Cell-level Parameters Research Articles

Dynamic Gain Decomposition Reveals Functional Effects of Dendrites, Ion Channels, and Input Statistics in Population Coding.

Modern, high-density neuronal recordings reveal at ever higher precision how information is represented by neural populations. Still, we lack the tools to understand these processes bottom-up, emerging from the biophysical properties of neurons, synapses, and network structure. The concept of the dynamic gain function, a spectrally resolved approximation of a population's coding capability, has the potential to link cell-level properties to network-level performance. However, the concept is not only useful but also very complex because the dynamic gain's shape is co-determined by axonal and somato-dendritic parameters and the population's operating regime. Previously, this complexity precluded an understanding of any individual parameter's impact. Here, we decomposed the dynamic gain function into three components corresponding to separate signal transformations. This allowed attribution of network-level encoding features to specific cell-level parameters. Applying the method to data from real neurons and biophysically plausible models, we found: (1) The encoding bandwidth of real neurons, approximately 400 Hz, is constrained by the voltage dependence of axonal currents during early action potential initiation. (2) State-of-the-art models only achieve encoding bandwidths around 100 Hz and are limited mainly by subthreshold processes instead. (3) Large dendrites and low-threshold potassium currents modulate the bandwidth by shaping the subthreshold stimulus-to-voltage transformation. Our decomposition provides physiological interpretations when the dynamic gain curve changes, for instance during spectrinopathies and neurodegeneration. By pinpointing shortcomings of current models, it also guides inference of neuron models best suited for large-scale network simulations.

Correlations of lithium-ion battery parameter variations and connected configurations on pack statistics

Battery cell-to-cell parameter variations and connected configurations jointly affect pack performance. Knowledge of the quantitative correlations of lithium-ion battery parameter variations and connected configurations on pack statistics is crucial for understanding and improving the pack performance in the automotive industry. From a statistical perspective, this study theoretically developed analytical correlations to analyze the impact of the statistics (mean and standard deviation) of cell-level parameters (capacity and resistance) and connected configurations on module/pack statistical performance under a random sampling scenario. These correlations are applied to various cell-to-cell parameter variations and module/pack configurations and are verified by Monte Carlo simulations. The results demonstrate that cell-to-cell variations have a negative effect on the statistical performance of modules, especially series mean capacity. Meanwhile, parallel and series configurations can dramatically reduce the variation in module-level parameters, and the variations in parallel or series modules with four cells are 50% of those of cell-to-cell. Therefore, parallel configurations can effectively compensate for the loss of mean capacity caused by series configuration so that the pack with parallel modules connected in series has the capacity advantages compared to that of the pack with series modules connected in parallel, but their resistance statistics are the same. The correlations developed in this study provide important guidance for battery screening and the selection of pack configuration and equalization topology, thereby giving critical information for optimizing pack performance from the cell level to the pack level.

Bayesian Tensor Response Regression with an Application to Brain Activation Studies

This article proposes a novel Bayesian implementation of regression with multi-dimensional array (tensor) response on scalar covariates. The recent emergence of complex datasets in various disciplines presents a pressing need to devise regression models with a tensor valued response. This article considers one such application of detecting neuronal activation in fMRI experiments in presence of tensor valued brain images and scalar predictors. The overarching goal in this application is to identify spatial regions (voxels) of a brain activated by an external stimulus. In such and related applications, we propose to regress responses from all cells (or voxels in brain activation studies) together as a tensor response on scalar predictors, accounting for the structural information inherent in the tensor response. To estimate model parameters with proper cell specific shrinkage, we propose a novel multiway stick breaking shrinkage prior distribution on tensor structured regression coefficients, enabling identification of cells which are related to the predictors. The major novelty of this article lies in the theoretical study of the contraction properties for the proposed shrinkage prior in the tensor response regression when the number of cells grows faster than the sample size. Specifically, estimates of tensor regression coefficients are shown to be asymptotically concentrated around the true sparse tensor in L2-sense under mild assumptions. Various simulation studies and analysis of a brain activation data empirically verify desirable performance of the proposed model in terms of estimation and inference on cell-level parameters.

A Model for Temperature-Dependent Degradation in Lithium-Ion Batteries: Correlating Electrochemical Phenomena with Cell-Level Performance Parameters

