Biology subjects

Brummer, A. B.

Publications and source records attributed to Brummer, A. B..

5 recordsLinked to original sources

bioRxiv2024

Improved tests for the origin of allometric scaling across tree architectures

The scaling of organismal metabolic rates with body size is one of the most prominent empirical patterns in biology. For over a century, the nature and causes of metabolic scaling have been the subject of much focus and debate. West, Brown, and Enquist (WBE) proposed a general model for the origin of metabolic scaling from branching vascular networks. However, recent empirical tests of WBE vascular scaling predictions in plants and animals have reported deviations caused by variability in network geometry. After clarifying the core assumptions of the WBE model, we revisit the methods and conclusions of recent tests conducted in trees, finding support for key WBE predictions in woody plant architecture. To do this, we apply an approach that better captures: i) network branching self-similarity and ii) leaf area as a proxy of plant metabolic capacity. The WBE model also predicts curvature in metabolic scaling in smaller organisms, and we introduce a novel method that accounts for curvature in plant branching geometry. Together, these advances allow more direct measurements of metabolic scaling than previous work, and we apply them to a dataset of diverse laser-scanned tree architectures. Analyses reveal the predicted interspecific [3/4] metabolic scaling across tree crowns, with intraspecific variation within individual tree crowns. Scaling variability is consistent with WBE predictions for curvature from asymptotic growth and underlying variation in branching geometry. We conclude that linking fine-scale branching variation to metabolic scaling allometries remains a challenge, while our results support the foundational hypotheses of the WBE model. Author summaryTrees survive in a variety of habitats and lifestyles across Earth. They are also characterized by a stunning array of sizes and shapes that make trees objects of vast cultural, economic, and ecological importance. At the same time, the need to link vascular plant function with traits and environment is more pressing than ever. Size (body mass) is fundamentally linked to plant functioning within ecosystems through allometric relationships. Allometric relationships emerge from the geometry of branch networks in trees, which are increasingly well-characterized with remote-sensing data. We use a dataset of laser-scanned tree crowns to test allometric predictions that link size to key traits, particularly metabolic capacity, understood as total leaf area. Our results indicate that i) scanning technology can provide accurate assessments of branch allometry with proper data preparation, and ii) studying branch allometries provides an organizing framework for interpreting natural variation in tree architecture.

plant biology↗

bioRxiv2023

Non-invasive measurement of intra-tumoral fluid dynamics with localized convolutional function regression

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to non-invasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that it will contribute to the better understanding of cancer progression and therapeutic response. One-Sentence SummaryA physics-informed computational method enables accurate and efficient measurement of fluid dynamics in individual patient tumors and demonstrates differences between tissues.

bioengineering↗

bioRxiv2023

Neuronal Branching is Increasingly Asymmetric Near Synapses, Potentially Enabling Plasticity While Minimizing Energy Dissipation and Conduction Time

Neurons primary function is to encode and transmit information in the brain and body. The branching architecture of axons and dendrites must compute, respond, and make decisions while obeying the rules of the substrate in which they are enmeshed. Thus, it is important to delineate and understand the principles that govern these branching patterns. Here, we present evidence that asymmetric branching is a key factor in understanding the functional properties of neurons. First, we derive novel predictions for asymmetric scaling exponents that encapsulate branching architecture associated with crucial principles such as conduction time, power minimization, and material costs. We compare our predictions with extensive data extracted from images to associate specific principles with specific biophysical functions and cell types. Notably, we find that asymmetric branching models lead to predictions and empirical findings that correspond to different weightings of the importance of maximum, minimum, or total path lengths from the soma to the synapses. These different path lengths quantitatively and qualitatively affect energy, time, and materials. Moreover, we generally observe that higher degrees of asymmetric branching-- potentially arising from extrinsic environmental cues and synaptic plasticity in response to activity-- occur closer to the tips than the soma (cell body).

neuroscience↗

bioRxiv2022

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.

cancer biology↗

bioRxiv2021

Destabilization of CAR T-cell treatment efficacy in the presence of dexamethasone

Chimeric antigen receptor (CAR) T-cell therapy is potentially an effective targeted immunotherapy for glioblastoma, yet there is presently little known about the efficacy of CAR T-cell treatment when combined with the widely used anti-inflammatory and immunosuppressant glucocorticoid, dexamethasone. Here we present a mathematical model-based analysis of three patient-derived glioblastoma cell lines treated in vitro with CAR T-cells and dexamethasone. Advanced in vitro experimental cell killing assay technologies allow for highly resolved temporal dynamics of tumor cells treated with CAR T-cells and dexamethasone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a nonautonomous, two-species predator-prey interaction of tumor cells and CAR T-cells, with explicit time-dependence in the clearance rate of dexamethasone. Using time as a bifurcation parameter, we show that (1) dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells in a dose-dependent manner and (2) as dexamethasone is cleared from the system, a stable coexistence equilibrium returns in the form of a Hopf bifurcation. With the model fit to experimental data, we demonstrate that high concentrations of dexamethasone antagonizes CAR T-cell efficacy by exhausting, or reducing the activity of CAR T-cells, and by promoting tumor cell growth. Finally, we identify a critical threshold in the ratio of CAR T-cell death to CAR T-cell proliferation rates that predicts eventual treatment success or failure that may be used to guide the dose and timing of CAR T-cell therapy in the presence of dexamethasone in patients. Author summaryBioengineering and gene-editing technologies have paved the way for advance immunotherapies that can target patient-specific tumor cells. One of these therapies, chimeric antigen receptor (CAR) T-cell therapy has recently shown promise in treating glioblastoma, an aggressive brain cancer often with poor patient prognosis. Dexamethasone is a commonly prescribed anti-inflammatory medication due to the health complications of tumor associated swelling in the brain. However, the immunosuppressant effects of dexamethasone on the immunotherapeutic CAR T-cells are not well understood. To address this issue, we use mathematical modeling to study in vitro dynamics of dexamethasone and CAR T-cells in three patient-derived glioblastoma cell lines. We find that in each cell line studied there is a threshold of tolerable dexamethasone concentration. Below this threshold, CAR T-cells are successful at eliminating the cancer cells, while above this threshold, dexamethasone critically inhibits CAR T-cell efficacy. Our modeling suggests that in the presence of high dexamethasone reduced CAR T-cell efficacy, or increased exhaustion, can occur and result in CAR T-cell treatment failure.

cancer biology↗