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	<title>bioRxiv Channel: Mathematical Oncology</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "Mathematical Oncology"
	</description>

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	<item rdf:about="https://biorxiv.org/cgi/content/short/028746v1?rss=1">
<title>
<![CDATA[
Cell-cell interactions and evolution using evolutionary game theory 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/028746v1?rss=1"
</link>
<description><![CDATA[
Cancers arise from genetic abberations but also consistently display high levels of intra-tumor heterogeneity and evolve according to Darwinian dynamics. This makes evolutionary game theory an ideal tool in which to mathematically capture these cell-cell interactions and in which to investigate how they impact evolutionary dynamics. In this chapter we present some examples of how evolutionary game theory can elucidate cancer evolution.
]]></description>
<dc:creator>David Basanta</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-10-09</dc:date>
<dc:identifier>doi:10.1101/028746</dc:identifier>
<dc:title><![CDATA[Cell-cell interactions and evolution using evolutionary game theory]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-10-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/035709v1?rss=1">
<title>
<![CDATA[
Simulating multi-substrate diffusive transport in 3-D tissues with BioFVM 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/035709v1?rss=1"
</link>
<description><![CDATA[
To simulate the spatiotemporal distribution of chemical compounds, we present BioFVM, an open-source reaction-diffusion equation solver using finite volume methods with motivation for biological applications. With various numerical solvers, we can simulate the interaction of dozens of compounds, including growth substrates, drugs, and signaling compounds in 3-D tissues, with cells by treating them as various source/sink terms. BioFVM has linear computational cost scalings and demonstrates first-order accuracy in time and second-order accuracy in space. Beyond simulating the transport of drugs and growth substrates in tissues, the ability to simulate dozens of compounds should make 3-D simulations of multicellular secretomics feasible.
]]></description>
<dc:creator>Samuel H Friedman</dc:creator>
<dc:creator>Ahmadreza Ghaffarizadeh</dc:creator>
<dc:creator>Paul Macklin</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-12-30</dc:date>
<dc:identifier>doi:10.1101/035709</dc:identifier>
<dc:title><![CDATA[Simulating multi-substrate diffusive transport in 3-D tissues with BioFVM]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-12-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/035733v1?rss=1">
<title>
<![CDATA[
Agent-based simulation of large tumors in 3-D microenvironments 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/035733v1?rss=1"
</link>
<description><![CDATA[
Multicellular simulations of tumor growth in complex 3-D tissues, where data come from high content in vitro and bioengineered experiments, have gained significant attention by the cancer modeling community in recent years. Agent-based models are often selected for these problems because they can directly model and track cells states and their interactions with the microenvironment. We describe PhysiCell, a specific agent-based model that includes cell motion, cell cycling, and cell volume changes. The model has been performance tested on systems of 105 cells on desktop computers, and is expected to scale to 106 or more cells on single super-computer compute nodes. We plan an open source release of the software in early 2016 at PhysiCell.MathCancer.org
]]></description>
<dc:creator>Ahmadreza Ghaffarizadeh</dc:creator>
<dc:creator>Samuel H Friedman</dc:creator>
<dc:creator>Paul Macklin</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-12-30</dc:date>
<dc:identifier>doi:10.1101/035733</dc:identifier>
<dc:title><![CDATA[Agent-based simulation of large tumors in 3-D microenvironments]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-12-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/036467v1?rss=1">
<title>
<![CDATA[
The role of contact inhibition in intratumoral heterogeneity: An off-lattice individual based model 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/036467v1?rss=1"
</link>
<description><![CDATA[
We present a model that shows how intratumoral heterogeneity, in terms of tumor cell phenotypic traits, can evolve in a tumor mass as a result of selection when space is a limited resource. This model specifically looks at the traits of proliferation rate and migration speed. The competition for space amongst individuals in the tumor mass creates a selection pressure for the cells with the fittest traits. To allow for organic movement and capture the invasive behavior, we use an off-lattice individual-based model.
]]></description>
<dc:creator>Jill Gallaher</dc:creator>
<dc:creator>Alexander Anderson</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-01-11</dc:date>
<dc:identifier>doi:10.1101/036467</dc:identifier>
<dc:title><![CDATA[The role of contact inhibition in intratumoral heterogeneity: An off-lattice individual based model]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-01-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/035766v1?rss=1">
<title>
<![CDATA[
Estimating cell cycle model parameters using systems identification 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/035766v1?rss=1"
</link>
<description><![CDATA[
A current challenge in data-driven mathematical modeling of cancer is identifying biologically-relevant parameters of mathematical models from sparse and often noisy experimental data of mixed types. We describe a cell cycle model and outline how to use the Optimization Toolbox in Matlab to estimate its timescale parameters, given flow cytometry and cell viability (synthetic) data, and illustrate the technique with simulated data. This technique can be similarly applied to a variety of cell cycle models, particularly as more laboratories begin to use high-content, quantitative cell screening and imaging platforms. An advanced version of this work (CellPD: cell line phenotype digitizer) will be released as open source in early 2016 at MultiCellDS.org.
