XML%20for%20Model%20Specification:%20Introduction%20and%20Workshop

1
XML for Model Specification Introduction and
Workshop
2

• XML for Model Specification
• An Introduction and Workshop
• An Introduction to XML in the Neurosciences
• Sharon Crook, Arizona State University
• An Introduction to NeuroML
• Fred Howell, University of Edinburgh
• NeuroML for Model Specification in ChannelDB and
  GENESIS
• Dave Beeman, University of Colorado, Boulder
• MorphML An XML Application for Neuronal
  Morphology Data
• Sharon Crook, Arizona State University
• Building 3-D Network Models with neuroConstruct
• Padraig Gleeson, University College London
• Discussion Current Issues and Future Development

3
Introduction to XML in the Neurosciences

• What is an eXtensible Markup Language (XML)
  application?
• Portable format for computer documents.
• Data are surrounded by text descriptions called
  tags.
• Due to the self-describing representation,
  programs can parse the data easily.
• Tags are ordinary text and should be clear,
  concise, and make sense to humans.
• Language elements that provide the structure make
  up an XML schema.
• Each language element may also be equivalent to
  an object class.

4
lt!--
Segment mainDend2, ID 2--gt ltsegmentgt lti
dgt2lt/idgt ltproximalgt4lt/proximalgt ltdistalgt
5lt/distalgt ltparentgt0lt/parentgt lt/segmentgt
5

• Additional Potential Benefits of XML Schema
• Validate documents/data.
• Generate instructions for creating database
  tables for data element storage and access.
• Easily generate data structures and code for
  reading and writing valid XML documents.
• Facilitates communication and collaboration!!!

• Potential Liabilities of XML
• May not be clear, concise, easy to read.
• Most of the advantages of XML can be accomplished
  with good discourse, good design, and good
  documentation.
• Extremely verbose so performance will suffer.
• Private data accessed more easily.

6

• Additional Benefits of XML
• Neuroinformatics infrastructure
• (NeuroML Schema and NeuroML Development Kit,
  MorphML, BrainML, CellML, SBML)
• Commercial/free development software available
• Schema development, validation, documentation and
  more
• Altova XMLSpy
• http//www.altova.com
• XML schema to Java object classes and more
• Java Architecture for XML Binding (JAXB)
• http//java.sun.com/xml/jaxb

7

• Relevant XML Applications
• BrainML (http//www.brainml.org)
• Laboratory of Neuroinformatics, Weill Medical
  College of Cornell University
• Examples time series data, spike trains,
  experimental protocols, recording sites,
  bibliographic citations, taxonomy, vital
  statistics of subject, training statistics, some
  attributes of neurons for inheritance
• SBML (http//www.sbml.org)
• Systems Biology Markup Language for modeling
  biochemical reaction networks
• Examples metabolic networks, cell-signaling
  pathways, regulatory networks
• CellML (http//www.cellml.org)
• Bioengineering Institute, University of Auckland
• Examples models of cellular and subcellular
  processes such as calcium dynamics, metabolic
  pathways, signal transduction
• MathML (http//www.w3.org/Math)
• W3C World Wide Web Consortium
• Examples mathematical notation with structure
  and content serve, receive, and process
  mathematics on the web

8
An introduction to NeuroML
Fred Howell Adaptive and neural
computation Informatics University of
Edinburgh fwh_at_inf.ed.ac.uk
9
Overview

• What are the problems?
• Object models, data binding and NeuroML
• The next steps?

10
Scratch pad

• Ideas for new slides.

Collins et al, J Biol Chem 2807 2005
11
Orig version by Ding Fan
12
Models wed like to build
13
Aim

• Move model specifications from programs to a
  declarative XML format.

14
Why XML?

• Language independent way to store complex
  structured information.
• Huge industry momentum.
• Not a programming language so encourages
  declarative specifications.
• Possible to transform from one format to another
  whereas programs have to be recoded by hand

15
Why not XML?

• Cumbersome to edit by hand
• Large text files, need to be compressed
• Harder to parse than ad hoc text formats
• Not suitable for binary data

16
(No Transcript)
17
(No Transcript)
18
How would one publish a model?

