1. 1 The Ribosome Flow Model Michael Margaliot School of Elec. Eng. Tel Aviv University, Israel Tamir Tuller (Tel Aviv University) Eduardo D. Sontag (Rutgers University) Joint work with:

2. 2 Overview Ribosome flow Mathematical models: from TASEP to the Ribosome Flow Model (RFM) Analysis of the RFM+biological implications:  Contraction (after a short time)  Monotone systems  Continued fractions

3. 3 From DNA to Proteins Transcription: the cell’s machinery copies the DNA into mRNA The mRNA travels from the nucleus to the cytoplasm Translation: ribosomes “read” the mRNA and produce a corresponding chain of amino-acids

4. 4 Translation http://www.youtube.com/watch? v=TfYf_rPWUdY

5. 5 Ribosome Flow During translation several ribosomes read the same mRNA. Ribosomes follow each other like cars traveling along a road. Mathematical models for ribosome flow: TASEP* and the RFM. *Zia, Dong, Schmittmann, “Modeling Translation in Protein Synthesis with TASEP: A Tutorial and Recent Developments”, J. Statistical Physics, 2011

6. 6 Totally Asymmetric Simple Exclusion Process (TASEP) Particles can only hop to empty sites (SE) Movement is unidirectional (TA) A stochastic model: particles hop along a lattice of consecutive sites

7. Simulating TASEP 7 At each time step, all the particles are scanned and hop with probability, if the consecutive site is empty. This is continued until steady state.

8. 8 Analysis of TASEP* 8 *Schadschneider, Chowdhury & Nishinari, Stochastic Transport in Complex Systems: From Molecules to Vehicles, 2010. 1.Mean field approximations 2.Bethe ansatz

9. Ribosome Flow Model* *Reuveni, Meilijson, Kupiec, Ruppin & Tuller, “Genome-scale analysis of translation elongation with a ribosome flow model”, PLoS Comput. Biol., 2011 9 A deterministic model for ribosome flow. mRNA is coarse-grained into consecutive sites. Ribosomes reach site 1 with rate, but can only bind if the site is empty.

10. Ribosome Flow Model 10 (normalized) number of ribosomes at site i State-variables : Parameters: >0 initiation rate >0 transition rates between consecutive sites

11. 11 Ribosome Flow Model

12. 12 Ribosome Flow Model Just like TASEP, this encapsulates both unidirectional movement and simple exclusion.

13. Simulation Results All trajectories emanating from remain in, and converge to a unique equilibrium point e. 13 e

14. Analysis of the RFM Uses tools from: 14 Contraction theory Monotone systems theory Analytic theory of continued fractions

15. Contraction Theory* The system: 15 is contracting on a convex set K, with contraction rate c>0, if for all * Lohmiller & Slotine, “On Contraction Analysis for Nonlinear Systems”, Automatica, 1988.

16. Contraction Theory Trajectories contract to each other at an exponential rate. 16 a b x(t,0,a) x(t,0,b)

17. Implications of Contraction 1. Trajectories converge to a unique equilibrium point; 17 2. The system entrains to periodic excitations.

18. Contraction and Entrainment* Definition is T-periodic if 18 *Russo, di Bernardo, Sontag, “Global Entrainment of Transcriptional Systems to Periodic Inputs”, PLoS Comput. Biol., 2010. Theorem The contracting and T-periodic system admits a unique periodic solution of period T, and

19. How to Prove Contraction? The Jacobian of is the nxn matrix 19

20. How to Prove Contraction? The infinitesimal distance between trajectories evolves according to 20 This suggests that in order to prove contraction we need to (uniformly) bound J(x).

21. How to Prove Contraction? Let be a vector norm. 21 The induced matrix norm is: The induced matrix measure is:

22. How to Prove Contraction? Intuition on the matrix measure: 22 Consider Then to 1 st order in so

23. Proving Contraction Theorem Consider the system 23 If for all then the Comment 1: all this works for system is contracting on K with contraction rate c. Comment 2: is Hurwitz.

24. Application to the RFM For n=3, 24 and for the matrix measure induced by the L 1 vector norm: for all The RFM is on the “verge of contraction.”

25. RFM is not Contracting on C For n=3: 25 so for is singular and thus not Hurwitz.

26. Contraction After a Short Transient (CAST)* Definition is a CAST if 26 *M., Sontag & Tuller, “Entrainment to Periodic Initiation and Transition Rates in the Ribosome Flow Model”, submitted, 2013. there exists such that -> Contraction after an arbitrarily small transient in time and amplitude.

27. Motivation for Contraction after a Short Transient (CAST) Contraction is used to prove asymptotic properties (convergence to equilibrium point; entrainment to a periodic excitation). 27

28. Application to the RFM Theorem The RFM is CAST on. 28 Corollary 1 All trajectories converge to a unique equilibrium point e.* *M.& Tuller, “Stability Analysis of the Ribosome Flow Model”, IEEE TCBB, 2012. Biological interpretation: the parameters determine a unique steady-state of ribosome distributions and synthesis rate; not affected by perturbations.

