1. Systems Biology

2. Two ways of looking a problem Top down or bottom up Top down or bottom up Either look at the whole organism and abstract large portions of it Either look at the whole organism and abstract large portions of it Or try to understand each small piece and then after understanding every small piece assemble into the whole Or try to understand each small piece and then after understanding every small piece assemble into the whole Both are used, valid and complement each other Both are used, valid and complement each other

3. Bottom up is traditional approach You would study a pathway in detail not worrying about how that pathway might interact with other elements in the cell. You would study a pathway in detail not worrying about how that pathway might interact with other elements in the cell. You would strive to understand a gene or pathway in great detail, eventually you might extend this knowledge to other organisms and compare and contrast. You would strive to understand a gene or pathway in great detail, eventually you might extend this knowledge to other organisms and compare and contrast. With top down you need other tools... With top down you need other tools...

4. Definitions At a recent NIH SysBio SIG retreat almost every talk started with that speakers definition of what systems biology is. At a recent NIH SysBio SIG retreat almost every talk started with that speakers definition of what systems biology is. Leroy Hood came up with the following (my summary) Leroy Hood came up with the following (my summary) As global a view as possible As global a view as possible Fundamentally quantitative Fundamentally quantitative Different scales integrated Different scales integrated

5. The Systems Biology Institute take: The Systems Biology Institute take: Understand the structure of the system Understand the structure of the system Regulatory and biochemical networks Regulatory and biochemical networks Understand the dynamics of the the system Understand the dynamics of the the system Construct model with predictive capabilities Construct model with predictive capabilities Understand the control methods Understand the control methods

6. Common “themes” Cross disciplinary Cross disciplinary Lots of data/information/knowledge Lots of data/information/knowledge Concepts of networks for abstract portrayal of many interaction types. Concepts of networks for abstract portrayal of many interaction types. Model development Model development Predictive models Predictive models Models to drive experimentation Models to drive experimentation Models to understand processes Models to understand processes

7. “Inner life of a Cell” SIGGRAPH 2006 showcase winner Need to fight infection Need to fight infection WBC WBC Need to keep blood from leaking out Need to keep blood from leaking out

8. Requires a higher level of understanding Many tools “feed” into this understanding Many tools “feed” into this understanding Microarrays Microarrays Homology tools (BLAST, alignments COGS) Homology tools (BLAST, alignments COGS) Biochemical literature Biochemical literature Genomic sequence Genomic sequence Specialized databases Specialized databases Any faults in these tools lead to problems in the analysis Any faults in these tools lead to problems in the analysis

9. A complex problem 35,000 genes either on or off (huge simplification!) would have 2^35,000 solutions 35,000 genes either on or off (huge simplification!) would have 2^35,000 solutions Things can be simplified by grouping and finding key genes which regulate many other genes and genes which may only interact with one other gene Things can be simplified by grouping and finding key genes which regulate many other genes and genes which may only interact with one other gene In reality there are lots of subtle interactions and non-binary states. In reality there are lots of subtle interactions and non-binary states.

10. Some real numbers from E. coli 630 transcription units controlled by 97 transcription factors. 630 transcription units controlled by 97 transcription factors. 100 enzymes that catalyse more than one biochemical reaction. 100 enzymes that catalyse more than one biochemical reaction. 68 cases where the same reaction is catalysed by more than one enzyme. 68 cases where the same reaction is catalysed by more than one enzyme. 99 cases where one reaction participates in multiple pathways. 99 cases where one reaction participates in multiple pathways. The regulatory network is at most 3 nodes deep. The regulatory network is at most 3 nodes deep. 50 of 85 studied transcription factors do not regulate other transcription factors, lots of negative auto-regulation 50 of 85 studied transcription factors do not regulate other transcription factors, lots of negative auto-regulation

12. Theoretical hurdles to jump Switching delay (McAdams and Arkin 1997) Switching delay (McAdams and Arkin 1997) More transcripts, less protein/transcript = more energy less noise More transcripts, less protein/transcript = more energy less noise Fewer transcripts, More protein/transcript = less energy more noise. Fewer transcripts, More protein/transcript = less energy more noise. Selection drives this trade-off Selection drives this trade-off Two critical times; how long after trigger does a protein reach a critical level how long after removal of the trigger does the protein level decline to below critical level. Two critical times; how long after trigger does a protein reach a critical level how long after removal of the trigger does the protein level decline to below critical level. How critical is the level How critical is the level

13. Conclusions from Arkin: Simulations found 3-20 minutes from transcript to active protein. Simulations found 3-20 minutes from transcript to active protein. Many processes are stochastic (random) not deterministic. Many processes are stochastic (random) not deterministic. The probabilities are definitely skewed but still have long tails The probabilities are definitely skewed but still have long tails This means that with a large population there are cells which may be in very different states than most of the rest of the population. This means that with a large population there are cells which may be in very different states than most of the rest of the population. Complex interplay between regulation, lag and activity that has implications when trying to reconstruct a network. Complex interplay between regulation, lag and activity that has implications when trying to reconstruct a network.