• Put XML model spec on your website, links to
  code to run it.
• Plus links back to any experimental data used to
  derive parameters / validate results.
• See Robert Gentleman's campaign for reproducible
  research
• (and also ModelDB)

19
Why is this hard?

• Lots of levels of scale and detail of models
  (from protein interactions to large scale
  networks of neurons)
• Different simulators have different and changing
  capabilities which creates a moving target for
  attempts to build any standards

20
Union or intersection?

• Should a model exchange format restrict itself to
  a standard subset of possible models, or cope
  with any possible model?

21
What is NeuroML?

• A way to map from object trees to XML
• ... with a java development kit
• ... and some suggestions for sample schemas
• channel, cell and network levels
• Emphasis on making it easy to define any object
  model and serialise it
• ... create generic tools which work with any
  object model
• ... and encourage developers to agree on common
  object models where it makes sense

22
Other XML Languages

• SBML a standard for intracellular pathway
  models
• CellML
• MathML

23
Practicalities

• I'm writing a simulator and I'd like to get the
  models into NeuroML what do I do?
• (1) Separate out the declarative aspects of the
  model spec
• (2) Serialise the model into XML, using the
  NeuroML development kit (in Java) or your own
  code
• (3) If any other developers are creating similar
  models, see if you can agree on a common set of
  classes to describe the models by hand

24
The NeuroML development kit

• A data binding kit
• Start with class definitions
• Utilities to read / write model definition as
  readable XML

25
Data binding

• A technique for serialising data in objects as
  XML
• Your program can read in an XML document using
• Object o XMLIn(file.xml)
• And write one using
• Object o new MyComplexStructure()
• XMLOut(o,file.xml)
• The XML tags can correspond to fields in the class

class MyComplexStructure int position
String sequence int pubmedID
ltneuroml classMyComplexStructuregt
ltpositiongt1000lt/positiongt ltsequencegtACGGTTCAGlt/s
equencegt ltpubmedIDgt4321652lt/pubmedIDgt lt/neuromlgt

26
Example network definition
package neuroml.model.network import
neuroml.core. public class Network extends
Element / A network has a set of
elements - can be populations or individual
cells / public Set elements new
Set("ElementRef") / A network also
defines a set of projections between elements
/ public Set projections new
Set("Projection")
27
Example class definition
public class Grid3DStructure extends
PopulationStructure public int xsize1
public int ysize1 public int
zsize1 ... and so on for all the classes /
parameters of your model. Uses a restricted
subset of Java as schema definition
language int, double, String Set, Ref,
List classes and inheritance namespaces
28
Do code modules / embedded scripts have a place
in NeuroML?
Useful for quickly coding loops for running
simulations, ad hoc connectivity... but perhaps
having any code in the model spec defeats the
object?
29
State of play

• simulators adopting own XML formats for
  serialising model descriptions
• common standards working where the domain is
  stable (SBML, MorphML)

30
The next steps?

• How much standardisation is useful?
• Just XML in any format?
• XML with uniform mapping from classes to lttagsgt?
• A set of rigid standards for compartmental
  neurons, channels, receptors, networks, ...?
• What features are needed from a development kit?
• C, python, java?

31
NeuroML for Model Specification in ChannelDB and
GENESIS
Dave Beeman University of Colorado, Boulder
WAM-BAMM05
32
The Problem
One neuronal model --gt Many implementations
EXAMPLE Hodgkin-Huxley K channel
Equations with parameter values describe the
model. Simulator scripts tell the simulator how
to implement it. Differences in simulator
design --gt NEURON and GENESIS scripts look very
different --gt Very difficult to convert a
script to one for a different simulator
The Solution
Establish a standard format for a declarative
representation, NOT a simulator-dependent
procedural representation.
33
Hodgkin-Huxley K Channel Model

• Possible Representations
• Represent the equations in a form that can be
  parsed into Java
• Store tabulated values of rate variables
• Use parameterized form (A BV) / (C D exp((E
  V)/F))

