brief review of common modeling formalisms and representation approaches

Brief review of common modeling formalismsand representation approaches

Michael Hucka, Ph.D. – California Institute of Technology, Pasadena, CA James Sluka, Ph.D. – Indiana University, Bloomington, IN

Herbert Sauro, Ph.D. – University of Washington, Seattle, WA

2015 MSM Consortium Satellite Meeting, September 2015, Bethesda, MD, USA

Trajectory of this presentation

Phenomena Formalisms Formats

What is the model about?

What is the form of the model?

How is the model stored and shared?

Phenomena

Dimensions of biological phenomena

ecosystempopulation

bodyorgan system

organtissue

cellsubcellular structures

moleculeatom

Spatial scale

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Temporal scale

yeardayhourminutesecond monthmillisecondpicosecond

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Modeling formalisms

Some common, basic formalisms

ODE (Nonlinear) ordinary differential equations expressing rates of change of variables w.r.t. a single continuous variable

PDE Partial differential equations expressing rates of change w.r.t. multiple variables (e.g., space & time)

Discrete stochastic

Monte Carlo methods (e.g., Gillespie’ SSA) used to produce time evolution of a system of individuals or particles

Constraint-based

Optimization of variables around a steady state, subject to constraints (e.g., mass balance)

Agent-based

Simulation of the operation and interaction of multiple (semi-)independent entities

Finite element

Solution methods for PDE problems that subdivide the domain into a mesh of smaller/simpler pieces

Dimensions of formal models

DiscreteContinuous

Not spatial Explicitly spatial

Deterministic Stochastic

What is the form of a given variable or quantity or attribute?

Some example variations:

• Entity values:

- State value (e.g., Boolean on/off )

- Discrete molecular count

- Continuous concentration

• Time:

- System iterations

- Discrete time steps

- Continuous time

Discrete vs. continuous

Do you always get the same result given the same initial conditions?


• Ordinary differential equations (⇒ deterministic)

• Hybrid deterministic and stochastic (⇒ both)

• Fully stochastic system

Deterministic vs. stochastic

Are spatial characteristics inherently accounted for?


• “Pure” biochemical reaction network model

• Compartmental model

• Spatial diffusion model

Not spatial vs. explicitly spatial

How some common formalisms compare

Finite element

Agent-based

continuous discretedeterministic stochastic

nonspatial spatialODE


nonspatial spatialPDE


nonspatial spatial

Discrete stochastic

Constraint-based


nonspatial spatial


nonspatial spatial


nonspatial spatial

Encoding the models

Structured format

• Software-independent

• SBML, CellML, NeuroML

• Structured format for some parts

• Hard coding for other parts

Spectrum of approaches to encoding models

“Hard-coded”

• The code is the model

• Python, MATLAB, etc.

Mixture

format interpreter

simulation software system

software’s internal format

What do we mean by a structured format?

definition in software-independent file format

Gen

eral

cas

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format interpreter





Gen

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cas

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se format interpreter



declarative model definition

declarative simulation protocol definition

Pros:

• Most flexibility and power for defining model & simulation

Cons:

• Model details intertwined with implementation details

• Others must read code to understand model

➡ Readers must have access to same environment (⇒ $$$)

➡ Readers must know language & environment

• Model reuse can be much more difficult

• Model annotation can be much more difficult

• Model comparison can be much more difficult

Pros and cons of hard-coded models

Pros

• Software-independent ⇒ model usable in any compatible tool

• Model details are made explicit ⇒ reproducibility enhanced

- The knowledge represented by the model is clarified

• Implementation details not mixed in ⇒ less error prone

• Tools & facilities can be devised for annotation, comparison, search

Cons:

• Suitable formats not available for all model formalisms

• Encoding model in a given format may not be easy

- Formats are often an intersection of commonly needed features, not a union of all possible features ⇒ limited in their features

Pros and cons of structured definition formats

Results of short MSM survey: approaches

“If you/your team write simulations, how do you usually encode or represent your models?”

Express models in a spreadsheetHard-coded in programming language

Encoded using an open, structured formatUse application with its own internal format

Mix of approachesOther

0 5 10 15

210

53

110

Survey run in August, 2015. Received 32 total responses.

However, number of responses listing model representation formats in answer to the question “If you use open formats, please list the relevant standards you use” = 14

Results of short MSM survey:

formats

“If you use open formats to represent and store your models, please list the relevant standards that you use.”

SBMLCellML

FieldMLSED-ML

FEMMATLABMIRIAM

VCMLAMPL

BioPAXBioSignalML

BNGLFortran

GAMSHDF5

GoTranGML

JSONMoML

NeuroMLOpenSim

PythonRDF

SymPyVTK

0 5 10

Results of short MSM survey:

formats

“If you use open formats to represent and store your models, please list the relevant standards that you use.”

SBMLCellML

FieldMLSED-ML

FEMMATLABMIRIAM

VCMLAMPL

BioPAXBioSignalML

BNGLFortran

GAMSHDF5

GoTranGML

JSONMoML

NeuroMLOpenSim

PythonRDF

SymPyVTK

0 5 10

not a model formatapplication-specific

prog. language or API

Open formats named in the surveySBML Declarative, process-oriented (e.g., reactions) descriptions. SBML

Level 3 packages support added constructs & application areas.

CellML Declarative, component-oriented descriptions of mathematical models of any kind.

FieldML Declarative descriptions of hierarchically-structured generalized mathematical fields.

SED-ML Declarative descriptions of simulation procedures to be applied to models in SBML, CellML, NeuroML or other format.

BNGL Rule-based descriptions of biomolecular interactions. Originally BioNetGen’s format but now used by some other tools.

NeuroML Declarative descriptions of neuronal cell and network models.

MoML Descriptions of hierarchical components of any kind. Defines connections, ports, and meta-data.

RDF General data representation format using directed, labeled graphs.

JSON General data rep. format using ordered lists of name-value pairs.

format interpreter





Alte

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case

Gen

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declarative model definition

declarative simulation protocol definition

SBML, CellML, NeuroML, FieldML

SED-ML

Formalisms vs. formats

ODE SBML, CellML, NeuroML, VCell “.vcml”, COPASI “.cps”, OpenSim “.osim”, JSIM “.mml”

PDE SBML Level 3, FieldML, VCell “.vcml”, JSIM “.mml”

Discrete stochastic

Molecular level: CHARMMS “.crd” & “.psf”, LAMMPS files Higher levels: SBML, BNGL, COPASI “.cps”, JSIM “.mml”, MIST zip

Constraint-based

SBML Level 3, AMPL “.mps”, GAMS “.gdx”

Agent-based

(application-specific formats, or programming languages + frameworks/APIs)

Finite element

SBML Level 3, FieldML, CMISS “.exelem”, CompuCell3D “.cc3d”, Tecplot “.tp”, FlexPDE “.pde”, COMSOL “.mph”, FEBio “.feb”, VCell “.vcml”, JSIM “.mml”

Why bother going down this road?

Responsible conduct of scientific research!

Promotes greater reproducibility

• Models tested in multiple software tools reveal hidden assumptions

Gives you access to a larger ecosystems of tools

• Databases (e.g., BioModels Database, Physiome Repository)

- Helps disseminate models (good for citations)

• Other compatible software written by other people: simulation, analysis, visualization, validation, comparison, annotation, …

• Automated model generation pipelines (e.g., Path2Models)

Ensures persistence of models after individual software tools disappear

Incentives for sharing models in open formats

brief review of common modeling formalisms and representation approaches

Science