14. Surviving heat shock: Control strategies for robustness and performance Taking engineering principles and applying them to systems biology Taking engineering principles and applying them to systems biology

15. Air conditioning Setpoint (temperature you set) Setpoint (temperature you set) Sensor (thermostat) Sensor (thermostat) Error signal (temp exceeded) Error signal (temp exceeded) Controller (thermostat/ac) Controller (thermostat/ac) Actuator (ac on) Actuator (ac on)

16. Heat shock protein Increased heat -> mRNA -  32 mRNA melting Increased heat -> mRNA -  32 mRNA melting Make  32 Make  32 Interacts with RNAP to activate specific sub- sets of genes Interacts with RNAP to activate specific sub- sets of genes Make a bunch >10,000 protein copies to deal with heat Make a bunch >10,000 protein copies to deal with heat

17. Heat shock response

18. Components DNAK DNAK Chaperone representative Chaperone representative Binds to  32 and degraded proteins Binds to  32 and degraded proteins FtsH FtsH Protease degrading  32 Protease degrading  32 Titrated away by degraded proteins Titrated away by degraded proteins  32  32 Temperature regulation at translation Temperature regulation at translation

19. Why make it more difficult? Need to turn off (cooler) Need to turn off (cooler) Don’t want to activate inappropriately (energy waste) Don’t want to activate inappropriately (energy waste) Fast response (proteins degrading) Fast response (proteins degrading) Proportional response (it’s a little hot) Proportional response (it’s a little hot)

20. Theoretical types of control

21. Effects of control types on response levels

22. Adding metabolic cost as a design parameter

23. Using feedback to get robustness

24. Summary Sometimes simple is better but: Sometimes simple is better but: Often some complexity adds desirable features Often some complexity adds desirable features Trade off between complexity, robustness, and economy Trade off between complexity, robustness, and economy Modules, reuse Modules, reuse “Helps” evolution “Helps” evolution Can help biologist Can help biologist

25. Techniques Advanced Methods and Algorithms for Biological Networks Analysis Advanced Methods and Algorithms for Biological Networks Analysis “such questions are conventionally viewed as computationally intractable. Thus, biologists and engineers alike are often forced to resort to inefficient simulation methods or translate their problems into biologically unnatural terms in order to use available algorithms; hence the necessity for an algorithmic scalable infrastructure the systematically addresses these questions”

26. Problems of modeling Compare model to data Compare model to data But with complex model and large parameter set any data set can be made to fit But with complex model and large parameter set any data set can be made to fit Could a simpler model also work Could a simpler model also work Untested parameters Untested parameters

27. Alternative to exhaustive searches Use sum of squares to generate dynamical behavior barriers Use sum of squares to generate dynamical behavior barriers Don’t test all possible values just see where they make a difference Don’t test all possible values just see where they make a difference Stocastic simulation is another way but Stocastic simulation is another way but Uses months to simulate picoseconds Uses months to simulate picoseconds Robustness provides a key Robustness provides a key Biological systems must exhibit robustness Biological systems must exhibit robustness This robustness also limits the search space This robustness also limits the search space

28. Case studies Consistency between literature and microarray profiles. Consistency between literature and microarray profiles. Galactose utilization in yeast. Galactose utilization in yeast.

29. Case study 1: Microarrays -> regulatory networks Long been a dream, all this data should tell me everything. Long been a dream, all this data should tell me everything. Try with E. coli: Try with E. coli: How consistent is the literature knowledge base with the microarray expression profile How consistent is the literature knowledge base with the microarray expression profile Genome Research 13:2435-2443 2003 Genome Research 13:2435-2443 2003 Literature compiled into the RegulonDB database Literature compiled into the RegulonDB database Correlation was significant 70-89% but… Correlation was significant 70-89% but…

30. But… Noise filtering removed >50% of the genes on the microarray Noise filtering removed >50% of the genes on the microarray 83/179 known regulatory genes where used the rest discarded also due to noise filtering. 83/179 known regulatory genes where used the rest discarded also due to noise filtering. Simple conditions: Minimal media, anaerobic and stationary phase growth. Simple conditions: Minimal media, anaerobic and stationary phase growth. 32% of the 83 where always off. 32% of the 83 where always off. Fell to ~40% if effector metabolites not considered. Fell to ~40% if effector metabolites not considered.