34
The ChannelDB Solution
(http/www.modelersworkspace.org/channeldb/Channel
DB.html)

• XML representation of a Java Hodgkin-Huxley
  object with attributes for Gmax, and a set of
  gates and their exponents
• Gate objects have attribute telling if it
  depends on voltage or concentration, and objects
  for the forward and backward rate parameters
• NeuroML development parser (http//www.neuroml.or
  g) converts between XML representation and Java
  objects
• Use simple Java string manipulation commands to
  produce a simulation script from information in
  the fields of the DBChannel object
• Prototype database and interface creates
  commented GENESIS scripts from stored XML channel
  descriptions

35
NeuroML representation of the Hodgkin-Huxley K
channel
ltneuroml class"DBChannel" description"Hodgkin-Hu
xley squid K channel" author"Dave Beeman"
keywords"Hodgkin-Huxley potassium squid delayed
rectifier" uniqueID"10262778758662F22_at_dogstar
.colorado.edu" notes"An implemention of the
GENESIS K_squid_hh channel" Erest"-0.07V"gt
ltchannelsgt ltchannel name"K_squid_hh"
class"HHChannel" permeantSpecie"K"
Erev"0.09V" Gmax"360.0S/m2" ivlaw"ohmic"gt
ltgatesgt ltgate name"X"
class"HHVGate" timeUnit"sec" voltageUnit"V"
vmin"-0.1" vmax"0.05"
instantCalculation"false"
useState"false" power"4"gt
ltforwardRate class"ParameterizedHHRate"
A"-600.0" B"-10000.0"
C"-1.0" D"1.0" E"0.060" F"-0.01"/gt
ltbackwardRate class"ParameterizedHHRate"
A"125.0" B"0.0 C"0.0"
D"1.0" E"0.07" F"-0.08"/gt lt/gategt
lt/gatesgt ltlog author"Dave
Beeman" date"Jul 9, 2002 111115 PM"
literatureReference"A.L. Hodgkin and A.F.
Huxley, J. Physiol. (Lond) 117,
pp 500-544 (1952)"gt ltlogEntriesgt
lt/logEntriesgt lt/loggt
lt/channelgt lt/channelsgt lt/neuromlgt
36
Some classes defined for ChannelDB DBChannel
Wrapper class that is used to contain any channel
model that is stored in ChannelDB, along with
some descriptive information. HHChannel Class
used for all the Hodgkin-Huxley type channels in
the database. HHVGate Used as a member of the
gates set of a HHChannel. It contains forward and
backward rate objects that depend on voltage, as
well as some additional fields to describe the
gate. HHCGate An ionic concentration-dependent
gate, analogous to the voltage-dependent HHVGate.
It provides an additional field for a reference
to the object that provides the source of the
ionic concentration. HHRate The superclass for
the specialized forms for the rate variables.
ParameterizedHHRate A subclass of HHRate that
expresses rate variables in a parameterized form
typical of many Hodgkin-Huxley type rate
equations, "rate (A BV) / (C D exp((E
V)/F))" EquationHHRate A subclass of HHRate
that expresses the rate variables as equations.
TabulatedHHRate A subclass of HHRate that
allows a gate's forwardRate or backwardRate to be
specified by a table at equally spaced voltage
(or concentration) points. ConcenPool Describes
a single shell model for a concentration pool,
with a buildup of concentration proportional to
an incoming current and a time constant for
decay. The object providing the source of
concentration to a HHCGate is typically formed
from this class. The source of currents is
provided by a set of objects of class
CurrentSource. CurrentSource Used by ionic
concentration pools to provide information about
the object that provides an ionic current.
37
Unfinished Business and Open Questions Extend
NeuroML to provide representations for more
detailed multi-shell models of calcium
diffusion Implement a more sophisticated
representation of literature references than the
simple string that is currently used in the
NeuroML software. (We have proposed a schema for
the Modeler's Workspace based on
BibTeX.) Software to convert ChannelDB
descriptions to NEURON and other
simulators Implement the HHCVGate, a
two-dimensional gate depending on both voltage
and concentration. (Note that the Traub
Ca-dependent K channel model uses a form that can
be expressed as a product of a HHVGate and a
HHCGate.) Implement Borg-Graham or
Lytton-Sejnowski temperature-dependent channel
models with the NeuroML ThermodynamicHHVGate. Is
there a better way for a concentration-dependent
channel model to reference the models that
provide the source of ionic currents and
concentrations? How much standardization should
there be for the format and the names of the
independent variables and parameters in equation
representations?
38
Unbundling GENESIS
39
GENESIS 3 Core Based on MOOSE
The Messaging Object Oriented Simulation
Environment a reimplementation of GENESIS base
code in C by U. S. Bhalla, NCBS, Bangalore
Provides