Significant progress has been made in developing lithium-ion battery models that incorporate transport phenomena, electrochemical kinetics, and thermodynamics. While these models have been used to produce reliable results for initial performance, they lack the ability to predict capacity degradation during cycling. The prediction of key performance parameters like battery lifetime and capacity is vital for the design and thermal management of the batteries. There is often a complex interplay of simultaneous degradation mechanisms with the electrochemical dynamics of the cell. These phenomena in turn influence trends in cell-level parameters of interest, namely cell capacity and voltage response. Degradation modeling is an active research area where several works have attempted to augment state-of-the-art electrochemical models with those of various degradation mechanisms.1 In this work, we aim to develop a detailed and efficient physics-based model which couples temperature gradients within the battery with the standard models for electrochemical SEI formation.2 The model inputs for temperature can be obtained experimentally or using a detailed thermal model for the battery. There have been past attempts to combine one or multiple degradation mechanisms.3 While these works provide valuable insight, a more comprehensive picture requires consideration of electrolyte dynamics and temperature variations within the battery. The poor predictions for battery degradation are related to spatial non-uniformities and difficulties in uniform thermal management, whether due to cost or performance constraints. There is also a significant variation in parameters, mechanisms, and predictive capability of the models in the literature. The present model will consider simultaneous battery degradation due to the SEI layer growth with temperature effects and will be integrated into state-of-the-art electrochemical models like the classic pseudo-two-dimensional (p2D) model4 to be able to present a comprehensive analysis of the battery performance parameters. In order to check for the computational challenges, the integration of fade and thermal models will be performed in two different solver environments to ensure accuracy and mitigate any convergence issues.The ultimate goal is a simple and efficient numerical framework for fast and robust simulations such that the developed model would be able to generate cell-level signatures of degradation over a wide range of operating conditions and temperatures. This tailor-made model can also be combined with available experimental data for an improved qualitative understanding, as well as quantitative prediction of the different stages of battery degradation. References Edge, J. S., O'Kane, S., Prosser, R., Kirkaldy, N. D., Patel, A. N., Hales, A., Ghosh, A., Ai, W., Chen, J., Jiang, J. and Li, S., Chem. Chem. Phys. (2021) DOI: 10.1039/D1CP00359C.Safari, M., Morcrette, M., Teyssot, A., and Delacourt, C., Electrochem. Soc., 156, A145 (2009).Reniers, J. M., Mulder, G. and Howey, D. A., Electrochem. Soc, 166(14), A3189 (2019).Fuller, T. F., Doyle, M., and Newman, J., J. Electrochem. Soc., 141, 1 (1994).

Modeling the Impact of Sequential Two-Electron Transfer Compounds on Redox Flow Battery Performance

Redox flow batteries (RFBs) are promising candidates for grid-scale energy storage, but further cost reductions are needed for widespread use.1 As such, redox-active organic molecules and metal-coordination complexes are emerging as charge storage materials considering their redox potential, solubility, and stability can be tailored through molecular functionalization.2,3 Moreover, a number of these compounds can support multiple electron transfers, affording dramatic increases in energy storage capacity.4,5 However, the nature of the electron transfer events, concerted or sequential, plays a key role in determining whether the added capacity can be accessed efficiently. Specifically, depending on molecular features and cell operation, voltaic inefficiencies may be incurred which, in turn, offset any benefits of increased capacity. Therefore, we sought to quantify these losses and their relationship to molecular- and cell-level parameters to better understand the feasibility of multi-electron transfer in RFBs.In this presentation, we use a zero-dimensional electrochemical model, accounting for thermodynamic, kinetic, and mass transfer, to simulate the performance of two-electron compounds in redox flow cells. First, using a single half-cell, we show that voltaic efficiency is significantly influenced by the average redox potential and potential difference between the redox events. We also discuss the effects of different mass transfer coefficients and comproportionation reaction rates. Second, using a full cell, we consider the effects of utilizing two-electron compounds in both half-cells and subsequent effects of charge imbalance, both resulting in further inefficiencies. By understanding the operational tradeoffs of multi-electron compounds, we can develop design criterion to guide molecular engineering efforts towards more efficient high energy density electrolytes for RFBs.AcknowledgementsThis work was funded by the National Science Foundation (NSF) under Award Number 1805566. B.J.N gratefully acknowledges the NSF Graduate Research Fellowship Program under Grant Number 1122374. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.References(1) Chalamala, B. R.; Soundappan, T.; Fisher, G. R.; Anstey, M. R.; Viswanathan, V. V.; Perry, M. L. Redox Flow Batteries: An Engineering Perspective. Proceedings of the IEEE 2014, 102 (6), 976–999.(2) Hogue, R. W.; Toghill, K. E. Metal Coordination Complexes in Nonaqueous Redox Flow Batteries. Current Opinion in Electrochemistry 2019.(3) Ding, Y.; Zhang, C.; Zhang, L.; Zhou, Y.; Yu, G. Molecular Engineering of Organic Electroactive Materials for Redox Flow Batteries. Chem. Soc. Rev. 2018, 47 (1), 69–103.(4) Kowalski, J. A.; Casselman, M. D.; Kaur, A. P.; Milshtein, J. D.; Elliott, C. F.; Modekrutti, S.; Attanayake, N. H.; Zhang, N.; Parkin, S. R.; Risko, C.; Brushett, F. R.; Odom, S. A. A Stable Two-Electron-Donating Phenothiazine for Application in Nonaqueous Redox Flow Batteries. J. Mater. Chem. A 2017, 5 (46), 24371–24379.(5) Sevov, C. S.; Fisher, S. L.; Thompson, L. T.; Sanford, M. S. Mechanism-Based Development of a Low-Potential, Soluble, and Cyclable Multielectron Anolyte for Nonaqueous Redox Flow Batteries. J. Am. Chem. Soc. 2016, 138 (47), 15378–15384.