]]></description>
<dc:creator>Edwin Francisco Juarez Rosales</dc:creator>
<dc:creator>Ahmadreza Ghaffarizadeh</dc:creator>
<dc:creator>Samuel H Friedman</dc:creator>
<dc:creator>Edmond Jonckheere</dc:creator>
<dc:creator>Paul Macklin</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-12-31</dc:date>
<dc:identifier>doi:10.1101/035766</dc:identifier>
<dc:title><![CDATA[Estimating cell cycle model parameters using systems identification]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-12-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/035022v1?rss=1">
<title>
<![CDATA[
Hybrid Systems Modeling for (Cancer) Systems Biology 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/035022v1?rss=1"
</link>
<description><![CDATA[
The advent of biological data of increasingly higher resolution in space and time has triggered the use of dynamic models to explain and predict the evolution of biological systems over space and time. Computer-aided system modeling and analysis in biology has led to many new discoveries and explanations that would otherwise be intractable to articulate without the available data and computing power. Nevertheless, the complexity in biology still challenges many labs in capturing studied phenomena in models that are tractable and simple enough to analyze. Moreover, the popular use of ordinary differential equation models have their limitations in that they solely capture continuous dynamics, while we observe many discrete dynamic phenomena in biology such as gene switching or mutations. Hybrid systems modeling provides a framework in which both continuous and discrete dynamics can be simulated and analyzed. Moreover, it provides techniques to develop approximations and abstractions of complex dynamics that are tractable to analyze.
]]></description>
<dc:creator>Roel I.J. Dobbe</dc:creator>
<dc:creator>Claire J. Tomlin</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-12-23</dc:date>
<dc:identifier>doi:10.1101/035022</dc:identifier>
<dc:title><![CDATA[Hybrid Systems Modeling for (Cancer) Systems Biology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-12-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/031252v1?rss=1">
<title>
<![CDATA[
Physics-based multiscale mass transport model in drug delivery and tumor microenvironment 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031252v1?rss=1"
</link>
<description><![CDATA[
We describe multiscale transport model, which was developed to simulate drug diffusion and convection in tissues and drug vectors. Models rely on material properties and physical laws of transport. Our methods show that drug transport analysis may provide deep insight into mechanisms of pharmacokinetics useful in nanotherapeutics and transport study within tumor microenvironment. Because the method relies on material properties and structures, the approach can help studying phenotypical differences as well.
]]></description>
<dc:creator>Arturas Ziemys</dc:creator>
<dc:creator>Milos Kojic</dc:creator>
<dc:creator>Mauro Ferrari</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-10</dc:date>
<dc:identifier>doi:10.1101/031252</dc:identifier>
<dc:title><![CDATA[Physics-based multiscale mass transport model in drug delivery and tumor microenvironment]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/031070v1?rss=1">
<title>
<![CDATA[
Agent based models to investigate cooperation between cancer cells 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031070v1?rss=1"
</link>
<description><![CDATA[
We present a type of agent-based model that uses off-lattice spheres to represent individual cells in a solid tumor. The model calculates chemical gradients and determines the dynamics of the tumor as emergent properties of the interactions between the cells. As an example, we present an investigation of cooperation among cancer cells where cooperators secrete a growth factor that is costly to synthesize. Simulations reveal that cooperation is favored when cancer cells from the same lineage stay in close proximity. The result supports the hypothesis that kin selection, a theory that explains the evolution of cooperation in animals, also applies to cancers.
]]></description>
<dc:creator>Joao Xavier</dc:creator>
<dc:creator>William Chang</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-09</dc:date>
<dc:identifier>doi:10.1101/031070</dc:identifier>
<dc:title><![CDATA[Agent based models to investigate cooperation between cancer cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/033332v1?rss=1">
<title>
<![CDATA[
A Bayesian network approach for modeling mixed features in TCGA ovarian cancer data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/033332v1?rss=1"
</link>
<description><![CDATA[
We propose an integrative framework to select important genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a logistic Bayesian network model based on The Cancer Genome Atlas data. The constructed Bayesian network has identified four gene clusters of distinct cellular functions, 13 driver genes, as well as some new biological pathways which may shed new light into the molecular mechanisms of ovarian cancer.
]]></description>
<dc:creator>Qingyang Zhang</dc:creator>
<dc:creator>Ji-Ping Wang</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-30</dc:date>
<dc:identifier>doi:10.1101/033332</dc:identifier>
<dc:title><![CDATA[A Bayesian network approach for modeling mixed features in TCGA ovarian cancer data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/031237v1?rss=1">
<title>
<![CDATA[
Stochastic Models for Population Dynamics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031237v1?rss=1"
</link>
<description><![CDATA[
Cell growth and division are stochastic processes that exhibit significant amount of cell-to-cell variation and randomness. In order to connect single cell division dynamics with overall cell population, stochastic population models are needed. We summarize the basic concepts, computational approaches and discuss simple applications of this modeling approach to understanding cancer cell population growth as well as population fluctuations in experiments.
