arXiv subjects

Yohei Kondo

Publications and source records attributed to Yohei Kondo.

7 recordsLinked to original sources

arXiv2025

Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm

Arabidopsis is a widely used model plant to gain basic knowledge on plant physiology and development. Live imaging is an important technique to visualize and quantify elemental processes in plant development. To uncover novel theories underlying plant growth and cell division, accurate cell tracking on live imaging is of utmost importance. The commonly used cell tracking software, TrackMate, adopts tracking-by-detection fashion, which applies Laplacian of Gaussian (LoG) for blob detection, and Linear Assignment Problem (LAP) tracker for tracking. However, they do not perform sufficiently when cells are densely arranged. To alleviate the problems mentioned above, we propose an accurate tracking method based on Genetic algorithm (GA) using knowledge of Arabidopsis root cellular patterns and spatial relationship among volumes. Our method can be described as a coarse-to-fine method, in which we first conducted relatively easy line-level tracking of cell nuclei, then performed complicated nuclear tracking based on known linear arrangement of cell files and their spatial relationship between nuclei. Our method has been evaluated on a long-time live imaging dataset of Arabidopsis root tips, and with minor manual rectification, it accurately tracks nuclei. To the best of our knowledge, this research represents the first successful attempt to address a long-standing problem in the field of time-lapse microscopy in the root meristem by proposing an accurate tracking method for Arabidopsis root nuclei.

cs.CV↗

arXiv2024

Hidden Markov model analysis to fluorescence blinking of fluorescently labeled DNA

We examine quantitatively the transition process from emitting to not-emitting states of fluorescent molecules with a machine learning technique. In a fluorescently labeled DNA, the fluorescence occurs continuously under irradiation, but it often transfers to the not-emitting state corresponding to a charge-separated state. The trajectory of the fluorescence consists of repetitions of light-emitting (ON) and not-emitting (OFF) states, called blinking, and it contains a very large amount of noise due to the several reasons, so in principle, it is difficult to distinguish the ON and OFF states quantitatively. The fluorescence trajectory is a typical stochastic process, and therefore requires advanced time-series data analysis. In the present study, we analyze the fluorescence trajectories using a hidden Markov model, and calculate the probability density of the ON and OFF duration. From the analysis, we found that the ON-duration probability density can be well described by an exponential function, and the OFF-duration probability density can be well described by a log-normal function, which are verified in terms of Kolmogorov-Smirnov test. The time-bin dependence in the fluorescence trajectory on the probability density is carefully analyzed. We also discuss the ON and OFF processes from failure-rate analysis used in life testing of semiconductor devices.

cond-mat.mtrl-sci↗

arXiv2020

Why epithelial cells collectively move against a traveling signal wave

The response of cell populations to external stimuli plays a central role in biological mechanical processes such as epithelial wound healing and developmental morphogenesis. Wave-like propagation of a signal of ERK MAP kinase has been shown to direct collective migration in one direction; however, the mechanism based on continuum mechanics under a traveling wave is not fully understood. To elucidate how the traveling wave of the ERK kinase signal directs collective migration, we constructed the mechanical model of the epithelial cell monolayer by considering the signal-dependent coordination of contractile stress and cellular orientation. The proposed model was studied by using an optogenetically-controlled cell system where we found that local signal activation induces changes in cell density and orientation with the direction of propagation. The net motion of the cell population occurred relative to the wave, and the migration velocity showed a maximum in resonance with the velocity of the ERK signal wave. The presented mechanical model was further validated in \textit{in vitro} wound healing process.

physics.bio-ph↗

arXiv2016

Rescaling of spatio-temporal sensing in eukaryotic chemotaxis

Eukaryotic cells respond to a chemoattractant gradient by forming intracellular gradients of signaling molecules that reflect the extracellular chemical gradient - an ability called directional sensing. Quantitative experiments have revealed two characteristic input-output relations of the system: First, in a static chemoattractant gradient, the shapes of the intracellular gradients of the signaling molecules are determined by the relative steepness, rather than the absolute concentration, of the chemoattractant gradient along the cell body. Second, upon a spatially homogeneous temporal increase in the input stimulus, the intracellular signaling molecules are transiently activated such that the response magnitudes are dependent on fold changes of the stimulus, not on absolute levels. However, the underlying mechanism that endows the system with these response properties remains elusive. Here, by adopting a widely used modeling framework of directional sensing, local excitation and global inhibition (LEGI), we propose a hypothesis that the two rescaling behaviors stem from a single design principle, namely, invariance of the governing equations to a scale transformation of the input level. Analyses of the LEGI-based model reveal that the invariance can be divided into two parts, each of which is responsible for the respective response properties. Our hypothesis leads to an experimentally testable prediction that a system with the invariance detects relative steepness even in dynamic gradient stimuli as well as in static gradients. Furthermore, we show that the relation between the response properties and the scale invariance is general in that it can be implemented by models with different network topologies.

q-bio.CB↗

arXiv2015

Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias

A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regularization often shrinks the estimator for relevant features, which leads to incorrect feature selection. Motivated by the above-mentioned issue, we propose Bayesian masking (BM), a sparse estimation method which imposes no regularization on the weights. The key concept of BM is to introduce binary latent variables that randomly mask features. Estimating the masking rates determines the relevance of the features automatically. We derive a variational Bayesian inference algorithm that maximizes the lower bound of the factorized information criterion (FIC), which is a recently developed asymptotic criterion for evaluating the marginal log-likelihood. In addition, we propose reparametrization to accelerate the convergence of the derived algorithm. Finally, we show that BM outperforms Lasso and automatic relevance determination (ARD) in terms of the sparsity-shrinkage trade-off.

stat.ML↗

arXiv2012

Identifying dynamical systems with bifurcations from noisy partial observation

Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically integrate information in noisy time-series data from partial observations. The method is tested using artificial data generated from two cell-cycle control system models that exhibit different bifurcations, and the learned systems are shown to robustly inherit the bifurcation structure.

q-bio.QM↗

arXiv2011

Growth states of catalytic reaction networks exhibiting energy metabolism

All cells derive nutrition by absorbing some chemical and energy resources from the environment; these resources are used by the cells to reproduce the chemicals within them, which in turn leads to an increase in their volume. In this study, we introduce a protocell model exhibiting catalytic reaction dynamics, energy metabolism, and cell growth. Results of extensive simulations of this model show the existence of four phases with regard to the rates of both the influx of resources and the cell growth. These phases include an active phase with high influx and high growth rates, an inefficient phase with high influx but low growth rates, a quasi-static phase with low influx and low growth rates, and a death phase with negative growth rate. A mean field model well explains the transition among these phases as bifurcations. The statistical distribution of the active phase is characterized by a power law and that of the inefficient phase is characterized by a nearly equilibrium distribution. We also discuss the relevance of the results of this study to distinct states in the existing cells.

q-bio.CB↗