Biology subjects

Cess, C. G.

Publications and source records attributed to Cess, C. G..

6 recordsLinked to original sources

bioRxiv2024

Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions

Interactions between cancer cells and immune cells in the tumor microenvironment influence tumor growth and can contribute to the response to cancer immunotherapies. It is difficult to gain mechanistic insights into the effects of cell-cell interactions in tumors using a purely experimental approach. However, computational modeling enables quantitative investigation of the tumor microenvironment, and agent-based modeling in particular provides relevant biological insights into the spatial and temporal evolution of tumors. Here, we develop a novel agent-based model (ABM) to predict the consequences of intercellular interactions. Furthermore, we leverage our prior work that predicts the transitions of CD8+ T cells from a naive state to a terminally differentiated state using Boolean modeling. Given the detailed incorporated to predict T cell state, we apply the integrated Boolean-ABM framework to study how the properties of CD8+ T cells influence the composition and spatial organization of tumors and the efficacy of an immune checkpoint blockade. Overall, we present a mechanistic understanding of tumor evolution that can be leveraged to study targeted immunotherapeutic strategies.

systems biology↗

bioRxiv2024

Compound-SNE: Comparative alignment of t-SNEs for multiple single-cell omics data visualisation

One of the first steps in single-cell omics data analysis is visualization, which allows researchers to see how well-separated cell-types are from each other. When visualizing multiple datasets at once, data integration/batch correction methods are used to merge the datasets. While needed for downstream analyses, these methods modify features space (e.g. gene expression)/PCA space in order to mix cell-types between batches as well as possible. This obscures sample-specific features and breaks down local embedding structures that can be seen when a sample is embedded alone. Therefore, in order to improve in visual comparisons between large numbers of samples, we introduce Compound-SNE, which performs what we term a soft alignment of samples in embedding space. We show that Compound-SNE is able to align cell-types in embedding space across samples and data modalities, while preserving local embedding structures from when samples are embedded independently.

bioinformatics↗

bioRxiv2023

Fitting agent-based models to tumor images using representation learning

Agent-based models (ABMs) have enabled great advances in the study of tumor development and therapeutic response, allowing researchers to explore the spatiotemporal evolution of the tumor and its microenvironment. However, these models face serious drawbacks in the realm of parameterization - ABM parameters are typically set individually based on various data and literature sources, rather than through a rigorous parameter estimation approach. While ABMs can be fit to time-course data, that type of data loses the spatial information that is a defining feature of ABMs. Tumor images provide spatial information; however, such images only represent individual timepoints, limiting their utility in calibrating the tumor dynamics predicted by ABMs. Furthermore, it is exceedingly difficult to compare tumor images to ABM simulations beyond a qualitative visual comparison. Without a quantitative method of comparing the similarity of tumor images to ABM simulations, a rigorous parameter fitting is not possible. Here, we present a novel approach that applies neural networks to represent both tumor images and ABM simulations as low dimensional points, with the distance between points acting as a quantitative measure of difference between the two. This enables a quantitative comparison of tumor images and ABM simulations, where the distance between simulated and experimental images can be minimized using standard parameter-fitting algorithms. Here, we describe this method and present two examples to demonstrate the application of the approach to estimate parameters for two distinct ABMs. Overall, we provide a novel method to robustly estimate ABM parameters.

systems biology↗

bioRxiv2021

Multiscale modeling of tumor adaption and invasion following anti-angiogenic therapy

In order to promote continued growth, a tumor must recruit new blood vessels, a process known as tumor angiogenesis. Many therapies have been tested that aim to inhibit tumor angiogenesis, thus starving the tumor of nutrients and preventing tumor growth. However, many of these therapies have been unsuccessful and can paradoxically further tumor development by leading to increased local tumor invasion and metastasis. In this study, we use agent-based modeling to examine how hypoxic and acidic conditions following anti-angiogenic therapy can influence tumor development. Under these conditions, we find that cancer cells experience a phenotypic shift to a state of higher survival and invasive capability, spreading further away from the tumor into surrounding tissue. Although anti-angiogenic therapy alone promotes tumor cell adaptation and invasiveness, we find that augmenting chemotherapy with anti-angiogenic therapy improves chemotherapeutic response and delays the time it takes for the tumor to regrow. Overall, we use computational modeling to explain the behavior of tumor cells in response to anti-angiogenic treatment in the dynamic tumor microenvironment.

systems biology↗

bioRxiv2020

Multi-scale modeling of macrophage - T cell interactions within the tumor microenvironment and impacts of macrophage-based immunotherapies

Within the tumor microenvironment, macrophages exist in an immunosuppressive state, preventing T cells from eliminating the tumor. Due to this, research is focusing on immunotherapies that specifically target macrophages in order to reduce their immunosuppressive capabilities and promote T cell function. In this study, we develop an agent-based model consisting of the interactions between macrophages, T cells, and tumor cells to determine how the immune response changes due to three macrophage-based immunotherapeutic strategies: macrophage depletion, recruitment inhibition, and macrophage reeducation. We find that reeducation, which converts the macrophages into an immune promoting phenotype, is the most effective strategy and that the macrophage recruitment rate and tumor proliferation rate (tumor-specific properties) have large impacts on therapy efficacy. We also employ a novel method of using a neural network to reduce the computational complexity of an intracellular signaling mechanistic model.

systems biology↗

bioRxiv2019

Data-Driven Analysis of a Mechanistic Model of CAR T Cell Signaling Predicts Effects of Cell-to-cell Heterogeneity

Due to the variability of protein expression, cells of the same population can exhibit different responses to stimuli. It is important to understand this heterogeneity at the individual level, as population averages mask these underlying differences. Using computational modeling, we can interrogate a system much more precisely than by using experiments alone, in order to learn how the expression of each protein affects a biological system. Here, we examine a mechanistic model of CAR T cell signaling, which connects receptor-antigen binding to MAPK activation, to determine intracellular modulations that can increase cellular response. CAR T cell cancer therapy involves removing a patients T cells, modifying them to express engineered receptors that can bind to tumor-associated antigens to promote cell killing, and then injecting the cells back into the patient. This population of cells, like all cell populations, would have heterogeneous protein expression, which could affect the efficacy of treatment. Thus, it is important to examine the effects of cell-to-cell heterogeneity. We first generated a dataset of simulated cell responses via Monte Carlo simulations of the mechanistic model, where the initial protein concentrations were randomly sampled. We analyzed the dataset using partial least-squares modeling to determine the relationships between protein expression and ERK phosphorylation, the output of the mechanistic model. Using this data-driven analysis, we found that only the expressions of proteins relating directly to the receptor and the MAPK cascade, the beginning and end of the network, respectively, are relevant to the cells response. We also found, surprisingly, that increasing the amount of receptor present can actually inhibit the cells ability to respond due to increasing the strength of negative feedback from phosphatases. Overall, we have combined data-driven and mechanistic modeling to generate detailed insight into CAR T cell signaling.

systems biology↗