Models of cellular regulation

1. Models of cellular regulation • A genetic switch • Lambda lysogeny/lysis • Three operator sites controlling two promoters PRM and PR • Cro and CI dimers bind to the operator sites, generating two antagonistic feedback loops • Cro dimer represses expression of CI, while CI represses Cro; bind to operators with different affinities and in opposite order • Concentration dependent logic

2. How do cells obtain signals from noise? • Uneven distribution of biomolecules among cells • Stochastic gene expression has been observed in both eukaryotic and prokaryotic cells • How do cells focus a signal for specific gene expression?

3. A paradigm shift • Reductionism  Integration • System properties are determined by concentration of each component and reaction rates – even with steady state assumptions still a complex issue • Model systems • Metabolism • Signal transduction

4. Genomics, proteomics, structural genomics, etc. • Looking to reveal networks inherent to cell physiology • Looking at models • Turning stochastic processes into deterministic events

5. Biological signaling occurs at multiple levels • Intracellular signaling complexity results from: • Interactions between pathways • Compartmentalization • Signal channeling

6. Compartments • Many signaling components are membrane-bound, and there is a distinct dearth in our understanding of membrane biochemistry. • Still, it has been readily identified that cells use compartments to derive specific microenvironments, which can offer distinct responses to the same signals • Look at compartments as wires or appliances

7. Reaction channeling • Central tenet of metabolism • Compartments communicate via transporters • Consider transporters as switches (?) controlling the flow of signals down gradients • An intersection between cell biology and biochemistry

9. Fatty acids are activated and transported into the mitochondria

10. Transduction by carnitine is the major regulatory point of fatty acid oxidation

11. Molecular scaffolds • Once considered the function of rRNA • Term used for a new class of signaling proteins that do not have information transfer capability of their own but interact with multiple signaling proteins in a pathway

12. “The scaffold provides an assembly line along which a series of enzymes process their substrates in a well-defined sequence and with an efficiency and specificity that are orders of magnitude higher than would be possible.”

14. Approaches to the complexity issue • Development of signaling databases (ie. BIND) • Systematic cataloging of proteins, lipids, sugars, and other signaling molecules together with genomic data of model systems

15. An example in modeling – metabolic phenomics • “It is now clear that we need to develop creative approaches and technologies to use all of this information [genomics and proteomics] to explore and determine genome function. We must essentially take on the view of a gene that we began with over 50 years ago, wherein the focus was on the functional attributes of a gene within the context of the whole organism.”

16. Surprise! • Even when multiple knockouts are generated, a surprising number of mutants result in no effect on growth. • Flexibility in metabolic genotype – rerouting of metabolites • Clear example given by PK knockout in E. coli

17. Yet, some metabolic modeling and engineering successes • Prediction and correlation of defined growth media • Glucose transporter confers heterotrophic growth upon a photosynthetic algae • Check out PLAS

18. Integrated circuits • How do metabolic pathways communicate? • How do signal transduction pathways illicit appropriate responses? • Etc.

19. Start with a simple model • Michaelis-Menten • Modeling interactions between adenosine receptor with adenylate cyclase with first order kinetics – Handout

20. b-adrenergic receptors • Integral membrane protein with 7 TM regions – serpentine receptor • Epinephrine (or adrenaline) binds and causes a conformational change that stimulates a G protein, which in turn stimulates adenylyl cyclase

21. Epinephrine transduction

22. G Protein has a built-in timing mechanism

23. The adenylyl cyclase reaction

24. Modeling this reaction • b-receptor is physically separated and activates the enzyme by “collision coupling” • Modeled as a first order reaction in the presence of non-hydrolyzable GTP analogue • Expressing the results mathematically

25. Activation of adenylate cyclase by adenosine • In contrast to collision coupling, the adenosine receptor is modeled as permanently coupled to adenylate cyclase • This predicts a distinct rate constant dependence for cyclase activity (cyclase activation) • Adenosine activation of adenylate cyclase is predicted to be independent of receptor concentration (k3 is unaltered), but the maximum catalytic units will decrease upon receptor activation

26. Braun and Levitzki • Examine figure 3; o-adenosine is a competitive inhibitor that does not affect the catalytic rate regarding adenosine activation of adenylate cyclase • This result is consistent with their model • Additional support comes from independence of adenosine activation from membrane fluidity • Relax, “permanent” means k3>>k1

