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Programming Languages for Biology

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					Programming Languages for Biology
Bor-Yuh Evan Chang November 25, 2003 OSQ Group Meeting

Biological Perspective
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F [http://www.nocturnalvisions.freeservers.com/page6.html] FF [Matsudaira et al. Molecular Cell Biology 4.0. Freeman, 2000]

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Traditional Biological Research
• Experiments must focus on a small, specific piece of a system
– isolate the variable – feasibility

• Have led to an enormous wealth of (detailed) knowledge but in a fragmented form

Virus Expert
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Cell Receptor Expert
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Systems Biology
• Emerging area of biology
– study of the relationships and interactions between biological components – many thousand of molecules interact in complex series of reactions to perform some function (called a pathway)
• e.g., lactose interacting with a receptor triggers a series of actions to create the enzyme capable of breaking it down into usable form

– “pathways” may overlap

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Approaching Systems Biology
• Need a common language of describing/modeling all components of a system
– must be modular, compositional, and provided varying levels of abstraction

• Abstraction is an absolute necessity
– 1 ribosome (eukaryotic) ¼ 82 proteins + rRNA – 1 membrane ¼ thousands of molecules (lipids, proteins, carbohydrates)
• 1 protein ¼ hundreds/thousands amino acids

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The Biologist’s View
• How do biologists think about or view biological entities (e.g., proteins)?
– an entity can interact with certain other types of entities – an entity can be in a certain “state” – interaction causes some action or state change

• Analogous to a system of thousands of concurrent computational processes
– Walter Fontana, a theoretical biologist, examined -calculus and linear logic for describing biological systems (¼1995).
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Example “Textbook” Description

http://vcell.ndsu.nodak.edu/~christjo/vcell/animationSite/lacOperon/

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Our Role
• Finding suitable abstractions for describing computation is our specialty! • Discovering/proving/checking properties of such descriptions (i.e., programs) is also our specialty! • Goal:
– Find a mathematical abstraction convenient for describing, reasoning, simulating biological systems
• DNA ! string over the alphabet {A,C,G,T} • Cellular Pathways ! ?
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– enables the use of string comparison algorithms

Outline
• • • • • Why PL is at all related to Biology? Previous Abstractions in Biology Possible Directions of Work PML Conclusion

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Previous Abstractions
• Chemical kinetic models

– can derive differential equations – well-studied, with considerable theoretical basis – variables do not directly correspond with biological entities – may become difficult to see how multiple equations relate to each other
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Previous Abstractions
• Pathway Databases (e.g., EcoCyc, KEGG)
– store information in a symbolic form and provide ways to query the database – behavior of biological entities not directly described

• Petri nets
– directed bipartite multigraph (P,T,E) of places, transitions, and edges; places contain tokens – place = molecular species, token = molecule, transition = reaction
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Previous Abstractions
• Concurrent computational processes
– each biological entity is a process that may carry some state and interacts with other processes – each process described by a “program” – prior proposals based on process algebras, such as the -calculus [Regev et al. ’01]

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Possible Directions of Work
• Biologically-motivated “process calculi”
– finding a suitable machine model to serve as a common basis for describing biological systems – Cardelli, Danos, Laneve, …

• High-level languages
– find suitable high-level languages to make descriptions closer to informal ones – [Chang and Sridharan ’03]

• Program analyses, simulation, and other tools
– simulation will likely be insufficient

• Creating models for obtaining results in biology
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Outline
• • • • • Why PL is at all related to Biology? Previous Abstractions in Biology Possible Directions of Work PML Conclusion

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Modeling in the -calculus
• The -calculus is concise and compact, yet powerful [Milner ’90]
– take this as the underlying machine model – not looking for another machine model

• However, it is far too low-level for direct modeling (ad-hoc structuring)

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Informal Graphical Diagrams
k-1
Protein Enzyme sites Protein Enzyme

k

rules Protein

kcat

Enzyme

domains
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PML: Enzyme
parameterized bind_substrate

Enzyme

declared in outer scope
interactions within the complex

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PML: Protein
Protein
bind_substrate

Protein

bind_product

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PML: A Simple System

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Larger Models
• Modeled a general description of ER cotranslational-translocation
– unclearly or incompletely specified aspects became apparent
• e.g., can the signal sequence and translocon bind without SRP? Yes [Herskovits and Bibi ’00]

• Extended to model targeting ER membrane with minor modifications

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PML: Summary
• Domains
– set of mutually dependent binding sites – defines at the lowest-level the reactions a biological entity can undergo

• Groups
– static structure for controlling namespace – may represent a large biological entity
• large complex, a system, etc.

• [Compartments]
– special groups that define boundaries

• Semantics defined via a translation to the calculus
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PML: Summary
• Benefits
– easier to write and understand because of a more direct biological metaphor – block structure for controlling namespace and modularity

• Future Work
– – – – – –
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naming? proximity of molecules integrating quantitative information (reaction rates, etc.) type-checking PML specifications exceptional / higher-level specifications graphical and simulation tools
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Conclusion
• Systems biology needs a mathematical foundation
– languages for describing concurrent computation seem like a step in the right direction

• Status: all very preliminary
– biologically-motivated process calculi
• BioSPI, BioAmbients, Brane Calculus, …

– high-level languages
• PML

– analyses and tools (emerging) – creating models for results in biology (emerging)

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Conclusion
• Abundance of new challenges for PL
– language design: biologically-motivated operators – analysis and simulation: dealing with the scale –…

• How much biology does one need to learn to begin?

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Bonus Slides

Compartments

Compartments
• Critical part of biological pathways
– prevents interactions that would otherwise occur

• Description of the behavior of a molecule should not depend on the compartment • Regev et al. use “private” channels in the calculus for both complexing and compartmentalization

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PML: Simple Compartments Example
MolB bind_a bind_a MolA

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PML: Simple Compartments Example
ER
MolB CytERBridge

Cytosol
MolA

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PML: Simple Compartments Example
ER
MolB CytERBridge

Cytosol
MolA

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Semantics of PML

Semantics of PML
• Defined in terms of the -calculus via two translations
– from PML to CorePML
• “flattens” compartments, removes bridges

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Semantics of PML
– from CorePML to the -calculus

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Syntax of PML

Syntax of PML

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Syntax of PML

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Example: Cotranslational Translocation

Example: Cotranslational Translocation
• Ribosome translates mRNA exposing a signal sequence • Signal sequence attracts SRP stopping translation • SRP receptor (on ER membrane) attracts SRP • Signal sequence interacts with translocon, SRP disassociates resuming translation • Signal peptidase cleaves the signal sequence in the ER lumen, Hsc70 chaperones aid in protein folding

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Example: Cotranslational Translocation

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Example: Cotranslational Translocation

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Example: Cotranslational Translocation

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Example: Cotranslational Translocation

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Example: Cotranslational Translocation

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Example: Cotranslational Translocation

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Example: Cotranslational Translocation

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