Biology subjects

Kim, C. M.

Publications and source records attributed to Kim, C. M..

6 recordsLinked to original sources

bioRxiv2026

Hydrogel microneedle patch for low-cost, colorimetric, continuous glucose monitoring

Diabetes is a chronic condition characterized by dysregulated blood glucose levels. Continuous glucose monitors (CGMs) enable minimally invasive, real-time monitoring of glucose levels, but broader access requires devices that are both effective and affordable. Popular CGMs such as Dexcom and FreeStyle Libre have high accuracy and are integrated with mobile applications to display real-time data and alerts but are limited by high cost. We developed a low-cost, electronic-free, microneedle hydrogel patch CGM that utilizes colorimetric detection to continuously monitor glucose levels. The proposed patch penetrates the skin and absorbs interstitial fluid (ISF) containing glucose. This glucose reacts with glucose oxidase (GOx) enzyme embedded in the hydrogel, producing gluconic acid and decreasing the pH of the hydrogel. As glucose levels increase, decreasing pH levels are reported by the loaded pH indicator bromocresol purple (BCP) via a color change from purple to yellow. This system was validated through a combination of in vitro experiments and COMSOL computational modeling to assess performance at physiologically relevant glucose concentrations. The resulting hydrogels demonstrated reversible and dynamic color changes in response to glucose levels ranging from 3.5-10 mmol/L (63-180 mg/dL), supporting their potential for continuous monitoring applications. A quantitative, image-based analysis method enabled estimation of glucose concentration from photographs, demonstrating strong predictive performance (RMSE=8.59 mg/dL, R2=0.94). Although optimization of mechanical strength and diffusion kinetics remain necessary, these findings highlight the promise of integrating colorimetric sensing with microneedle-based sampling for affordable, user-friendly glucose monitoring.

bioengineering↗

bioRxiv2026

Solanum americanum Bs2 and ZAR1 homologs recognize Xanthomonas euvesicatoria effectors essential for infection.

Multiple recognition events of pathogen-secreted effectors by immune receptors confer robust disease resistance in plants. Understanding the underlying mechanisms facilitates the discovery and deployment of valuable resistance genes for crop protection. Xanthomonas euvesicatoria causes devastating bacterial spot disease in solanaceous crops but cannot infect the wild relative Solanum americanum. Here, we identified X. euvesicatoria type III effectors (T3Es) that induce cell death in S. americanum when transiently expressed in leaf. By quantifying immune responses of the S. americanum SP2273 accession to X. euvesicatoria multiple-T3E knockout mutants, we demonstrated that at least nine T3Es (AvrBs2, XopAP, XopAU, XopE1, XopJ3, XopM, XopN, XopX, and XopZ1) collectively contribute to effector-triggered immunity (ETI). Among these, AvrBs2 and XopJ3 were the primary drivers of ETI, eliciting robust cell death and defense gene expression when naturally delivered into plant cells. We next generated S. americanum lines concomitantly edited at the corresponding immune receptor loci, SaBs2 and SaZAR1 (SP2273-bz). Genetic complementation of SP2273-bz confirmed that AvrBs2 and XopJ3 are specifically recognized by each of the four SaBs2 homologs and by SaZAR1, respectively. Moreover, enhanced growth of X. euvesicatoria on the characterized SP2273-bz line indicated that both effectors are required for bacterial multiplication in S. americanum. Together, our findings provide a framework for understanding the mechanisms of ETI-mediated resistance and establish a genetic foundation for resistance breeding in solanaceous crops.

plant biology↗

bioRxiv2026

Mathematical modeling and sensitivity analysis of synNotch-CAR T-cells identify engineering targets for dynamic tunability

Cancer therapeutics are increasingly incorporating engineered receptors due to their ability to detect extracellular ligands and initiate intracellular responses that regulate gene expression. By redesigning these natural signaling systems, synthetic receptors hold great potential for use in novel cell-based therapies. One particularly promising direction is modifying the Notch receptor, a transmembrane protein that naturally mediates ligand-dependent signaling at the cell surface to regulate cell proliferation and differentiation in neurogenesis. Both the intracellular and extracellular domains of Notch can be replaced with alternative domains, creating the family of modified Notch receptors known as synthetic Notch (synNotch). In existing synNotch-activated chimeric antigen receptor (CAR) T-cells, the extracellular domain can be engineered to adjust binding affinity for a specific cancer antigen, enabling precise tuning of therapeutic activity while minimizing off-target effects. To quantify and inform such tuning, we develop differential equations models of synNotch receptor signaling and subsequent gene expression. The mathematical models couple activation dynamics on fast timescales (characteristic of receptor-ligand interactions) and on slow timescales (characteristic of downstream gene expression dynamics). Global Sobol sensitivity analysis of the proposed models highlights parameters that yield the greatest variability in synNotch signal transduction and gene expression, indicating their potential to be engineered for different functions in future cancer therapeutics. For the receptor-ligand interactions in the synNotch model, we find that ligand association and ligand-independent activation are the most sensitive parameters. In the downstream gene expression model, promoter strength and degradation rates of mRNA and gene product are found to be most amenable to engineering.

