Biology subjects

Rajakaruna, H.

Publications and source records attributed to Rajakaruna, H..

4 recordsLinked to original sources

bioRxiv2025

A framework integrating multiscale in-silico modeling and experimental data predicts CD33 CAR-NK cytotoxicity across target cell types

Uncovering mechanisms and predicting tumor cell responses to CAR-NK cytotoxicity is essential for improving therapeutic efficacy. Currently, the complexity of these effector-target interactions and the donor-to-donor variations in NK cell receptor (NKR) repertoire require functional assays to be performed experimentally for each manufactured CAR-NK cell product and target combination. Here, we developed a computational mechanistic multiscale model which considers heterogenous expression of CARs, NKRs, adhesion receptors and their cognate ligands, signal transduction, and NK cell-target cell population kinetics. The model trained with quantitative flow cytometry and in vitro cytotoxicity data accurately predicts the short- and long- term cytotoxicity of CD33CAR-NK cells against leukemia cell lines across multiple CAR designs. Furthermore, using Pareto optimization we explored the effect of CAR proportion and NK cell signaling on the differential cytotoxicity of CD33CAR-NK cells to cancer and healthy cells. This model can be extended to predict CAR-NK cytotoxicity across many antigens and tumor targets.

cancer biology↗

bioRxiv2023

A Multiscale Protein Abundance Structured Population Kinetic Model Systematically Explores the Design Space of Constitutive and Inducible CAR-T cells

Engineered chimeric antigen receptor (CAR)-T cells are designed to bind to antigens overexpressed on the surface of tumor cells and induce tumor cell lysis. However, healthy cells can express these antigens at lower abundances and can get lysed by CAR-T cells. A wide variety of CAR-T cells have been designed that increase tumor cell elimination while decreasing destruction of healthy cells. However, given the cost and labor-intensive nature of such efforts, a systematic exploration of potential hypotheses becomes limited. To this end, we develop a framework (PASCAR) by combining multiscale population dynamic models and multi-objective optimization approaches with data obtained from published cytometry and cytotoxicity assays to systematically explore design space of constitutive and tunable CAR-T cells. We demonstrate PASCAR can quantitatively describe in vitro and in vivo results for constitutive and inducible CAR-T cells and can successfully predict experiments outside the training data. Our exploration of the CAR design space reveals that CAR affinities in an intermediate range of dissociation constants (KD) in constitutive and tunable CAR-T cells can dramatically decrease healthy cell lysis but sustain a high rate of tumor cell killing. In addition, our modeling provides guidance towards optimal tuning of CAR expressions in synNotch CAR T cells. The proposed framework can be extended for other CAR immune cells.

systems biology↗

bioRxiv2022

Evaluating impact of experimental design on accuracy of parameter estimation and model selection efficiency: T cell clustering as a case study

Mathematical modeling provides a rigorous way to quantify immunological processes and to discriminate between alternative mechanisms driving specific biological phenomena. It is typical that mathematical models of immunological phenomena are developed by modellers to explain specific sets of experimental data after the data have been collected by experimental collaborators. Whether the available data are sufficient to accurately estimate model parameters or to discriminate between alternative models is not typically investigated. While previously collected data may be sufficient to guide development of alternative models and help estimating model parameters, such data often do not allow to discriminate between alternative models. As a case study we develop a series of power analyses to determine optimal sample sizes that allow for accurate estimation of model parameters and for discrimination between alternative models describing clustering of T cells around Plasmodium liver stages. In our typical experiments, mice are infected intravenously with Plasmodium sporozoites that invade hepatocytes (liver cells), and then activated CD8 T cells are transferred into the infected mice. The number of T cells found in the vicinity of individual infected hepatocytes at different times after T cell transfer is counted using intravital microscopy. We previously developed a series of mathematical models aimed to explain highly variable number of T cells per parasite; one of such model, the density-dependent recruitment (DDR) model, fitted the data from preliminary experiments better than the alternative models, such as the density-independent exit (DIE) model. Here we show that the ability to discriminate between these alternative models depends on the number of parasites imaged in the analysis; analysis of about n = 50 parasites at 2, 4, and 8 hours after T cell transfer will allow for over 95% probability to select the correct model. The type of data collected also has an impact; following T cell clustering around individual parasites over time (called as longitudinal (LT) data) allows for a more precise and less biased estimates of the parameters of the DDR model than that generated from a more traditional way of imaging individual parasites in different liver areas/mice (cross-sectional (CS) data). However, LT imaging comes at a cost of a need to keep the mice alive under the microscope for hours which may be ethically unacceptable. We finally show that the number of time points at which the measurements are taken also impacts the precision of estimation of DDR model parameters; in particular, measuring T cell clustering at one time point does not allow accurately estimating all parameters of the DDR model. Using our case study, we propose a general framework on how mathematical modeling can be used to guide experimental designs and power analyses of complex biological processes.

immunology↗

bioRxiv2020

Environment-imposed constraints make Brownian walkers efficient searchers

Pathogen-specific CD8 T cells face the problem of finding rare cells that present their cognate antigen either in the lymph node or infected tissue. While quantitative details of T cell movement strategies in some tissues such lymph nodes or skin have been relatively well characterized, we still lack quantitative understanding of T cell movement in many other important tissues such as the spleen, lung, liver, and gut. Furthermore, how environmental constraints influence movement patterns of T cells in tissues remains incompletely characterized. Liver is one of the major organs in mice and humans and is the target site for many pathogens such as hepatitis B and C viruses in humans and Plasmodium parasites in multiple mammalian species. We developed a protocol to generate stable numbers of liver-located CD8 T cells, used intravital microscopy to record movement patterns of CD8 T cells in livers of live mice, and analyzed these and previously published data using well-established statistical and computational methods. We show that in most of our experiments Plasmodium-specific liver-localized CD8 T cells perform correlated random walks characterized by transiently superdiffusive displacement with persistence times of 10-15 min that exceed those observed for T cells in lymph nodes. Liver-localized CD8 T cells typically crawl on the lumenal side of liver sinusoids (i.e., are in the blood); simulating T cell movement in digital structures derived from the liver sinusoids illustrates that liver structure alone is sufficient to explain the relatively long superdiffusive displacement of T cells. In experiments when CD8 T cells in the liver poorly attach to the sinusoids (e.g., one week post immunization with radiation-attenuated Plasmodium sporozoites) T cells also undergo Levy flights - large displacements occurring due to cell deattaching from the endothelium, floating with the blood flow, and re-attaching at another location. Our analysis, thus, provides quantitative details of movement patterns of liver-localized CD8 T cells and illustrates how structural and physiological details of the tissue may impact T cell movement patterns.

immunology↗