Biology subjects

Kunida, K.

Publications and source records attributed to Kunida, K..

9 recordsLinked to original sources

bioRxiv2026

Expanding gene regulatory networks from transcriptome data through graphical modeling with heterogeneous priors

Gene regulatory network inference is widely used to reconstruct large-scale networks and identify functional genes from transcriptome data. Meanwhile, in many biological fields, core regulatory genes have been extensively studied, leading to the establishment of small-scale gene regulatory networks, and novel genes connected to these networks remain to be identified. However, methods for expanding existing gene networks by identifying novel regulatory interactions, rather than reconstructing the entire network, are not well established. Here, we propose a method for gene network expansion that incorporates known regulatory relationships and evaluates each candidate gene individually to infer its regulatory connections to the existing network. Using simulated datasets from the DREAM4 benchmark and the PRECISE-1K experimental dataset, our method outperformed conventional methods by incorporating prior knowledge. In particular, it improved the ability to distinguish true regulatory interactions from indirect associations arising from strong correlations among genes in the existing network. The method also showed strong performance for interactions involving genes with high outdegree or centrality. Furthermore, it maintained stable performance as the size of the existing network increased and was robust to noise in prior information. These results demonstrate that our method provides an effective framework for expanding existing gene regulatory networks by leveraging prior knowledge.

bioinformatics↗

bioRxiv2024

Stepwise Bayesian Machine Learning Uncovers a Novel Gene Regulatory Network Component in Neural Tube Development

Recent advancements in machine learning-based data processing techniques have facilitated the inference of gene regulatory interactions and the identification of key genes from multidimensional gene expression data. In this study, we applied a stepwise Bayesian framework to uncover a novel regulatory component involved in differentiation of specific neural and neuronal cells. We treated naive neural precursor cells with Sonic Hedgehog (Shh) at various concentrations and time points, generating comprehensive whole-genome sequencing data that captured dynamic gene expression profiles during differentiation. The genes were categorized into 224 subsets based on their expression profiles, and the relationships between these subsets were extrapolated. To accurately predict gene regulation among subsets, known networks were used as a core model and subsets to be added were tested stepwise. This approach led to the identification of a novel component involved in neural tube patterning within gene regulatory networks (GRNs), which was experimentally validated. Our study highlights the effectiveness of in silico modeling for extrapolating GRNs during neural development.

systems biology↗

bioRxiv2024

Designing a hybrid in silico/in-cell controller robust to process-model mismatch associated with dynamically regulated enzyme overexpression

Discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a serious challenge in bioprocess optimization. Previously, we proposed the hybrid in silico/in-cell controller (HISICC) concept combining model-based optimization with cell-based feedback to address the PMM problem. Herein, this approach was advanced to regulate intracellular concentrations of rate-limiting enzymes. Advanced HISICC was demonstrated using an engineered Escherichia coli strain for fatty acid production (FA3). This strain harbors an in-cell feedback controller, which decelerates acetyl-CoA carboxylase (ACC) overexpression in response to sensing the concentration of malonyl-CoA formed by this enzyme. A mathematical model for FA3 was constructed and validated using experimental data. Simulations assuming various PMM revealed that the HISICC using FA3 effectively mitigates toxicity from excessive ACC by robustly adapting braking its overexpression, minimizing yield loss. This study confirmed HISICC as a viable strategy for enhancing bioprocess efficiency, especially in balancing the bottleneck enzyme levels.

synthetic biology↗

bioRxiv2023

A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch

The optimization of bioprocess inputs using mathematical models is widely practiced. However, the mismatch between model prediction and the actual process [called process-model mismatch (PMM)] is problematic; when a large PMM exists, the process inputs optimized using the mathematical model in advance are no longer optimal for the actual process. In this study, we propose a hybrid control system that combines model-based optimization (in silico feedforward controller) and feedback controllers using synthetic genetic circuits integrated into cells (in-cell feedback controller) - which we named the hybrid in silico/in-cell controller (HISICC) - as a solution to this PMM issue. As a proof of concept for HISICC, we constructed a mathematical model of an engineered Escherichia coli strain for the isopropanol production process that was previously developed. This strain contains an in-cell feedback controller, and its combination with an in silico controller can be regarded as an example of HISICC. We demonstrated the robustness of HISICC against PMM by comparing the strain with another strain with no in-cell feedback controller in simulations assuming PMM of various magnitudes.

synthetic biology↗

bioRxiv2022

On-line reoptimization of mammalian fed-batch culture using a nonlinear model predictive controller

