1. Diana Hermith, BSc. Molecular Biology dhermith@javerianacali.edu.co Graduate Student Program in Engineering Emphasis in Computer Systems (Graduate Research Draft Proposal) Research in Avispa: Concurrency Theory and Applications Pontificia Universidad Javeriana, Cali Cali (Colombia), Tuesday January 13 th 2009 Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

2. Agenda I. Introduction II. State of the Art (Short) III. Detailed Description of the G Protein Signal Cascade IV. Why to develop a model by using NTCC calculus? References Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

4. Source: Author: Kieran O'Neill A modern illustration of the 1970 version of the central dogma of molecular biology, after the diagrams in the original article: 1970 Crick, F., Central Dogma of Molecular Biology. Nature 227, 561-563 Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

5. American Chemical Society, Jun Xu, Ph. D., January 24, 2008, San Diego Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

6. American Chemical Society, Jun Xu, Ph. D., January 24, 2008, San Diego Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

7. http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

8. Introduction Most of biological functions are mediated by protein interactions. These interactions can be physical, such as when two proteins form a complex, or “logical,” such as when one or more proteins control the behavior of one or more other proteins without physical interaction. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

9. Introduction Metabolic pathways provide us with many examples of logical interactions. The concentration of a product is often “sensed” by other proteins in its synthetic cascade and modulates their activity. The presence of hormones is detected by cell surface receptors and transmitted to other proteins in the cell that can interact with the genetic material to activate or repress genes. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

10. Introduction In biology, signal transduction refers to any process by which a cell converts one kind of signal or stimulus into another. Most processes of signal transduction involve ordered sequences of biochemical reactions inside the cell, which are carried out by enzymes, activated by second messengers, resulting in a signal transduction pathway. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

11. Introduction An environmental signal, such as a hormone, is first received by interaction with a cellular component, most often a cell-surface receptor. The information that the signal has arrived is then converted into other chemical forms, or transduced. The signal is often amplified before evoking a response. Feedback pathways regulate the entire signaling process. Signal Transduction Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

12. State of the Art (Short) Cell Signaling or Signal Transduction, is the study of the mechanisms that enable the transfer of biological information. Signaling impinges on all aspects of biology, from development to disease. Many diseases, such as cancer, involve malfunction of signal transduction pathways. Mathematical modeling and simulation in this field has the porpuse to help and guide the biologist in designing experiments and generally to establish a conceptual framework in which to think (Kitano et al, 2003). Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

13. State of the Art (Short) Thus, a more complete understanding of the fundamental properties of GPCRs and how they interact with, and activate, their target G-proteins is of utmost importance to future drug discovery (Johnston et al, 2006). How GPCRs operate is one of the most fundamental questions in the field of transmembrane signal transduction. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

14. State of the Art (Short) In particular, models often fail to account for the complexities of protein-protein interactions, such as how these interactions depend on contextual details at the level of protein sites. New modeling approaches that address this problem involve the use of rules to represent protein-protein interactions, rules are also useful for representing other types of biomolecular interactions. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

15. State of the Art (Short) The introduction of rules greatly eases the task of specifying a model that incorporates details at the level of protein sites. A rule—such as “ligand binds receptor with rate constant k whenever ligand and receptor have free binding sites”— describes the features of reactants that are required for a particular type of chemical transformation to take place. Rules simplify the specification of a model when the reactivity of a component in a system is determined by only a subset of its possible features (Hlavacek et al, 2006). Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

16. State of the Art (Short) Other authors propose that the concurrency paradigm and the pi calculus theory are uniquely suited to model and study biomolecular processes in general and Signaling Transduction pathways in particular. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

17. State of the Art (Short) Within the particular framework of the pi calculus, they set three principles for this correspondence; first, as primitive process, they choose the functional signaling domain. Second, they model the component residues of domains as communication channels that construct a process. Finally, molecular interaction and modification is modeled as communication and the subsequent change of channel names. Based on these three principles the pi calculus allows to fully represent complex molecular structures and signaling events (Shapiro et al, 2000). Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

18. State of the Art (Short) Table 1. Pi calculus modeling of typical molecular structures involved in Signaling Transduction Pathways and key signaling events. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

19. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

20. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

21. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

22. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

23. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

24. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

25. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

26. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions http://www.scribd.com/doc/48863/CELL-SIGNALING?autodown=pdf

27. G Protein Signal Cascade Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions G Protein Signal Cascade ANIMATION G Protein Signal Cascade ANIMATION

28. Why Models using NTCC Calculus Partial information arises naturally in the description of biological systems. It is possible to distinguish two main kinds of partial information when modeling those systems: quantitative and behavioral. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

