Event Abstract

PhysioDesigner for Multilevel Neural System Modeling

  • 1 Okinawa Institute of Science and Technology Graduate University, Japan
  • 2 RIKEN BSI, Neuroinformatics Japan Center (NIJC), Japan
  • 3 Intasect Communicatons Inc, Japan
  • 4 Graduate School of Medicine, Osaka University, Department of Pharmacology, Japan
  • 5 The Systems Biology Institute, Japan

PhysioDesigner [1,2] is an open platform that supports multilevel modeling of physiological systems in the field of integrated life sciences and systems biology, including physiology and neuroscience. Users can combine and build mathematical models of biological and physiological functions on PhysioDesigner. Users can also integrate morphometric data on a model, which is used, for example, to define a domain in which partial differential equations (PDEs) are solved. PhysioDesigner is capable to build a model including PDEs as well as ordinary differential equations.

Physiological systems are modeled based on modules on PhysioDesigner. Hence a model is represented as an aggregate of modules. There are modules called “capsule modules” which involve several other modules (called “functional modules”) so that it is possible to create a kind of sub-package in the model. Users can reuse the encapsulated modules just by copy and paste in other part of the model or in the other model.

One of distinguished features of PhysioDesigner is a capability to create SBML-PHML hybrid models. SBML is a widely prevailing language to describe subcellular phenomena such as gene expressions and protein-protein interactions. PHML is a language natively used in PhysioDesigner to describe models, and was designed to describe a network of functions based on the hierarchical structure of physiological systems. Combining these two languages to describe one single model is a novel method to build a hierarchical model of physiological systems.

Another feature is the template/instance framework, which supports users to create large size models. Encapsulated modules can be defined as a template. Then instances are created according to the template having the completely the same information with the template. However the instances are not merely copies of the template. Once the properties of the template was changed, the changes are immediately applied to all instances. In addition to that, constants and initial values of instance modules can be changed individually, so that each of them can have a personal quality. This would be helpful for example to create a neural network models.

PhysioDesigner has also a capability to process medical imaging data, such as extraction, smoothing, and changing the resolution. By these processing, for example, it is possible to create an 3D object representing the brain conductivity from CT images of grey, white matters, cerebrospinal fluid and skull. This can be applied to simulate EEG (electroencephalogram) [3].

Combining those features of PhysioDesigner, it can be one of the most powerful tools to support multilevel modeling processes in computational neuroscience. PhysioDesigner is available at http://physiodesigner.org.

Figure 1


[1] http://physiodesigner.org
[2] Asai, Y., Abe, T., Okita M., Okuyama, T., Yoshioka, N., Yokoyama, S., Nagaku, M., Hagihara, K., Kitano, H. (2012) Multilevel modeling of Physiological Systems and Simulation Platform: PhysioDesigner, Flint and Flint K3 service. The 12th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT 2012). Conference Proceedings. pp.215-219
[3] Oka, H., Iwasaki, K., Asai, Y., Nomura, T., Yamaguchi, Y. (2012) EEG Analysis in PhysioDesigner. NeuroInformatics 2012. Conference Proceedings p. 128.

Keywords: multilevel modeling, Modeling Environment, physiome, Systems Biology, SBML and PHML

Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.

Presentation Type: Poster

Topic: Computational neuroscience

Citation: Asai Y, Oka H, Li L, Abe T, Kurachi Y and Kitano H (2013). PhysioDesigner for Multilevel Neural System Modeling. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00015

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Received: 05 Apr 2013; Published Online: 11 Jul 2013.

* Correspondence: Dr. Yoshiyuki Asai, Okinawa Institute of Science and Technology Graduate University, Kunigami-gun, Okinawa, 904-0412, Japan, asai@yamaguchi-u.ac.jp