1. Stochastic, Spatial and Concurrent Biological Processes Modeling Yifei Bao, Eduardo Bonelli, Philippe Bidinger, Justin Sousa, Vishakha Sharma Advisor: Adriana Compagnoni Department of Computer Science Joint work with Libera’s lab and Sukhishvili’s lab from Department of CCBBME

2. Objective Construct a language to model and simulate biological processes. Apply it for the modeling of a drug delivery nano-system.

3. Outline Motivating example: Bio Film System Survey for Existing Modeling Techniques Our Contribution: A Simulation Language Ongoing and Future Work Project Demo

4. Drug Delivery System Biofilms are loaded with antibiotics and they are used to coat medical implants. When the pH changes due to infection, the Biofilm releases molecules of antibiotics.

5. Sequential release of bioactive molecules from layer-by-layer films

6. Bio Film System increasing pHbasic/neutral 3.2 μm fast release of capsule cargo

7. Data from Prof. Sukhishvili’s Lab Relationship between release of drug molecules and PH with respect to time.

8. Computational Model Motivation: – Wet lab experiments are costly – Some data are difficult to observe (local pH) Predict interactions between species  Bacteria  Drug Molecule Predict local PH Visualization of Bio system

9. SPIM Concurrent communicating processes – Processes evolve concurrently – Synchronize by message passing Successfully used for modeling biological systems – Process = Molecule (with state) – Synchronization = Reaction Existing implementation Simulation and visualization 4000 lines of ML (Ocaml, F#) code

10. SPiM Model

11. SPiM not suitable for Bio Film example SPiM assumes reactions occur in homogeneous mixture Not applicable to Bio Film example (antibiotic stored in film – not in solution)

12. Spatial modeling is needed Reaction distance: only molecules close enough can react. Reaction boundary: the movements and reactions should occur in specific areas. Shape of Binding Sites : only matching shapes can bind.

13. Existing modeling methods Lack spatial attributes: ODEs, SPiM, Kappa, Petri Nets. Limited notion of space: BioAmbinet, BioPepa, StochSim. Lack stochasticity: SpacePi. Very ad hoc models.

14. Our Contribution A language for the simulation of stochastic biological processes with spatial information –An extension of the SPIM language –Language definition and implementation Model of the Biofilm system

15. SPIM SPiM Assumption: all molecules (processes) are assumed to be uniformly distributed in space Interactions scheduled randomly based on concentrations and reaction rates – Informally: interaction involving higher concentrations and rates are more likely to occur Gillespie algorithm

16. Spatial Features Process state includes spatial information – Each process has a position and three vectors that define its local system of coordinates This state can be modified by application of affine maps (translation, rotation..) – Simulation of movement (translation, rotations) Interactions may be conditioned by the distance between two molecules

17. Spatial Features

18. Implementation Based on SPIM Interpreter Update of parser, type checker Simulation algorithm (scheduler) Graphical output Basic geometric computation (affine map application, distance, rotation..)

19. Ongoing Work: Validation We need to validate: 1) Language design (expressivity) 2) Correctness of simulation algorithm 3) Performance 4) Biofilm model Involve interaction with the bio-chemistry team (esp. for 2 and 4) – e.g. actual physical data

20. Ongoing Work: Shapes Enrich the language to target a wider class of systems – Processes are modeled as immaterial points – But physical objects have a shape Add shape information to processes in order to model – Boundaries (material that can't be crossed) – More complex interaction patterns based on the shape and orientation of a molecules Apply our technique to Wireless Communication

21. Demo

22. 09/02/10 Her2 Signaling Pathways