• Improved Messaging between GENESIS objects
• Faster, smaller, cleaner implementation
• Portable to MS Windows and non-UNIX platforms
• Improved equation solvers
• Allows multiple parsers and interfaces

GENESIS 3 will add

• Graphical interface
• XML representation of models
• Backwards compatibility with GENESIS 2
• Tutorials and educational applications

40
Planned GENESIS 3 Interfaces
41
An XML Application for Neuronal Morphology
Data http//www.morphml.org
Sharon Crook Arizona State University Department
of Mathematics and Statistics School of Life
Sciences
42
MorphML XMLSpy Documentation
43
MorphML XMLSpy Documentation
44
MorphML A Simple Example from neuroConstruct
lt?xml version"1.0" encoding"UTF-8"?gt ltnmorphml
xmlnsxsi"http//www.w3.org/2001/XMLSchema-instan
ce" xmlnsn"http//morphml.org/morphml/schema/1.0
.0" xsischemaLocation"http//morphml.org/morphml
/schema/1.0.0 http//math.la.asu.edu/crook/morphm
l/MorphML.xsd"gt ltnnamegtSimpleCelllt/nnamegt ltnn
otesgtA Simple cell for testing purposeslt/nnotesgt
ltnlengthUnitsgtMicrometerslt/nlengthUnitsgt lt!--C
onverting cell SimpleCell--gt ltnpointsgt lt!--
Start point of segment Soma, ID
0--gt ltnpointgt ltnidgt0lt/nidgt ltnxgt0.0lt/n
xgt ltnygt0.0lt/nygt ltnzgt0.0lt/nzgt ltndiame
tergt16lt/ndiametergt lt/npointgt lt!-- End
point of segment Soma, ID 0--gt ltnpointgt ltn
idgt1lt/nidgt ltnxgt0.0lt/nxgt ltnygt0.0lt/nygt
ltnzgt0lt/nzgt ltndiametergt16lt/ndiametergt lt/
npointgt lt!-- Start point of segment
mainDend1, ID 1--gt ltnpointgt ltnidgt2lt/nidgt
ltnxgt0.0lt/nxgt ltnygt0.0lt/nygt ltnzgt0.0lt/n
zgt ltndiametergt2lt/ndiametergt lt/npointgt
45
MorphML A Simple Example from neuroConstruct
lt!-- End point of segment mainDend1, ID
1--gt ltnpointgt ltnidgt3lt/nidgt ltnxgt-10.0lt/
nxgt ltnygt-30.0lt/nygt ltnzgt0lt/nzgt ltndia
metergt2lt/ndiametergt lt/npointgt lt!-- Start
point of segment mainDend2, ID
2--gt ltnpointgt ltnidgt4lt/nidgt ltnxgt0.0lt/n
xgt ltnygt0.0lt/nygt ltnzgt0.0lt/nzgt ltndiame
tergt2lt/ndiametergt lt/npointgt lt!-- End
point of segment mainDend2, ID
2--gt ltnpointgt ltnidgt5lt/nidgt ltnxgt10.0lt/n
xgt ltnygt-30.0lt/nygt ltnzgt0lt/nzgt ltndiam
etergt2lt/ndiametergt lt/npointgt lt!-- Start
point of segment mainAxon, ID
3--gt ltnpointgt ltnidgt6lt/nidgt ltnxgt0.0lt/n
xgt ltnygt0.0lt/nygt ltnzgt0.0lt/nzgt ltndiame
tergt2lt/ndiametergt lt/npointgt
46
MorphML A Simple Example from neuroConstruct
ltncellsgt ltncellgt ltnnamegtSimpleCelllt/nna
megt lt!-- Segments of the cell
--gt ltnsegmentsgt lt!-- Segment Soma, ID
0--gt ltnsegmentgt ltnidgt0lt/nidgt ltnp
roximalgt0lt/nproximalgt ltndistalgt0lt/ndistalgt
lt/nsegmentgt lt!-- Segment mainDend1,
ID 1--gt ltnsegmentgt ltnidgt1lt/nidgt
ltnproximalgt2lt/nproximalgt ltndistalgt3lt/ndis
talgt ltnparentgt0lt/nparentgt lt/nsegmentgt
lt!-- Segment mainDend2, ID
2--gt ltnsegmentgt ltnidgt2lt/nidgt ltnp
roximalgt4lt/nproximalgt ltndistalgt5lt/ndistalgt
ltnparentgt0lt/nparentgt lt/nsegmentgt
lt!-- Segment mainAxon, ID 3--gt ltnsegmentgt
ltnidgt3lt/nidgt ltnproximalgt6lt/nproxima
lgt ltndistalgt7lt/ndistalgt ltnparentgt0lt/n
parentgt lt/nsegmentgt
47
MorphML A Simple Example from neuroConstruct