29. Entrainment in the RFM 29

30. Application to the RFM Theorem The RFM is CAST on C. 30 Corollary 2 Trajectories entrain to periodic initiation and/or transition rates (with a common period T).* Biological interpretation: ribosome distributions and synthesis rate converge to a periodic pattern, with period T. *M., Sontag & Tuller, “Entrainment to Periodic Initiation and Transition Rates in the Ribosome Flow Model”, submitted, 2013.

31. Entrainment in the RFM 31 Here n=3,

32. Analysis of the RFM Uses tools from: 32 Contraction theory Monotone systems theory Analytic theory of continued fractions

33. Monotone Dynamical Systems* Define a (partial) ordering between vectors in R n by: 33 *Smith, Monotone Dynamical Systems: An Introduction to the Theory of Competitive and Cooperative Systems, AMS, 1995 Definition is called monotone if i.e., the dynamics preserves the partial ordering.

34. Monotone Dynamical Systems in the Life Sciences Used for modeling a variety of biochemical networks:* -behavior is ordered and robust with respect to parameter values -large systems may be modeled as interconnections of monotone subsystems. 34 *Sontag, “Monotone and Near-Monotone Biochemical Networks”, Systems & Synthetic Biology, 2007

35. When is a System Monotone? Theorem (Kamke Condition.) Suppose that f satisfies: 35 then is monotone. Intuition: assume monotonicity is lost, then

36. Verifying the Kamke Condition Theorem cooperativity Kamke condition ( system is monotone) 36 This means that increasing increases Definition is called cooperative if

37. Application to the RFM Every off-diagonal entry is non- negative on C. Thus, the RFM is a cooperative system. 37 Proposition The RFM is monotone on C. Proof :

38. RFM is Cooperative increase. A “traffic jam” in a site induces “traffic jams” in the neighboring sites. 38 Intuition if x 2 increases then and

39. RFM is Monotone 39 Biological implication: a larger initial distribution of ribosomes induces a larger distribution of ribosomes for all time.

40. Analysis of the RFM Uses tools from: 40 Contraction theory Monotone systems theory Analytic theory of continued fractions

41. 41 Continued Fractions Suppose (for simplicity) that n =3. Then Let denote the unique equilibrium point in C. Then

42. 42 Continued Fractions This yields: Every e i can be expressed as a continued fraction of e 3...

43. 43 Continued Fractions Furthermore, e 3 satisfies:.... This is a second-order polynomial equation in e 3. In general, this is a th–order polynomial equation in e n.

44. 44 Homogeneous RFM In certain cases, all the transition rates are approximately equal.* In the RFM this can be modeled by assuming that *Ingolia, Lareau & Weissman, “Ribosome Profiling of Mouse Embryonic Stem Cells Reveals the Complexity and Dynamics of Mammalian Proteomes”, Cell, 2011 This yields the Homogeneous Ribosome Flow Model (HRFM). Analysis is simplified because there are only two parameters.

45. 45 HRFM and Periodic Continued Fractions In the HRFM, This is a periodic continued fraction, and we can say a lot more about e.

46. 46 Equilibrium Point in the HRFM* Theorem In the HRFM, *M. & Tuller, “On the Steady-State Distribution in the Homogeneous Ribosome Flow Model”, IEEE TCBB, 2012 Biological interpretation: This provides an explicit expression for the capacity of a gene.

47. mRNA Circularization* 47 * Craig, Haghighat, Yu & Sonenberg, ”Interaction of Polyadenylate-Binding Protein with the eIF4G homologue PAIP enhances translation”, Nature, 1998

48. RFM as a Control System This can be modeled by the RFM with Input and Output (RFMIO): 48 *Angeli & Sontag, “Monotone Control Systems”, IEEE TAC, 2003 and then closing the loop via Remark: The RFMIO is a monotone control system.*

49. RFM with Feedback* 49 Theorem The closed-loop system admits an equilibrium point e that is globally attracting in C. *M. & Tuller, “Ribosome Flow Model with Feedback”, J. Royal Society -Interface, to appear Biological implication: as before, but this is probably a better model for translation in eukaryotes.

50. RFM with Feedback* 50 Theorem In the homogeneous case, where Biological implication: may be useful, perhaps, for re-engineering gene translation.

51. Further Research 51 1. Analyzing translation: sensitivity analysis; optimizing translation rate; adding features (e.g. drop-off); estimating initiation rate;… 2. TASEP has been used to model: biological motors, surface growth, traffic flow, walking ants, Wi-Fi networks,….

52. Summary 52 The Ribosome Flow Model is: (1) useful; (2) amenable to analysis. Papers available on-line at: www.eng.tau.ac.il/~michaelm Recently developed techniques provide more and more data on the translation process. Computational models are thus becoming more and more important. THANK YOU!