Published on

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Why there’s any relationship between PL and Bio? Try to convince that we have much to contribute here.
  • EChang-SystemsBiology

    1. 1. Programming Languages for Biology Bor-Yuh Evan Chang November 25, 2003 OSQ Group Meeting
    2. 2. Biological Perspective F [] FF [Matsudaira et al. Molecular Cell Biology 4.0 . Freeman, 2000] F FF FF FF
    3. 3. Traditional Biological Research <ul><li>Experiments must focus on a small, specific piece of a system </li></ul><ul><ul><li>isolate the variable </li></ul></ul><ul><ul><li>feasibility </li></ul></ul><ul><li>Have led to an enormous wealth of (detailed) knowledge but in a fragmented form </li></ul>Cell Receptor Expert Virus Expert
    4. 4. Systems Biology <ul><li>Emerging area of biology </li></ul><ul><ul><li>study of the relationships and interactions between biological components </li></ul></ul><ul><ul><li>many thousand of molecules interact in complex series of reactions to perform some function (called a pathway) </li></ul></ul><ul><ul><ul><li>e.g., lactose interacting with a receptor triggers a series of actions to create the enzyme capable of breaking it down into usable form </li></ul></ul></ul><ul><ul><li>“pathways” may overlap </li></ul></ul>
    5. 5. Approaching Systems Biology <ul><li>Need a common language of describing/modeling all components of a system </li></ul><ul><ul><li>must be modular, compositional, and provided varying levels of abstraction </li></ul></ul><ul><li>Abstraction is an absolute necessity </li></ul><ul><ul><li>1 ribosome (eukaryotic) ¼ 82 proteins + rRNA </li></ul></ul><ul><ul><ul><li>1 protein ¼ hundreds/thousands amino acids </li></ul></ul></ul><ul><ul><li>1 membrane ¼ thousands of molecules (lipids, proteins, carbohydrates) </li></ul></ul>
    6. 6. The Biologist’s View <ul><li>How do biologists think about or view biological entities (e.g., proteins)? </li></ul><ul><ul><li>an entity can interact with certain other types of entities </li></ul></ul><ul><ul><li>an entity can be in a certain “state” </li></ul></ul><ul><ul><li>interaction causes some action or state change </li></ul></ul><ul><li>Analogous to a system of thousands of concurrent computational processes </li></ul><ul><ul><li>Walter Fontana, a theoretical biologist, examined  -calculus and linear logic for describing biological systems ( ¼ 1995). </li></ul></ul>
    7. 7. Example “Textbook” Description
    8. 8. Our Role <ul><li>Finding suitable abstractions for describing computation is our specialty! </li></ul><ul><li>Discovering/proving/checking properties of such descriptions (i.e., programs) is also our specialty! </li></ul><ul><li>Goal: </li></ul><ul><ul><li>Find a mathematical abstraction convenient for describing, reasoning, simulating biological systems </li></ul></ul><ul><ul><ul><li>DNA ! string over the alphabet {A,C,G,T} </li></ul></ul></ul><ul><ul><ul><ul><li>enables the use of string comparison algorithms </li></ul></ul></ul></ul><ul><ul><ul><li>Cellular Pathways ! ? </li></ul></ul></ul>
    9. 9. Outline <ul><li>Why PL is at all related to Biology? </li></ul><ul><li>Previous Abstractions in Biology </li></ul><ul><li>Possible Directions of Work </li></ul><ul><li>PML </li></ul><ul><li>Conclusion </li></ul>
    10. 10. Previous Abstractions <ul><li>Chemical kinetic models </li></ul><ul><ul><li>can derive differential equations </li></ul></ul><ul><ul><li>well-studied, with considerable theoretical basis </li></ul></ul><ul><ul><li>variables do not directly correspond with biological entities </li></ul></ul><ul><ul><li>may become difficult to see how multiple equations relate to each other </li></ul></ul>
    11. 11. Previous Abstractions <ul><li>Pathway Databases (e.g., EcoCyc, KEGG) </li></ul><ul><ul><li>store information in a symbolic form and provide ways to query the database </li></ul></ul><ul><ul><li>behavior of biological entities not directly described </li></ul></ul><ul><li>Petri nets </li></ul><ul><ul><li>directed bipartite multigraph ( P,T,E ) of places , transitions, and edges ; places contain tokens </li></ul></ul><ul><ul><li>place = molecular species, token = molecule, transition = reaction </li></ul></ul>2
    12. 12. Previous Abstractions <ul><li>Concurrent computational processes </li></ul><ul><ul><li>each biological entity is a process that may carry some state and interacts with other processes </li></ul></ul><ul><ul><li>each process described by a “program” </li></ul></ul><ul><ul><li>prior proposals based on process algebras, such as the  -calculus [Regev et al. ’01] </li></ul></ul>
    13. 13. Possible Directions of Work <ul><li>Biologically-motivated “process calculi” </li></ul><ul><ul><li>finding a suitable machine model to serve as a common basis for describing biological systems </li></ul></ul><ul><ul><li>Cardelli, Danos, Laneve, … </li></ul></ul><ul><li>High-level languages </li></ul><ul><ul><li>find suitable high-level languages to make descriptions closer to informal ones </li></ul></ul><ul><ul><li>[Chang and Sridharan ’03] </li></ul></ul><ul><li>Program analyses, simulation, and other tools </li></ul><ul><ul><li>simulation will likely be insufficient </li></ul></ul><ul><li>Creating models for obtaining results in biology </li></ul>
    14. 14. Outline <ul><li>Why PL is at all related to Biology? </li></ul><ul><li>Previous Abstractions in Biology </li></ul><ul><li>Possible Directions of Work </li></ul><ul><li>PML </li></ul><ul><li>Conclusion </li></ul>