lt!--
Segment subAxon1, ID 4--gt ltnsegmentgt lt
nidgt4lt/nidgt ltnproximalgt8lt/nproximalgt
ltndistalgt9lt/ndistalgt ltnparentgt3lt/nparent
gt lt/nsegmentgt lt!-- Segment subAxon2,
ID 5--gt ltnsegmentgt ltnidgt5lt/nidgt
ltnproximalgt10lt/nproximalgt ltndistalgt11lt/nd
istalgt ltnparentgt3lt/nparentgt lt/nsegment
gt lt/nsegmentsgt lt/ncellgt lt/ncellsgt lt/nmor
phmlgt
48
Virtual Ratbrain (http//www.ratbrain.org)
Laszlo Zaborszky, Peter Varsanyi Center for
Molecular and Behavioral Neuroscience,
Rutgers Fred Howell, Nicola McDonnell Institute
of Adaptive and Neural Computation, University of
Edinburgh

• Database for peer reviewed 3-D cellular
  anatomical data of the rat brain
• Visualization and analysis tools including
  analysis of dendritic and axonal morphometry
• Data stored in MorphML format

49
Virtual Ratbrain (http//www.ratbrain.org)
MorphML Viewer
50
Building 3D Network Models with
neuroConstruct(Summary of main presentation)

• Padraig Gleeson
• University College London
• p.gleeson_at_ucl.ac.uk
• WAM-BAMM05
• 31 March 2005

51
Scope of Application

• Reuses existing base of models/modellers
• Adds functionality
• Graphical interface
• Checks on morphologies
• Network building capabilities
• Storage/replay/analysis of simulations
• Built with Java runs on any platform
• Code produced is native GENESIS/NEURON

52
Visualization

• Single Cells can be viewed in 3D
• Information on morphology
• Checks on consistency of cell structure
• Segments can be edited
• Info on basic electrophysiology

53
Screenshot Cell Visualization
54
Packing in 3D

• Cell Groups are packed in 3D Regions
• Rectangular Box
• Spherical
• Various Packing Patterns
• Random
• Cubic close packed
• Hexagonal
• Single positioned
• Evenly spaced in 1D

55
Screenshot Packing in 3D
56
Simulator Interaction(1)

• Morphology files can be imported from
• GENESIS (.p readcell format files)
• NEURON (most ntscable like files, with create,
  pt3dadd, connect)
• Cvapp (SWC format files)
• MorphML
• Imported cells are checked for validity i.e.
  errors which may cause problems on some platforms
• zero length segments
• all except root segment have parents
• unique names, etc.