The role of intracellular signaling in the stripe formation in engineered Escherichia coli populations.

Recent experiments showed that engineered Escherichia coli colonies grow and self-organize into periodic stripes with high and low cell densities in semi-solid agar. The stripes develop sequentially behind a radially propagating colony front, similar to the formation of many other periodic patterns in nature. These bacteria were created by genetically coupling the intracellular chemotaxis pathway of wild-type cells with a quorum sensing module through the protein CheZ. In this paper, we develop multiscale models to investigate how this intracellular pathway affects stripe formation. We first develop a detailed hybrid model that treats each cell as an individual particle and incorporates intracellular signaling via an internal ODE system. To overcome the computational cost of the hybrid model caused by the large number of cells involved, we next derive a mean-field PDE model from the hybrid model using asymptotic analysis. We show that this analysis is justified by the tight agreement between the PDE model and the hybrid model in 1D simulations. Numerical simulations of the PDE model in 2D with radial symmetry agree with experimental data semi-quantitatively. Finally, we use the PDE model to make a number of testable predictions on how the stripe patterns depend on cell-level parameters, including cell speed, cell doubling time and the turnover rate of intracellular CheZ.

An Analytical Approach to Investigate the Evolution of Bone Volume Fraction in Bone Remodeling Simulation at the Tissue and Cell Level

Simulation of bone remodeling at the bone cell level can predict changes in bone microarchitecture and density due to bone diseases and drug treatment. Their clinical application, however, is limited since bone microarchitecture can only be measured in the peripheral skeleton of patients and since the simulations are very time consuming. To overcome these issues, we have developed an analytical model to predict bone density adaptation at the organ level, in agreement with our earlier developed bone remodeling theory at the cellular level. Assuming a generalized geometrical model at the microlevel, the original theory was reformulated into an analytical equation that describes the evolution of bone density as a function of parameters that describe cell activity, mechanotransduction and mechanical loading. It was found that this analytical model can predict changes in bone density due to changes in these cell-level parameters that are in good agreement with those predicted by the earlier numerical model that implemented a detailed micro-finite element (FE) model to represent the bone architecture and loading, at only a fraction of the computational costs. The good agreement between analytical and numerical density evolutions indicates that the analytical model presented in this study can predict well bone functional adaptation and, eventually, provide an efficient tool for simulating patient-specific bone remodeling and for better prognosis of bone fracture risk.

A Simplified Procedure to Determine the Optimal Rate of Freezing Biological Systems

The effect of several cell-level parameters on the predicted optimal cooling rate B(opt) of an arbitrary biological system has been studied using a well-defined water transport model. An extensive investigation of the water transport model revealed three key cell level parameters: reference permeability of the membrane to water L(pg), apparent activation energy E(Lp), and the ratio of the available surface area for water transport to the initial volume of intracellular water (SA/WV). We defined B(opt) as the "highest" cooling rate at which a predefined percent of the initial water volume is trapped inside the cell (values ranging from 5% to 80%) at a predefined end temperature (values ranging from -5 degrees C to -40 degrees C). Irrespective of the choice of the percent of initial water volume trapped and the end temperature, an exact and linear relationship exists between L(pg), SA/WV, and B(opt0. However, a nonlinear and inverse relationship is found between E(Lp) and B(opt). Remarkably, for a variety of biological systems a comparison of the published experimentally determined values of B(opt) agreed quite closely with numerically predicted B(opt) values when the model assumed 5% of initial water is trapped inside the cell at a temperature of -15 degrees C. This close agreement between the experimental and model predicted optimal cooling rates is used to develop a generic optimal cooling rate chart and a generic optimal cooling rate equation that greatly simplifies the prediction of the optimal rate of freezing of biological systems.

Genome size and developmental complexity.

Haploid genome size (C-value) is correlated positively with cell size, and negatively with cell division rate, in a variety of taxa. Because these associations are causative, genome size has the potential to impact (and in turn, be influenced by) organism-level characters affected by variation in either of these cell-level parameters. One such organismal feature is development. Developmental rate, in particular, has been associated with genome size in numerous plant, vertebrate, and invertebrate groups. However, rate is only one side of the developmental coin; the other important component is complexity. When developmental complexity is held essentially constant, as among many plants, developmental rate is the visibly relevant parameter. In this case, genome size can impose thresholds on developmental lifestyle (and vice versa), as among annual versus perennial plants. When developmental rate is constrained (as during time-limited amphibian metamorphosis), complexity becomes the notable variable. An appreciation for this rate-complexity interaction has so far been lacking, but is essential for an understanding of the relationships between genome size and development. Moreover, such an expanded view may help to explain patterns of variation in taxa as diverse as insects and fish. In each case, a hierarchical approach is necessary which recognizes the complex interaction of evolutionary processes operating at several levels of biological organization.