PhysioDesigner, an open platform for multilevel modelingapplicable to computational neuroscience

Y. Asai (P)1, T. Okamoto2, T. Abe1, M. Okita3, Ken-ichi Hagihara2,3, H. Oka2, T. Nomura2,4, H. Kitano1,5

1 Open Biology Unit, Okinawa Institute of Science and Technology2 The Center for Advanced Medical Engineering and Informatics, Osaka University

3 Department of Computer Science, Osaka University4 Graduate School of Engineering Science, Osaka University

5 The Systems Biology InstituteE-mail: yoshiyuki.asai@oist.jp

Abstract— An open platform, PhysioDesigner forenhancing multilevel modeling of physiological func-tions in the field of integrated life-science, which is alsoapplicable to computational neuroscience, has been de-veloped. Users combine and build mathematical mod-els of biological and physiological functions on Phys-ioDesigner. Users can also integrate morphometricdata on a model, which is used, for example, to definea domain on which partial differential equations aresolved. The models developed by PhysioDesigner isstored in insilicoML (ISML) format which is an XML-based specification, to describe wide variety of modelsof biological and physiological functions with hierar-chical structure.

Keywords— PhysioDesigner, Multilevel modeling, In-tegrated life science, Computational neuroscience

Figure 1: PhysioDesigner snapshot. PhysioDesignershows a Hodgkin-Huxely model in nesting diagram(right upper), the same mode but in the tree dia-gram (right lower) and component list tree (left up-per). Square like objects in nesting or tree diagramsrepresent modules in which several physical quantitiesare defined with equations and values.

1 IntroductionMathematical multi-level modeling of biological and

physiological phenomena is crucial for integrating

Edge

(Structural, Logical)

constituteinclude

In-Port

Module

Physical-Quantity

DATA from FILE

dV

dt=1

C(−V − Iext )

Morphological Data

S = 3.2S =

VIext

CS

Out-Port

Edge

(Functional)

inhibitdrive

V

Figure 2: A model is represented as an aggregate ofmodules. Each module is quantitatively characterizedby physical-quantities.

pieces of biological and physiological knowledge[1, 2].The models developed in the relevant scientific fieldare getting larger in size and higher in accuracy. A col-laborative framework should be developed. We havebeen developing an open platform called PhysioDe-signer (Fig. 1), as a successor of insilicoIDE[3] de-veloped by Physiome.jp initiative (www.physiome.jp),on which users can build a new model by combiningexisting models and integrating morphometric data.Simulations of these models can be performed by anaccompanying simulator (insilicoSim) which supportsparallel computing in a MPI-available environment.There is also a database of models, timeseries and mor-phometric data on Physiome.jp website. Anyone canfreely download them. The high reusability of modelsis one of outstanding features of PhysioDesigner.

2 Basic ConceptsPhysioDesigner uses ISML[4] (insilicoML) to store

models internally, which has been developed in theframework of insilico platform[2]. ISML is partiallycompatible with CellML which is one of the pioneer-ing languages developed in IUPS Physiome project[5].ISML is especially designed to structuralize the tar-geting physiological phenomena and to represent theirhierarchical structure based on modularity. A modulerepresents a conceptual physiological entity, such asion channel, cell membrane, neuron, neuronal nucleusand so on. A group of modules can be treated as a

The 21st Annual Conference of the Japanese Neural Network Society (December, 2011)

[P3-39]

Equations for the electric source

Morphometric data

Simulation with FreeFEM++

Figure 3: An example of modeling with a morphome-tric data. In this example, estimated permittivity ofbrain tissue in a 3D cuboid is assigned to a module.EEG is calculated according to a Poisson equation forelectric conduction and equations for electric sources.

module which is at one level above. This definition al-lows to express a situation such as a neuronal nucleusis composed of many neurons. Consequently, a modelis represented as an aggregation of modules.

Each module is characterized by several physical-quantities, that can represent dynamical variables (socalled state) used in differential equations to determinedynamics, time varying parameters, and constants.Usually the physical-quantities are defined by equa-tions such as algebraic equations, ordinary/partial dif-ferential equations, numbers or morphometric data.Programing like expression such as IF-THEN syntaxcan be also defined in physical-quantities.

Relationships between modules are defined by edges.Two types of edges are defined in ISML. One is afunctional edge representing a relationship that onemodule gives values (physical quantities) to the othermodule. The receiver module can utilize the values inequations. The other type is called a structural edge,defining spatial or logical hierarchical relationship.

3 Various modelingPhysioDesigner can deal with models based on ordi-

nary differential equations (ODEs) in a sophisticatedmanner. The methods to handle morphological infor-mation and partial differential equations (PDEs) canspread the targets to be modeled. Models includingPDEs and morphometric data developed on PhysioDe-signer can be exported in FreeFEM++ [6] format andsolved with the finite element method by it (Fig. 3).

Systems biology markup language (SBML) [7] hasbeen developed as a pioneering effort to develop anunified methodology to develop mathematical modelsof biological functions mainly in the subcellular level,such as gene expression/regulation and signal process-ing in cells. As shown in Fig. 4 PhysioDesigner can

SBML modeling for subcellular biochemical reactions

modeling for membrane potential dynamics ISML modeling for cell

network

Figure 4: A schema of SBML-ISML hybrid modeling.

import a SBML model in a module. And modulescan form a network with functional/structural edges torepresent phenomena in the physiological levels suchas cell network, tissue and organs. The hybridizationmodeling between SBML and ISML can be a goodmethodology for multi-level modeling.

There are still open challenges for further develop-ment on other modeling techniques such as multi-agentsystems, though currently the platform can supportthese techniques for limited cases. We have introduceda template/instance framework which can be helpfulto build a large scale model with high-efficiency. Oncewe define a cluster of modules as a template, we cancreate a kind of ”copy”s (instances) as the modules,whose properties follow the one of the template, andcan have some personality.

4 ConclusionWe have been developing an open platform as a com-

prehensive versatile information infrastructure for en-hancing the integrative life science, which can be ap-plicable to computational neuroscience as well. Suchframework can also work to sustain the quality andsoundness of the models.

AcknowledgmentThis work was supported in part by MEXT G-COE

program “in silico medicine” at Osaka University, andGrant-in-Aid for Scientific Research on Innovative Ar-eas at Osaka University and at OIST.

References[1] H. Kitano, “Computational systems biology.” Naure,

vol. 420, pp. 206–10, 2002.

[2] T. Nomura, “Toward integration of biological andphysiological functions at multiple levels,” Frontiersin Systems Physiology, vol. 1, no. 164, 2010.

[3] Y. Suzuki, et al., “A platform for in silico modeling ofphysiological systems iii.” Conf Proc IEEE Eng MedBiol Soc, vol. 2009, pp. 2803–6, 2009.

[4] Y. Asai, et al., “Specifications of insilicoml 1.0: amultilevel biophysical model description language.” JPhysiol Sci, vol. 58, no. 7, pp. 447–58, 12 2008.

[5] http://www.cellml.org/

[6] http://www.freefem.org/ff++/

[7] http://sbml.org/