Biology subjects

Heiko Enderling

Publications and source records attributed to Heiko Enderling.

6 recordsLinked to original sources

bioRxiv2016

Enhancing synergy of CAR T cell therapy and oncolytic virus therapy for pancreatic cancer.

The poor immunogenicity of pancreatic tumors makes them particularly difficult to treat. Standard chemotherapies and single agent immunotherapies have had notoriously little success in this arena. Oncolytic virus therapy has the potential to enhance the penetration of immunotherapeutically-delivered CAR T cells into the tumor and improve treatment outcomes. We evaluate this potential by combining two different mathematical approaches: an ordinary differential equation model to simulate population level tumor response to cytotoxic activity of T cells, coupled with an agent-based model to simulate the enhancement of CAR T cell penetration by oncolytic virus therapy.

Cancer Biology↗

bioRxiv2015

Agent-based modeling of cancer stem cell driven solid tumor growth

Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth.

Systems Biology↗

bioRxiv2015

Vectorization techniques for efficient agent-based model simulations of tumor growth

AbstractMulti-scale agent-based models are increasingly used to simulate tumor growth dynamics. Simulating such complex systems is often a great challenge despite large computational power of modern computers and, thus, implementation techniques are becoming as important as the models themselves. Here we show, using a simple agent-based model of tumor growth, how the computational time required for simulation can be decreased by using vectorization techniques. In numerical examples we observed up to 30-fold increases in computation performance when standard approaches were, at least in part, replaced with vectorized routines in MATLAB.

Systems Biology↗

bioRxiv2015

Integrating experimental data to calibrate quantitative cancer models

AbstractFor quantitative cancer models to be meaningful and interpretable the number of unknown parameters must be kept minimal. Experimental data can be utilized to calibrate model dynamics rates or rate constants. Proper integration of experimental data, however, depends on the chosen theoretical framework. Using live imaging of cell proliferation as an example, we show how to derive cell cycle distributions in agent-based models and averaged proliferation rates in differential equation models. We focus on a tumor hierarchy of cancer stem and progenitor non-stem cancer cells.

Cancer Biology↗

bioRxiv2015

From concept to clinic: mathematically informed immunotherapy

Introduction Introduction Mathematical oncology Immunotherapy Modeling tumor-immune... Modeling treatment outcomes Adoptive T-cell and cytokine... Dendritic cell vaccines Preventative vaccines Cancer-specific treatments Combination therapies Discussion Summary References Mathematical modeling has become a valuable tool in the continued effort to understand, predict and ultimately treat a wide range of cancers in recent years [1,2]. By describing biological phenomena in the concise and formal language of mathematics, it is possible to elucidate key components of complex systems and ultimately develop tools capable of quantifying and ...

Cancer Biology↗

bioRxiv2015

Cancer stem cell plasticity as tumor growth promoter and catalyst of population collapse

It is increasingly argued that cancer stem cells are not a cellular phenotype but rather a transient state that cells can acquire, either through intrinsic signaling cascades or in response to environmental cues. While cancer stem cell plasticity is generally associated with increased aggressiveness and treatment resistance, we set out to thoroughly investigate the impact of different rates of plasticity on early and late tumor growth dynamics and the response to therapy. We develop an agent-based model of cancer stem cell driven tumor growth, in which plasticity is defined as a spontaneous transition between stem and non-stem cancer cell states. Simulations of the model show that plasticity can substantially increase tumor growth rate and invasion. At high rates of plasticity, however, the cells get exhausted and the tumor will undergo spontaneous remission in the long term. In a series of in silico trials we show that such remission can be facilitated through radiotherapy. The presented study suggests that stem cell plasticity has rather complex, non-intuitive implications on tumor growth and treatment response. Further theoretical, experimental and integrated studies are needed to fully decipher cancer stem cell plasticity and how it can be harnessed for novel therapeutic approaches.

Cancer Biology↗