27. Use simple models to build complicated ones … • http://occawlonline.pearsoned.com/bookbind/pubbooks/bc_mcampbell_genomics_1/medialib/method/T7list.html • http://discover.nci.nih.gov/kohnk/fig6a.html • http://www.genesis-sim.org/GENESIS/

28. Signal transduction • http://www.sciencemag.org/cgi/reprint/284/5411/92.pdf • Bhalla and Iyengar • Signaling pathways are wires, since not separated by insulators – signaling molecules are distinct

29. A role for cAMP

30. Desensitization from persistent signal

31. Other second messengers • Phospholipase C cleaves membrane lipid phosphatidylinositol 4,5 bisphospate into two messengers diacylgllycerol and inositol 1,4,5 trisphosphate (IP3) • IP3 in turn activates release of calcium ions that act as a messenger and activate protein kinase C (numerous isozymes with tissue specific roles, for instance in cell division)

32. PLC mediated signal transduction

33. Regulation of cell cycle by protein kinases

34. Cyclin-dependent protein kinases control cell cycle • By phosphorylating specific proteins at precise time intervals these kinases orchestrate the metabolic activities of the cell for cell division • Heterodimers – one regulatory subunit (cyclin) and one catalytic subunit (cyclin-dependent protein kinase [CDK])_

35. Post-translational regulation through phosphorylation and proteolysis

36. Four mechanisms to control CDK activity • Phosphorylation • Phosphorylate tyrosine prevents ATP binding • Removal of phosphate from tyrosine and phosphorylation of threonine allows substrate binding • Controlled degradation • Feedback loop involving DBRP • Regulated synthesis of CDKs and cyclins • MAPK mediated activation of Jun and Fos • Inhibition of CDK • Specific proteins such as p21 bind and inactivate CDK

37. Observe variations in the activities of specific CDKs during cell cycle

38. Whither MAPK?

39. MAPK kinase cascade • Many signals stimulate MAPK kinase cascade, but the wire is well conserved in biology – Handout • Why does MAPK kinase use three kinases instead of one? • Allows conversion of graded inputs into switch-like outputs

40. Regulation of passage from G1 to S

41. Neuron function and signal transduction • Voltage- and ligand-gated • ion channels

42. Allosteric effectors of protein structure/function

43. Glutamate receptor http://www.ibcp.fr/GGMM/Nimes/O11.html

44. Forming memories • http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/L/LTP.html • Mini-review handout • http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11807168&dopt=Abstract

45. Integrating circuits • Circuits exhibit synergy within a cellular context • Bhalla and Iyengar modeling signal transduction in the brain and long-term potentiation (LTP) (Fig 8.15) • http://doqcs.ncbs.res.in/~bhalla/doqcs/template.php?x=home&y=index • PKC activates MAPK, while MAPK helps activate PKC (Figure 8.16)

46. Why does it take 100 minutes of 5 nM EGF to reach LTP? • 10 min at 5 nM or 100 min at 2 nM EGF is insufficient for LTP (Fig 8.18) • Fig 8.19 result of determining concentration dependence of MAPK activation of PKC and the converse • Three intersection points – MM 8.2 “Cobweb” • A indicates high activity for both enzymes • B indicates low activity for both • T is threshold stimulation, if EGF is sufficient to activate either PKC or MAPK above T – both will reach A (T serves as a switch between A and B)

47. Turning off LTP • Use a phosphatase to knock MAPK below threshold • AA (arachidonic acid) generated by PLA2 persists, which makes it hard to turn off • Takes awhile to de-phosphatase

48. Integrating more circuits • Start with MAPK circuit • Add calcium activation, etc. • Result in Figure 8.23 • PKC • MAPK • cAMP • Calcium

49. A network algorithm • Derived in analogous fashion to protein interaction algorithm • Use RegulonDB as training set • Set up a matrix where the score = 1 if an operon (j) encodes a transcription factor that regulates another operon (I) to detect network motifs • Random model – maintain number of connections but partners are chosen randomly

50. Applied to several model networks • Biochemistry • Ecology • Neurobiology • Engineering (WWW)