Fed-batch culture enables high productivity by maintaining low substrate concentrations in the early stage of the culture to suppress the accumulation of by-products that are harmful to cell growth. Therefore, they are widely used in the production of biopharmaceuticals by mammalian cells. However, there exists a trade-off in the design of the fed-batch process: early feeding results in the accumulation of harmful by-products, whereas late feeding results in a shortage of substrates needed for cell growth and synthesis of the desired product. To manage this trade-off and maximize the product yield, model-based optimization of the feeding trajectory has been reported in several studies. A significant drawback of this off-line optimization approach is the mismatch between the predictions made using the model and the actual process states, called the process-model mismatch (PMM). If the PMM is large, the off-line optimized feeding trajectory is no longer optimal for the actual process, resulting in lower product yields. Mammalian cell culture models typically contain dozens of unknown parameters that must be estimated prior to optimization. Sufficient parameter estimation is often unachievable owing to the nonlinear nature of these models. We believe that reoptimizing the feeding trajectory in real time using a nonlinear model predictive controller (NLMPC) is an effective solution to this PMM. Although NLMPC is a model-based feedback controller widely utilised in mammalian fed-batch culture, only a few studies have applied it to on-line reoptimization, and it remains unclear whether NLMPC with a standard kinetic model can effectively compensate for a large PMM. In this study, we demonstrated the reoptimization of the feeding trajectory with a NLMPC using two previously reported standard monoclonal antibody (mAb) production models. In both models, NLMPC successfully suppressed the reduction in mAb yield caused by the intentional introduction of PMM.

bioengineering↗

bioRxiv2021

Decoding cellular deformation from pseudo-simultaneously observed Rho GTPase activities

The inability to simultaneously observe all of the important Rho GTPases (Cdc42, Rac1, and RhoA) has prevented us from obtaining evidence of their coordinated regulation during cell deformation. Here, we propose Motion-Triggered Average (MTA), an algorithm that converts individually observed GTPases into pseudo-simultaneous observations. Using the time series obtained by MTA and mathematical model, we succeeded for the first time in decoding the cell edge velocity from the three GTPase activities to provide clear numerical evidence for coordinated cell edge regulation by the three GTPases. We found that the characteristics of the obtained activities were consistent with those of previous studies, and that GTPase activities and their derivatives were involved in edge regulation. Our approach provides an effective strategy for using single-molecule observations to elucidate problems hampered by the lack of simultaneous observations.

systems biology↗

bioRxiv2019

Single-cell information analysis reveals small intra- and large intercellular variations increase cellular information capacity

Cells transmit information about extracellular stimulation through signaling pathways to control cellular function. A signaling pathway can be regarded as a communication channel. In the analysis of channels of cell populations, intercellular variation is considered noise. However, intercellular variation enables individual cells to encode different information. Therefore, at the single-cell level, each cell can be regarded as an independent channel. Thus, we propose that responses of cells of the same type in tissues, such as the fibers in a skeletal muscle, should be regarded as a multiple-cell channel composed of single-cell channels, in which intercellular variation contains information. Here, we applied electrical pulses to individual myotubes from cultured C2C12 cells or dissociated skeletal muscle fibers and measured Ca2+ responses or contraction, respectively, to estimate information capacity in a biological system. For each muscle cell system, we found that a single-cell channel transmitted more information than did a cell-population channel, indicating that the cellular response is consistent with each cell (low intracellular variation) but different among individual cells (high intercellular variation). As cell number and thus the number of single-cell channels increased, a multiple-cell channel transmitted more information by incorporating the differences among individual cells. Thus, a tissue with small intracellular and large intercellular variations has the capacity to distinguish differences in stimulation intensity to precisely control physiological function.\n\nOne Sentence SummarySmall intracellular and large intercellular variations increase information transmission for precise control of tissue function.

systems biology↗

bioRxiv2018

Development of a microfluidic cell culture and monitoring system for intracellular signaling studies

We describe a microfluidic cell culture and monitoring system that temporally controls molecule concentrations around cells cultured in a small space. The simple system consists of three syringe pumps and a microfluidic device with two inlet ports and two outlet ports. Each syringe pump discharges or draws culture medium, solutions containing signal molecules, or cell suspensions through a port in a programmed flow rate sequence. Signal molecule solutions of differing concentration are merged in a microchannel, mixed immediately, and transported into the cell culture chamber. Regulating the flow rate ratio of syringe pumps over time enables dynamic control of the concentration of signal molecules in the cell culture chamber. The system provides various time-dependent waveforms of concentration over cultured cells, including pulse, rectangular, and triangular. The practical performance of the system for concentration control was evaluated using fluorescent dye imaging. The system was also used with CHO-K1 cells to measure intracellular Ca2+ concentrations, which vary with extracellular ATP levels. When a rectangular pulse of ATP was applied to the cells, Ca2+ levels increased quickly. By contrast, several Ca2+ peaks were observed in response to stepwise increases in ATP concentration. Single-cell Ca2+ responses to ATP pulse stimulation were analyzed by quantitative fluorescence imaging. Hierarchical clustering and quantitative analysis of single-cell data revealed the diversity of Ca2+ responses to ATP pulse stimulation. These results demonstrate that the microfluidic cell culture system is useful for studying a variety of cellular responses, including cell signaling.

bioengineering↗

bioRxiv2017

System Identification Using Compressed Sensing Reveals Signaling-Decoding System By Gene Expression

Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales.\n\nHighlightsO_LIWe developed a system identification method using compressed sensing.\nC_LIO_LIThis method allowed us to find a pathway using data of different time scales.\nC_LIO_LIWe identified a selective signaling-decoding system by gene expression.\nC_LIO_LIWe validated the identified system by pharmacological perturbation.\nC_LI\n\neTOC BlurbWe describe a system identification method of molecular networks with different time-scale data using a signal recovery technique in compressed sensing.

systems biology↗