Biology subjects

Okano, Y.

Publications and source records attributed to Okano, Y..

5 recordsLinked to original sources

bioRxiv2026

CFD-based Bayesian Optimization of Stirring Strategies in Stirred Tank Cultures of Pluripotent Stem Cell Spheroids

The suspension culture of pluripotent stem (PS) cells in stirred bioreactors poses a delicate balance between maintaining homogeneous cell dispersion and avoiding excessive shear stress that can compromise cell viability and pluripotency. In this study, we used computational fluid dynamics (CFD) coupled with a discrete particle method (DPM) to simulate iPS cell behavior in a 5 mL delta-impeller stirred tank. Our analysis revealed that upward flow at the tank bottom and downward flow at the top are critical for maintaining a stable suspension. To optimize the stirring protocol, we applied Bayesian optimization to identify a time-dependent stirring schedule that begins with a high-speed phase for resuspension, followed by a low-speed phase for sustained suspension with minimal hydrodynamic stress. The optimized schedule demonstrated improved floating rate and reduced slip velocity, indicating lower mechanical stress on cells. These findings provide engineering insights into scalable bioreactor operation, contributing to the design of robust iPS cell manufacturing systems. HighlightsO_LICFD-DPM simulations predicted iPS spheroid motion in a stirred tank. C_LIO_LIBayesian optimization identified a two-step agitation strategy. C_LIO_LIHigh initial agitation promoted rapid particle resuspension. C_LIO_LILow subsequent agitation maintained suspension with lower downflow. C_LIO_LIParticle Reynolds number correlated with the floating rate. C_LI

bioengineering↗

bioRxiv2026

BasalCell: A project scaffold generator for bioinformatics analysis

In the current bioinformatics landscape, where R-centric and Python-centric ecosystems coexist and overlap, there is an increasing demand for the organic integration of these disparate development environments. As bioinformatics practices become ubiquitous, it is crucial to lower the technical barriers for biologists to adopt software engineering standards--such as version control, environment reproducibility, code readability, continuous integration/continuous deployment (CI/CD), and comprehensive documentation--which often present significant implementation hurdles for biologists with limited programming experience. To address these challenges, we developed BasalCell (https://github.com/yo-aka-gene/BasalCell), a project scaffolding system designed to provide a standardized, easily reproducible template that integrates these essential features by default--enabling researchers to seamlessly manage multi-language environments and automate rigorous development workflows, ultimately fostering greater transparency and reliability in biological data science.

bioinformatics↗

bioRxiv2025

Design-of-Experiments for Nonlinear, Multivariate Biology: Rethinking Experimental Design through Perturb-seq

Design of experiments (DOE) principles are increasingly applied to biological assays, yet it remains unclear whether the optimality of their foundational assumption--orthogonal decomposition--holds in nonlinear biological systems. We addressed this question using Perturb-seq as a case study. By benchmarking a design commonly used in Perturb-seq and related experiments against the orthogonal Plackett-Burman (PB) design via simulations, we uncovered a counter-intuitive phenomenon: while orthogonal designs generally excel, the correlated structure inherent to the common design is functionally robust in systems with significant signal amplification. This challenges the blind application of DOE to biology. Based on these findings, we developed the PB suitability index (PBSI), a simple, parameter-free metric that predicts the optimal design solely from network structure. Our work not only provides practical guidelines for Perturb-seq but also establishes a "biology-oriented DOE" framework, bridging the gap between statistical rigor and biological complexity.

bioinformatics↗

bioRxiv2025

Graph topology reframes the coherence of cell-state manifold inference under heterogeneous single-cell observations

Manifold-based single-cell omics analyses assume that high-dimensional observations can fall into a low-dimensional space encoding biological constraints. In practice, per-cell observation is highly heterogeneous: shallowly- and deeply-observed cells coexist. In an empirical scRNA-seq dataset, shallowly-observed cells cluster together to generate spurious hubs that can give rise to illusory loops in low-dimensional manifold skeletons in graph abstraction. Several imputation methods leave these artifacts largely intact, whereas graph abstraction restricted to homogeneously-observed cells alone recovers tree-like structures locally representing constrained cell state transitions. Simulations further demonstrate that realistic heterogeneous observation can create spurious subclusters and false branching. Grounded by these observations, we propose topological stability descriptors of low-dimensional manifold skeletons to delineate a regime in which manifold-based inference is trustworthy despite realistic heterogeneous observations. Our findings underscore that the heterogeneity of observations is not merely noise but a source of systemic distortion in manifold-based inference that must be addressed.

bioinformatics↗

bioRxiv2024

A graph-based practice of evaluating collective identities of cell clusters

The rise of single-cell RNA-sequencing (scRNA-seq) and evolved computational algorithms have significantly advanced biomedical science by revealing and visualizing the multifaceted and diverse nature of single cells. These technical advancements have also highlighted the pivotal role of cell clusters as representations of biologically universal entities such as cell types and cell states. However, to some extent, these clusterings remain dataset-specific and method-dependent. To improve comparability across different datasets or compositions, we previously introduced a graph-based representation of cell collections that captures the statistical dependencies of their characteristic genes. While our earlier work focused on theoretical insights, it was not sufficiently adapted and fine-tuned for practical implementation. To address this, the present paper introduces an improved practice to define and evaluate cellular identities based on our theory. First, we provide a concise summary of our previous theory and workflow. Then, point-by-point, we highlight the issues that needed fixing and propose solutions. The frameworks utility was enhanced by leveraging alternative formats of cellular features such as gene ontology (GO) terms and effectively handling dropouts. Supplemental techniques are offered to reinforce the versatility and robustness of our method.

bioinformatics↗