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
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Stochastic, Spatial and Concurrent Biological Processes Modeling Yifei Bao, Eduardo Bonelli, Philippe Bidinger, Justin Sousa, Vishakha Sharma Advisor:
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Stochastic, Spatial and Concurrent Biological Processes Modeling
Joint work with Libera’s lab and Sukhishvili’s lab from Department of CCBBME
Objective
• Construct a language to model and simulate biological processes.
• Apply it for the modeling of a drug delivery nano-system.
Outline
• Motivating example: Bio Film System• Survey for Existing Modeling Techniques • Our Contribution: A Simulation Language • Ongoing and Future Work• Project Demo
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.
Sequential release of bioactive molecules from layer-by-layer films
Bio Film System
increasing pH basic/neutral
3.2 μm
3.2 μm
fast release of capsule cargo
Data from Prof. Sukhishvili’s Lab
Relationship between release of drug molecules and PH with respect to time.
Computational Model• Motivation:
– Wet lab experiments are costly– Some data are difficult to observe (local pH)
• Predict interactions between species Bacteria Drug Molecule
• Successfully used for modeling biological systems– Process = Molecule (with state)– Synchronization = Reaction
•Existing implementation• Simulation and visualization• 4000 lines of ML (Ocaml, F#) code
SPiM Model
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)
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.
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.
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
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
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