synthetic biology↗

bioRxiv2026

Topological Entropy Correlates with the Predictive Power of Multiplexed Ensemble Reservoir Computing

Modeling nonlinear, multiscale, and transiently chaotic biological processes remains a major challenge in computational biology. Traditional deep learning models, while powerful, require large datasets and lack mechanistic interpretability, limiting their effectiveness for time-resolved biological systems. Reservoir computing (RC) offers a promising alternative by leveraging the rich transient dynamics of fixed nonlinear systems, yet standard RC architectures struggle with high-dimensional biological data and complex temporal regimes. Here, we introduce Dynamical System Machine Learning (DynML), a multiplexed reservoir framework designed to model gene-expression dynamics in systems such as liver regeneration and Drosophila embryogenesis. DynML encodes biological signals using heterogeneous Lorenz reservoirs and employs a single global readout to capture stage-dependent dynamics with high predictive accuracy. We further show that reservoir topological entropy quantitatively predicts model performance, linking dynamical richness to biological forecasting accuracy. Beyond biological time-series modeling, we demonstrate the generality of DynML on the MNIST handwritten digit classification task using a Rossler-based chaotic reservoir, showing that fixed dynamical cores with linear readouts can also support high-dimensional static classification. Overall, DynML provides a scalable, interpretable, and computationally efficient framework that unifies biological time-series modeling and conventional machine-learning tasks within a single dynamical systems paradigm. Author summaryComplex biological phenomena such as development, regeneration, and disease progression emerge from time-dependent gene-expression programs governed by nonlinear, multiscale dynamics. Capturing these dynamics remains challenging for conventional machine-learning approaches, which typically require large datasets and lack interpretability. In this study, we introduce Dynamical System Machine Learning (DynML), a modeling framework that leverages the transient dynamics of chaotic systems to learn and predict biological time series. DynML transforms gene-expression measurements into high-dimensional dynamical representations using ensembles of nonlinear reservoirs, enabling accurate prediction of future expression states with simple and interpretable linear readouts. We apply DynML to both synthetic dynamical systems and real biological datasets, including Drosophila embryonic development and human liver regeneration, where it achieves high predictive accuracy across multiple temporal transitions. Importantly, we show that the predictive performance of DynML is strongly linked to the topological entropy of the reservoir dynamics, providing a principled and quantitative measure of model expressiveness. Beyond biological time-series prediction, we demonstrate that the same dynamical framework can also classify static data, achieving strong performance on handwritten digit recognition. Together, our results establish DynML as a scalable and interpretable approach for modeling complex biological dynamics, and highlight how concepts from dynamical systems theory can guide the design of effective machine-learning models for biological data.

systems biology↗

bioRxiv2024

Neural dynamics of reversal learningin the prefrontal cortex and recurrent neural networks

In probabilistic reversal learning, the choice option yielding reward with higher probability switches at a random trial. To perform optimally in this task, one has to accumulate evidence across trials to infer the probability that a reversal has occurred. We investigated how this reversal probability is represented in cortical neurons by analyzing the neural activity in the prefrontal cortex of monkeys and recurrent neural networks trained on the task. We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model. The reversal probability activity at the start of a trial was stationary, stable and consistent with the attractor dynamics. However, during the trial, the activity was associated with task-related behavior and became non-stationary, thus deviating from the line attractor. Fitting a predictive model to neural data showed that the stationary state at the trial start serves as an initial condition for launching the non-stationary activity. This suggested an extension of the line attractor model with behavior-induced non-stationary dynamics. The non-stationary trajectories were separable indicating that they can represent distinct probabilistic values. Perturbing the reversal probability activity in the recurrent neural networks biased choice outcomes demonstrating its functional significance. In sum, our results show that cortical networks encode reversal probability in stable stationary state at the start of a trial and utilize it to initiate non-stationary dynamics that accommodates task-related behavior while maintaining the reversal information.

neuroscience↗

bioRxiv2018

Learning recurrent dynamics in spiking networks

Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity of a balanced network, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.

bioinformatics↗