A computational framework for bioimaging simulation

Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units.Comment: 57 page

ハクサンコザクラの保全生物学: 遺伝的変異と集団の遺伝的分化

[論文] Articl

Simple models (A) 100 stationary HaloTag-TMR molecules are distributed on a glass surface. (B) 19,656 HaloTag-TMR molecules are distributed in a 30 × 30 × 6 <i>μ</i>m³ box of aqueous solution (= 5 nM), and rapidly diffuse at 100 <i>μ</i>m²/sec.

<p>Simple models (A) 100 stationary HaloTag-TMR molecules are distributed on a glass surface. (B) 19,656 HaloTag-TMR molecules are distributed in a 30 × 30 × 6 <i>μ</i>m³ box of aqueous solution (= 5 nM), and rapidly diffuse at 100 <i>μ</i>m²/sec.</p

Schematic overview of the computational framework.

<p>Direction of photon propagation is presented by thick blue arrows.</p

Optical configurations.

<p>(A) TIRFM simulation module. (B) LSCM simulation modules. Grey arrows represent direction of photon propagation.</p

Self-organizing wave model of PTEN for the chemotactic pathway of <i>D. discoideum</i>.

<p>(A) Reaction network. (B) Geometry of <i>D. discoiduem</i> cell model. A hemispherical cell measuring 25 <i>μ</i>m in diameter and 5 <i>μ</i>m in height is assumed. (C) Time-lapse image of the self-organizing wave model observed using the LSCM simulation module. Size of each images is 52 × 51 pixel. Orange scalebar represents 5.39 <i>μ</i>m. (D) Time-lapse images obtained from the experiment. Red and green indicate PTEN-TMR and PH-EGFP, respectively. The colorscale of each images is adjusted in the range of 0 to 225.</p

Using HaloTag-TMR molecules distributed on a glass surface to evaluate the performance of TIRFM simulation module.

<p>(A) Expected images of the simple particle model at various beam flux densities (20,30,40 and 50 W/cm²). The expected images are obtained by averaging 100 images over 3 sec exposure period. Intensity histograms are also shown below each expected images and presented with black-colored bars. Each histograms are logarithmically scaled and presented with grey-colored bars. (B) Simulated digital images of the simple particle model are shown at various beam flux densities (20,30,40 and 50 W/cm²). Size of each images is 152 × 156 pixel. Orange scalebar represents 3.15 <i>μ</i>m. Intensity histograms are also shown below each simulated images. (C) Real captured images obtained from <i>in vitro</i> experiment are shown at various beam flux densities (20,30,40 and 50 W/cm²). The maximum value of the grayscale is adjusted to improve visualization of each image. Intensity histograms are also shown below each actual images.</p