29. Why Models using NTCC Calculus While partial quantitative information usually involves incomplete information on the state of the system (e.g., the set of possible values that a variable can take), partial behavioral information refers to the uncertainty associated to behavior of interactions (e.g., the unknown relative speeds on which two systems interact). Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

30. Why Models using NTCC Calculus In NTCC the above-mentioned kinds of partial information are naturally captured. On the one hand, partial quantitative information is captured by the notion of constraint system, a structure that gives coherence and defines (logic) inference capabilities over constraints. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

31. Why Models using NTCC Calculus On the other hand, partial behavioral information is represented by non-deterministic and asynchronous operators available in ntcc. The interplay of these operators in the discrete time of ntcc allows to explicitly describe and reason about the uncertainty in the occurrence time of Signal-transduction pathways. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

32. Why Models using NTCC Calculus Signal-transduction pathways can be viewed as a Reactive system that consists of parallel processes, where each process may change state in reaction to another process changing state, cells constantly send and receive signals and operate under various conditions simultaneously. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

33. Why Models using NTCC Calculus Signal-transduction pathways can be viewed as a Nondeterministic system, that may have several possible reactions to the same stimulus. Hence, nondeterministic models capture the diverse behavior often observed in Signal-transduction pathways by allowing different choices of execution, without assigning priorities or probabilities to each choice. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

34. Why Models using NTCC Calculus Signal-transduction pathways can be viewed as a Concurrent System, that consist of many processes running in parallel and sharing common resources. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions

35. Why Models using NTCC Calculus Biological Description Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Copyright © 1999-2008 by Joyce J. Diwan. All rights reserved.

36. Turn on of the signal: 1. Initially G  has bound GDP, and  &  subunits are complexed together. G , , the complex of  &  subunits, inhibits G . Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Why Models using NTCC Calculus

37. 2. Hormone binding, usually to an extracellular domain of a 7-helix receptor (GPCR), causes a conformational change in the receptor that is transmitted to a G-protein on the cytosolic side of the membrane. The nucleotide-binding site on G  becomes more accessible to the cytosol, where [GTP] > [GDP]. G  releases GDP & binds GTP (GDP-GTP exchange). Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Why Models using NTCC Calculus

38. 3. Substitution of GTP for GDP causes another conformational change in G . G  -GTP dissociates from the inhibitory  complex & can now bind to and activate Adenylate Cyclase. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Why Models using NTCC Calculus

39. 4. Adenylate Cyclase, activated by the stimulatory G  -GTP, catalyzes synthesis of cAMP. 5. Protein Kinase A (cAMP Dependent Protein Kinase) catalyzes transfer of phosphate from ATP to serine or threonine residues of various cellular proteins, altering their activity. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Why Models using NTCC Calculus

40. Turn off of the signal: 1. G  hydrolyzes GTP to GDP + P i. (GTPase). The presence of GDP on G  causes it to rebind to the inhibitory  complex. Adenylate Cyclase is no longer activated. 2. Phosphodiesterases catalyze hydrolysis of cAMP  AMP. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Why Models using NTCC Calculus

41. 3. Receptor desensitization varies with the hormone. In some cases the activated receptor is phosphorylated via a G- protein Receptor Kinase. The phosphorylated receptor then may bind to a protein  -arrestin.  -Arrestin promotes removal of the receptor from the membrane by clathrin-mediated endocytosis.  -Arrestin may also bind a cytosolic Phosphodiesterase, bringing this enzyme close to where cAMP is being produced, contributing to signal turnoff. 4. Protein Phosphatase catalyzes removal by hydrolysis of phosphates that were attached to proteins via Protein Kinase A. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Why Models using NTCC Calculus

42. Signal amplification Signal amplification is an important feature of signal cascades:  One hormone molecule can lead to formation of many cAMP molecules.  Each catalytic subunit of Protein Kinase A catalyzes phosphorylation of many proteins during the life-time of the cAMP. Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Why Models using NTCC Calculus

43. The goal will be find an appropriate NTCC model for G Protein Signal Cascade that include molecular structure, behavior and biological formal semantics. What kind of expected results we are thinking to obtain: a unified view of structure and dynamics of G Protein Signal Cascade, a detailed molecular information (complexes, molecules, domains, residues) in visible form, a complex dynamic behavior (feedback, cross-talk, split and merge), a modular system. For more details and References, please visit: http://dianahermith.phipages.com/research/ Using a Timed Concurrent Constraint Process Calculus for Modeling Biomolecular Interactions Why Models using NTCC Calculus