57
Simulator Interaction(2)

• Files can currently be exported to
• NEURON, for simulation
• GENESIS, for simulation
• MorphML, for publishing/use by other simulators
• Cell info held in simulator independent format
• Can be mapped to other/future simulators

58
Cell Processes

• Generic models of Cell Process (channels/synapses)
  can be used in neuroConstruct
• Model separated from experimentally measured
  parameters
• Reuse of tried and tested template files
• Can be mapped on to any simulator
• Automatic handling of units

59
Modularity of Cell Processes (1)
Pre-existing well tested
XML template of model of Cell Process, e.g.
Double Exp Synapse, HH Channel
Experimentally determined parameter set gmax,
Tau Rise/Decay, etc.
Published model of Cell Process (XML file)
Mapping of templates to existing simulators
60
Modularity of Cell Processes (2)
neuroConstruct
Parameters coupled with GENESIS/ NEURON template
placed on cells
GENESIS/NEURON source code
Published model of Cell Process
Parameters with plotting module
Plots of Cell Process internals
61
Screenshot Cell Processes
62
Morphology mapping (1)

• neuroConstruct Concepts
• Section unbranched part of axon/dendrite with
  the same biophysical properties
• Segment Specifies one 3D point along Section,
  shaped like conical frustum
• Section specifies start point, Segments specify
  3D points along

63
Morphology mapping (2)

• Going from GENESIS -gt NEURON
• Reasonably straightforward
• Compartments in GENESIS mapped to segments in
  neuroConstruct
• Going from NEURON -gt GENESIS
• Non-trivial mapping conical sections to
  cylinders
• Simple mapping each segment to compartment with
  equivalent surface area

64
NeuroML/MorphML interaction

• neuroConstruct currently allows
• Import export of MorphML morphologies
• Future support
• Greater support for specification of groups/Cell
  Process locations in MorphML format
• Importation of Cell Processes in NeuroML format
• Export of network structure in NeuroML format
• Generation of simulation code in NeuroML/NeoSim
  format

65
XML for Model Specification Introduction and
Workshop
66
XML for Model Specification Discussion

• Wider/Easier Access to NeuroML
• Would a CVS server for materials on the website
  that allows for multiple contributors/editors be
  of use?
• Would a separation of the NeuroML schema into
  several, more focused schemas (ex morphology,
  channels, channel distributions, connectivity) be
  useful for making it more transparent and easier
  to use? If so, where do we define relationships
  among the language elements of each separate
  schema?
• An XMLSpy HTML version of the specs might help
  with documentation.
• Tight coupling of NeuroML with Java? What about
  other languages?

67
XML for Model Specification Discussion
Channel Issues 1. Can we use one XML channel
specification for both NEURON and GENESIS
specs? 2. How can we make XML channel documents
easy to use for someone who is not a GENESIS or
NEURON user? 3. Details of channel distributions
in current NeuroML Schema, GENESIS, and NEURON?
Can we define a schema that includes them all?
68
XML for Model Specification Discussion

• Morphology Issues
• Mapping morphology across formats (MorphML,
  GENESIS, NEURON, BBT, SWC).
• One issue is that GENESIS uses cylindrical
  compartments while others can be frustums. You
  can map between compartments of equal surface
  area which accounts for the membrane parameters
  but not the axial resistance.
• In Neuron, the 3-D points describing a
  cable/section and the actual numerical
  integration points (nseg points evenly spaced)
  are different.
• Segment of length zero for cell body?
• MorphML-gtNEURON-gtMorphML connectivity?

69
XML for Model Specification Discussion

• Future Development
• 1. Relation to other XML applications? BrainML,
  CellML and SBML?
• 2. Some of the inbuilt XML features in Java can
  be useful and further schema development should
  take advantage of these (ex XML support in Java
  J2SE 5.0).
• 3. What concrete plans do different groups have
  over the next year for XML-related developments?

70
The End