Four-dimensional X-ray phase tomography with Talbot interferometry and white synchrotron radiation: dynamic observation of a living worm.

X-ray Talbot interferometry is attractive as a method for X-ray phase imaging and phase tomography for objects that weakly absorb X-rays. Because X-ray Talbot interferometry has the advantage that X-rays of a broad energy bandwidth can be used, high-speed X-ray phase imaging is possible with white synchrotron radiation. In this paper, we demonstrate time-resolved three-dimensional observation with X-ray Talbot interferometry (namely, four-dimensional X-ray phase tomography). Differential phase images, from which a phase tomogram was reconstructed, were obtained through the Fourier-transform method, unlike the phase-stepping method that requires several (at least three) moiré images to be measured sequentially in order to generate one differential phase image. We demonstrate dynamic observation of a living worm in three dimensions with a time resolution of 0.5 s, visualizing a drastic change in the respiratory tract.

Code generator for distributed parameter biological model simulation with PDE numerical schemes.

The physiological simulation at the tissue and organ level typically involves the handling of partial differential equations (PDEs). Boundary conditions and in cases like pharmacokinetics, distributed parameters add to the complexity of the PDE solution. These factors make most PDE solutions and their corresponding program codes tailored for specific problems. We propose a general approach for handling PDEs in computational models using a replacement scheme for discretization. This method allows for the handling of the different PDE types. The replacement scheme involves substituting all the partial differential terms with the numerical solution equations. Once the model equations are discretized with the numerical solution scheme, instances of the equations are generated to undergo dependency analysis. The result of the dependency analysis is then used to determine the simulation loop structure and generate the program code.

A CellML simulation compiler and code generator using ODE solving schemes.

: Models written in description languages such as CellML are becoming a popular solution to the handling of complex cellular physiological models in biological function simulations. However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving schemes have to be combined with it. Though boundary conditions can be described in CellML, it is difficult to explicitly specify ODE solving schemes using existing tools. In this study, we define an ODE solving scheme description language-based on XML and propose a code generation system for biological function simulations. In the proposed system, biological simulation programs using various ODE solving schemes can be easily generated. We designed a two-stage approach where the system generates the equation set associating the physiological model variable values at a certain time t with values at t + Δt in the first stage. The second stage generates the simulation code for the model. This approach enables the flexible construction of code generation modules that can support complex sets of formulas. We evaluate the relationship between models and their calculation accuracies by simulating complex biological models using various ODE solving schemes. Using the FHN model simulation, results showed good qualitative and quantitative correspondence with the theoretical predictions. Results for the Luo-Rudy 1991 model showed that only first order precision was achieved. In addition, running the generated code in parallel on a GPU made it possible to speed up the calculation time by a factor of 50. The CellML Compiler source code is available for download at http://sourceforge.net/projects/cellmlcompiler.

A program code generator for multiphysics biological simulation using markup languages.

To cope with the complexity of the biological function simulation models, model representation with description language is becoming popular. However, simulation software itself becomes complex in these environment, thus, it is difficult to modify the simulation conditions, target computation resources or calculation methods. In the complex biological function simulation software, there are 1) model equations, 2) boundary conditions and 3) calculation schemes. Use of description model file is useful for first point and partly second point, however, third point is difficult to handle for various calculation schemes which is required for simulation models constructed from two or more elementary models. We introduce a simulation software generation system which use description language based description of coupling calculation scheme together with cell model description file. By using this software, we can easily generate biological simulation code with variety of coupling calculation schemes. To show the efficiency of our system, example of coupling calculation scheme with three elementary models are shown.

A general CellML simulation code generator using ODE solving scheme description.

To cope with the complexity of the biological function simulation models, model representation with description language is becoming popular. However, simulation software itself becomes complex in these environment, thus, it is difficult to modify target computation resources or numerical calculation methods or simulation conditions. Typical biological function simulation software consists of 1) model equation, 2) boundary conditions and 3) ODE solving scheme. Introducing the description model file such as CellML is useful for generalizing the first point and partly second point, however, third point is difficult to handle. We introduce a simulation software generation system which use markup language based description of ODE solving scheme together with cell model description file. By using this software, we can easily generate biological simulation program code with different ODE solving schemes. To show the efficiency of our system, experimental results of several simulation models with different ODE scheme and different computation resources are shown.