Biology subjects

Kamimura, A.

Publications and source records attributed to Kamimura, A..

3 recordsLinked to original sources

bioRxiv2024

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density

Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.

cell biology↗

bioRxiv2021

Representation and inference of size control laws by neural network aided point processes

The regulation and coordination of cell growth and division is a long-standing problem in cell physiology. Recent single-cell measurements using microfluidic devices provide quantitative time-series data of various physiological parameters of cells. To clarify the regulatory laws and associated relevant parameters such as cell size, mathematical models have been constructed based on physical insights over the phenomena and tested by their capabilities to reproduce the measured data. However, such a conventional model construction by abduction faces a constant risk that we may overlook important parameters and factors especially when complicated time series data is concerned. In addition, comparing a model and data for validation is not trivial when we work on noisy multi-dimensional data. Using cell size control as an example, we demonstrate that this problem can be addressed by employing a neural network (NN) method, originally developed for history-dependent temporal point processes. The NN can effectively segregate history-dependent deterministic factors and unexplainable noise from a given data by flexibly representing functional forms of the deterministic relation and noise distribution. With this method, we represent and infer birth and division cell size distributions of bacteria and fission yeast. The known size control mechanisms such as adder model are revealed as the conditional dependence of the size distributions on history and their Markovian properties are shown sufficient. In addition, the inferred NN model provides a better data representation for the abductive model searching than descriptive statistics. Thus, the NN method can work as a powerful tool to process the noisy data for uncovering hidden dynamic laws.

systems biology↗

bioRxiv2019

Molecular Diversity and Network Complexity in Growing Protocells

A great variety of molecular components is encapsulated in cells. Each of these components is replicated for cell reproduction. To address an essential role of the huge diversity of cellular components, we study a model of protocells that convert resources into catalysts with the aid of a catalytic reaction network. As the resources are limited, it is shown that diversity in intracellular components is increased to allow the use of diverse resources for cellular growth. Scaling relation is demonstrated between resource abundances and molecular diversity. We then study how the molecule species diversify and complex catalytic reaction networks develop through the evolutionary course. It is shown that molecule species first appear, at some generations, as parasitic ones that do not contribute to replication of other molecules. Later, the species turn to be host species that support the replication of other species. With this successive increase of host species, a complex joint network evolves. The present study sheds new light on the origin of molecular diversity and complex reaction network at the primitive stage of a cell.

biophysics↗