A stochastic model for stress response in CHO mammalian cells

1. A stochastic model for stress response in CHO mammalian cells SAMSI Discrete Models in Systems Biology December 3-5, 2008 Ovidiu Lipan Physics Department University of Richmond, Virginia

3. Supra-chiasmatic nucleus (SCN): The master pacemaker in mammals From: Moore-Ede, Sulzman and Fuller (eds.) The Clock That Times Us

4. Experimental design • Mice were entrained to a 12:12 light-dark cycle for 2 weeks • Animals were then placed in constant dim white light (<1 Lux) for 42 hr • Tissues were collected at 4-hr intervals over two circadian cycles (12time-points) • RNA of one mouse per time point was analyzed on oligonucleotide arrays (Affymetrix U74Av2)

5. Single profiles of genes showing circadian regulation in both liver and heart

7. STRESS l l l f h k R I M C S T A H S i i i i i i t t t t t t o r m o o e s n r e s s : r a n s c r p o n a c v a o n o e a o c . . , ( ) G S i 1 9 9 3 2 5 9 1 4 0 9 b l h l l A j i i i i t e n e s c e n c e m a o r q u e s o n n o o g y s o w c e s c o p e . , , h d h h h i i i i i t t t w r a p c a n g e s n e r e n v r o n m e n s u c , l d h l t t t t t a s e x p o s u r e o e e v a e e m p e r a u r e s e a v y m e a s , , b l d l f i i i i t t a c e r a a n v r a n e c o n s . h b l h l l h I i t t t a s e c o m e c e a r a a o r g a n s m s s a r e a l l h l d d i i t t t c o m m o n m o e c u a r r e s p o n s e a n c u e s a r a m a c h h f d h i i t t t t c a n g e n e p a e r n o g e n e e x p r e s s o n a n e l d h f f l f h h k i i t t t e e v a e s y n e s s o a a m y o e a s o c d d i i t t o r s r e s s - n u c e p r o e n s . h k l d H i i t t e a s o c p r o e n s e n s u r e s u r v v a u n e r f l d h f l f h k d i i i t t t t t s r e s s u c o n o n s a e u n c e c e , , l d l d l l l l d h i t t t t w o u e a u m a e y o c e e a .

8. h d l l l l l f h k R I I M C S T A H S i i i i i i t t t t t t t t n e u o n r s m r o e s o s e c e e s n r e s s : r a n s c r p o n a c v a o n o e a o c . . , , ( ) d G H S F S i i i i i 1 1 9 9 3 2 5 9 1 4 0 9 t e n e s s m a n a c n e e n c e n a . , , b d D N A i i i m o n o m e r c n o n - n n g , f h h k U H S F 1 t o r m p o n e a s o c . , b l i i t t a s s e m e s n o a r m e r , b d ¯ i i t n s o s p e c c s e q u e n c e l h h k i t t e e m e n s n e a s o c l T i i t t p r o m o e r r a n s c r p o n a . f h h k i i t t t a c v a o n o e a s o c g e n e l d d l l f i t e a e s o n c r e a s e e v e s o h l l d F H S F i i i 7 0 t s p n a y s s o c a e s . , f h d D N A i t r o m e a n s l l d t t t e v e n u a y c o n v e r e o b d i i n o n - n n g m o n o m e r s

9. / / / h i t t p : w w w m c r o s c o p y u c o m . . Chinese-hamster ovary cells (CHO)

10. l h d b l l G i i t t t e n e r a a p p r o a c o s u y a o o g c a s y s e m

11. l i d i k l b d ' P A D N A i i i 5 3 5 t t t t a s m c o n s r u c o n : - o a s e c o n a n n g p r o m o e r a n - . l d f h h b l d f l b d i 7 0 1 t t t u n r a n s a e r e g o n o e m o u s e s p g e n e w a s s u c o n e r o m a a m a . h l h d ¯ d b l b i i i i i i 7 0 1 t p a g e c o n e c a r r y n g a n s p g e n e e n e y g e n o m c r a r y s c r e e n n g . ( ) h h b d f h S D N A A D N A i i 7 0 1 t t t r a a g e n e u s n g a u m a n s p c a s a p r o e c c o n g o r e . . h l l f l d f G F P A S V T i i i i 4 0 t t t w a p o y s g n a r o m a r g e a n g e n g e n e w a s e n g n e e r e o u s e ( ) h d f h h h h d A T G T i i 7 0 1 t t t t t t o e s a r c o o n o e s p g e n e e c m e r a g e n e w a s n s e r e . . h d l S P i i i i i i 7 2 t t t t t t n o a p v e c o r c o n a n n g a y g r o m y c n r e s s a n c e g e n e n o r e r o s e e c f b l f t t t t o r s a e r a n s e c a n s .

12. ( ) i f f l l P C H O K A T C C M V A 1 t t t t r e p a r a o n o r a n s e c a n s : - c e s a n a s s a s , , ( ) l h l l l l d M E M C i i i i i i i t t t w e r e g r o w n n - a p a e g r o c o n a n n g p e n c n s r e p o m y c n a n a m - , ( ) % ( ) h l l d l d h d C F B S G B P i i i i i i 1 0 t t t t p o e r c n e g r o a n c o m p e m e n e w e m n o - r o u c s . ( ) l l f d b l f f C L I i i i i i i i t t t t t e s w e r e r a n s e c e y p o e c o n u s n g p o e c a m n e n v r o g e n a s p r e v - ( / ) l d b d f d f l h l A L i i i i i 1 0 5 0 0 t t o u s y e s c r e e r a y s o s e e c o n n y g r o m y c n ¹ g m s n g e . , l l l d d b l d l h f d b T i i i i i i i t t c e c o n e s w e r e e r v e y m n g u o n e s c r e e n n g w a s p e r o r m e y . ( ) ° k d l h l b l ° N T E E i i i i 2 0 0 0 t e p u o r e s c e n c e o n a n c o n e s w a o w a s a u o r e s c e n c e n - l d d l ¯ d f d d l b ° i i i i i t t t t t t t t e n s y w e r e s e e c e a n a m p e o r a o n a e s n g y o w c y o m e r y .

13. The HSP70-GFP construct

14. An example of GFP in CHO cells www.panomics.com/images/36_3_CELLS_2_V1.jpg

15. Flow cytometry BD Biosciences LSR II. http://home.ncifcrf.gov/ccr/flowcore/instru_LSR.JPG

17. h d b l l h h k T i t t t e o u e e x p o n e n a r e s p o n s e o e a s o c s

24. The stochastic modelfor the heat shock

25. State

35. State A finite set of transitions Master equation Transition probability rates Moments

36. Generating function Fallingfactorial polynomials

37. Factorial moments Stirling numbers of the second kind

38. Boundary condition

39. State

45. Subgroup Factorial cumulants, Young tableaux and Faadi Bruno formula

47. Concatenation

48. Time evolution equation for factorial cumulants

50. Solution to the linear stochastic genetic network State Signal generators Transition probability rates