Database • http://www.biocyc.org/