31. Case study 2: Figure out Galactose utilization in yeast Classic last line: “As technologies for cellular perturbation and global measurement mature, these approaches will soon become feasible in higher eukaryotes” Classic last line: “As technologies for cellular perturbation and global measurement mature, these approaches will soon become feasible in higher eukaryotes” Combines: literature knowledge, microarray, proteomics, visualization, and network techniques to refine what is known about galactose utilization in yeast. Combines: literature knowledge, microarray, proteomics, visualization, and network techniques to refine what is known about galactose utilization in yeast. Science 292:929-934 Science 292:929-934

32. Utilization of galactose is well studied 1625 papers in PubMed dating back to the 1950s. Utilization of galactose is well studied 1625 papers in PubMed dating back to the 1950s. Simple process get Galactose into the cell then modify this sugar into the more usable form of glucose-6-P; don’t waste a lot of energy doing it if: (1.) there is no gal or (2.) you have plenty of glucose. Simple process get Galactose into the cell then modify this sugar into the more usable form of glucose-6-P; don’t waste a lot of energy doing it if: (1.) there is no gal or (2.) you have plenty of glucose.

33. Galactose metabolism

34. The Process Define all genes in the genome, particularly the subset of genes and other small molecules that are involved in the gal pathway (DONE) Define all genes in the genome, particularly the subset of genes and other small molecules that are involved in the gal pathway (DONE) For each gene or condition change (ie delete the gene) and measure the global effect on both mRNA and protein levels. For each gene or condition change (ie delete the gene) and measure the global effect on both mRNA and protein levels. Integrate the changes in respect to the first point with all known protein-protein and protein-DNA networks Integrate the changes in respect to the first point with all known protein-protein and protein-DNA networks Form new hypothesises and test Form new hypothesises and test

35. Expression measurements

36. Visualizing the data Blue line (pp) Yellow line (pd)

37. Networks the “system” of systems biology Humans produce some pretty complex structures. Humans produce some pretty complex structures. Computer chips Computer chips Oil refineries Oil refineries Airplanes Airplanes The goals for these structures are similar to life forms The goals for these structures are similar to life forms Survive Survive Do it at a cheap cost Do it at a cheap cost Reproduce/evolve?? Reproduce/evolve??

38. Basic network terminology Nodes Nodes Edges Edges Scale-free Scale-free Power laws Power laws Exponential/Random networks Exponential/Random networks Robustness Robustness Ability to respond to different conditions Ability to respond to different conditions Robust yet fragile Robust yet fragile Complexity Complexity Not the number of parts… consider a lump of coal Not the number of parts… consider a lump of coal The number of different parts AND the organization of those parts The number of different parts AND the organization of those parts

39. Graph theory, networks Two types of networks Two types of networks Exponential and scale free Exponential and scale free Most cellular networks are scale free Most cellular networks are scale free It makes the most sense to study the interactions of the central nodes not the outer nodes It makes the most sense to study the interactions of the central nodes not the outer nodes

40. High Throughput data sources Microarray data Microarray data Already well covered in the last couple of weeks. Already well covered in the last couple of weeks. Probably the most mature Probably the most mature Proteomics Proteomics Several processes Several processes Separation of the products Separation of the products Digest the products Digest the products Find the mass of the products Find the mass of the products Problems Problems Contamination Contamination Phosphorylation, glycosylation, Acylation, methylation, cleavage. Phosphorylation, glycosylation, Acylation, methylation, cleavage.

41. Cytoscape Software tool to manage data and develop predictive models (Genome Research Shannon et al. 2003) Software tool to manage data and develop predictive models (Genome Research Shannon et al. 2003) Not directed specifically to a cellular process or disease pathway Not directed specifically to a cellular process or disease pathway Combine Combine Protein-protein interactions Protein-protein interactions RNA expression RNA expression Genetic interactions Genetic interactions Protein-dna interactions Protein-dna interactions Protein abundance Protein abundance Protein phosphorylation Protein phosphorylation Metabolite concentrations Metabolite concentrations Integrate (global) molecular interactions and state measurements. Integrate (global) molecular interactions and state measurements. Organized around a network graph Organized around a network graph