    15. 15. Modeling in the  -calculus <ul><li>The  -calculus is concise and compact, yet powerful [Milner ’90] </li></ul><ul><ul><li>take this as the underlying machine model </li></ul></ul><ul><ul><li>not looking for another machine model </li></ul></ul><ul><li>However, it is far too low-level for direct modeling (ad-hoc structuring) </li></ul>
    16. 16. Informal Graphical Diagrams Protein Enzyme Protein Enzyme Enzyme Protein k k -1 k cat domains rules sites
    17. 17. PML: Enzyme bind_substrate parameterized declared in outer scope interactions within the complex Enzyme
    18. 18. PML: Protein bind_substrate bind_product Protein Protein
    19. 19. PML: A Simple System
    20. 20. Larger Models <ul><li>Modeled a general description of ER cotranslational-translocation </li></ul><ul><ul><li>unclearly or incompletely specified aspects became apparent </li></ul></ul><ul><ul><ul><li>e.g., can the signal sequence and translocon bind without SRP? Yes [Herskovits and Bibi ’00] </li></ul></ul></ul><ul><li>Extended to model targeting ER membrane with minor modifications </li></ul>
    21. 21. PML: Summary <ul><li>Domains </li></ul><ul><ul><li>set of mutually dependent binding sites </li></ul></ul><ul><ul><li>defines at the lowest-level the reactions a biological entity can undergo </li></ul></ul><ul><li>Groups </li></ul><ul><ul><li>static structure for controlling namespace </li></ul></ul><ul><ul><li>may represent a large biological entity </li></ul></ul><ul><ul><ul><li>large complex, a system, etc. </li></ul></ul></ul><ul><li>[Compartments] </li></ul><ul><ul><li>special groups that define boundaries </li></ul></ul><ul><li>Semantics defined via a translation to the  -calculus </li></ul>
    22. 22. PML: Summary <ul><li>Benefits </li></ul><ul><ul><li>easier to write and understand because of a more direct biological metaphor </li></ul></ul><ul><ul><li>block structure for controlling namespace and modularity </li></ul></ul><ul><li>Future Work </li></ul><ul><ul><li>naming? </li></ul></ul><ul><ul><li>proximity of molecules </li></ul></ul><ul><ul><li>integrating quantitative information (reaction rates, etc.) </li></ul></ul><ul><ul><li>type-checking PML specifications </li></ul></ul><ul><ul><li>exceptional / higher-level specifications </li></ul></ul><ul><ul><li>graphical and simulation tools </li></ul></ul>
    23. 23. Conclusion <ul><li>Systems biology needs a mathematical foundation </li></ul><ul><ul><li>languages for describing concurrent computation seem like a step in the right direction </li></ul></ul><ul><li>Status: all very preliminary </li></ul><ul><ul><li>biologically-motivated process calculi </li></ul></ul><ul><ul><ul><li>BioSPI, BioAmbients, Brane Calculus, … </li></ul></ul></ul><ul><ul><li>high-level languages </li></ul></ul><ul><ul><ul><li>PML </li></ul></ul></ul><ul><ul><li>analyses and tools (emerging) </li></ul></ul><ul><ul><li>creating models for results in biology (emerging) </li></ul></ul>
    24. 24. Conclusion <ul><li>Abundance of new challenges for PL </li></ul><ul><ul><li>language design: biologically-motivated operators </li></ul></ul><ul><ul><li>analysis and simulation: dealing with the scale </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>How much biology does one need to learn to begin? </li></ul>
    25. 26. Bonus Slides
    26. 27. Compartments
    27. 28. Compartments <ul><li>Critical part of biological pathways </li></ul><ul><ul><li>prevents interactions that would otherwise occur </li></ul></ul><ul><li>Description of the behavior of a molecule should not depend on the compartment </li></ul><ul><li>Regev et al. use “private” channels in the  -calculus for both complexing and compartmentalization </li></ul>
    28. 29. PML: Simple Compartments Example MolB bind_a bind_a MolA
    29. 30. PML: Simple Compartments Example MolA MolB ER Cytosol CytERBridge
    30. 31. PML: Simple Compartments Example MolB ER Cytosol CytERBridge MolA
    31. 32. Semantics of PML
    32. 33. Semantics of PML <ul><li>Defined in terms of the  -calculus via two translations </li></ul><ul><ul><li>from PML to CorePML </li></ul></ul><ul><ul><ul><li>“ flattens” compartments, removes bridges </li></ul></ul></ul>
    33. 34. Semantics of PML <ul><ul><li>from CorePML to the  -calculus </li></ul></ul>
    34. 35. Syntax of PML
    35. 36. Syntax of PML
    36. 37. Syntax of PML
    37. 38. Example: Cotranslational Translocation
    38. 39. Example: Cotranslational Translocation <ul><li>Ribosome translates mRNA exposing a signal sequence </li></ul><ul><li>Signal sequence attracts SRP stopping translation </li></ul><ul><li>SRP receptor (on ER membrane) attracts SRP </li></ul><ul><li>Signal sequence interacts with translocon, SRP disassociates resuming translation </li></ul><ul><li>Signal peptidase cleaves the signal sequence in the ER lumen, Hsc70 chaperones aid in protein folding </li></ul>
    39. 40. Example: Cotranslational Translocation
    40. 41. Example: Cotranslational Translocation
    41. 42. Example: Cotranslational Translocation
    42. 43. Example: Cotranslational Translocation
    43. 44. Example: Cotranslational Translocation
    44. 45. Example: Cotranslational Translocation
    45. 46. Example: Cotranslational Translocation
    1. A particular slide catching your eye?

      Clipping is a handy way to collect important slides you want to go back to later.