]]></description>
<dc:creator>Sean Sun</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-12-03</dc:date>
<dc:identifier>doi:10.1101/031237</dc:identifier>
<dc:title><![CDATA[Stochastic Models for Population Dynamics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-12-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/031294v1?rss=1">
<title>
<![CDATA[
Micro-Pharmacodynamics: Bridging In Vitro and In Vivo Experimental Scales in Testing Drug Efficacy and Resistance 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031294v1?rss=1"
</link>
<description><![CDATA[
Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue that can impede the transport of therapeutic agents to tumor cells in sufficient quantities. As a result, drugs that work well in vitro often fail at clinical trials when confronted with the complexities of interstitial transport within the tumor microenvironment. The microPD model that we developed is used to investigate the penetration of drug molecules through the tumor tissue and influenced by the physical and metabolic properties of tumor microenvironment, and how it affects drug efficacy and the emergence of drug resistance.
]]></description>
<dc:creator>Katarzyna A Rejniak</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-11</dc:date>
<dc:identifier>doi:10.1101/031294</dc:identifier>
<dc:title><![CDATA[Micro-Pharmacodynamics: Bridging In Vitro and In Vivo Experimental Scales in Testing Drug Efficacy and Resistance]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/032086v1?rss=1">
<title>
<![CDATA[
Vectorization techniques for efficient agent-based model simulations of tumor growth 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/032086v1?rss=1"
</link>
<description><![CDATA[
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.
]]></description>
<dc:creator>Jan Poleszczuk</dc:creator>
<dc:creator>Heiko Enderling</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-17</dc:date>
<dc:identifier>doi:10.1101/032086</dc:identifier>
<dc:title><![CDATA[Vectorization techniques for efficient agent-based model simulations of tumor growth]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/032102v1?rss=1">
<title>
<![CDATA[
Integrating experimental data to calibrate quantitative cancer models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/032102v1?rss=1"
</link>
<description><![CDATA[
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.
]]></description>
<dc:creator>Heiko Enderling</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-17</dc:date>
<dc:identifier>doi:10.1101/032102</dc:identifier>
<dc:title><![CDATA[Integrating experimental data to calibrate quantitative cancer models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/031534v1?rss=1">
<title>
<![CDATA[
Overview: Modeling Heterogeneous Tumor Tissue as a Multiphase Material 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031534v1?rss=1"
</link>
<description><![CDATA[
Tumors are typically heterogeneous tissues comprised of multiple cell species in addition to extra-cellular matrix (ECM) and water fluid. It is difficult to model these components at the tissue (10-3-10-2m) scale, where individual cells cannot be represented without prohibitive computational burden. Assuming that same-kind components tend to cluster together, a multiphase approach can be applied to represent heterogeneous tumor tissue at this larger physical scale. This method enables simulating mixture of elements within tissues, e.g., geno-/phenotypic heterogeneity underlying mutation- or microenvironment-driven tumor progression. Further, by not explicitly tracking interfaces, this methodology facilitates realistic modeling of tissue in 3-D.
]]></description>
<dc:creator>Hermann B Frieboes</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-12</dc:date>
<dc:identifier>doi:10.1101/031534</dc:identifier>
<dc:title><![CDATA[Overview: Modeling Heterogeneous Tumor Tissue as a Multiphase Material]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/031245v1?rss=1">
<title>
<![CDATA[
Modeling Transitions between Responsive and Resistant States in Breast Cancer with Application to Therapy Optimization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031245v1?rss=1"
</link>
<description><![CDATA[
We present a mathematical model that captures the transitions among three experimentally observed estrogen-sensitivity phenotypes in breast cancer cells. Based on this model, a population-level model is created and used to explore the optimization of a therapeutic protocol
]]></description>
<dc:creator>Chun Chen</dc:creator>
<dc:creator>John J. Tyson</dc:creator>
<dc:creator>William Baumann</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-10</dc:date>
<dc:identifier>doi:10.1101/031245</dc:identifier>
<dc:title><![CDATA[Modeling Transitions between Responsive and Resistant States in Breast Cancer with Application to Therapy Optimization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/037424v1?rss=1">
<title>
<![CDATA[
Precision Medicine in the Clinic: Personalizing a Model of Glioblastoma Through Parameterization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/037424v1?rss=1"
</link>
<description><![CDATA[
In this article, we present an example of how to parameterize a partial differential equation model of glioblastoma growth for individual patients. The parameters allow for a deeper understanding of individual patients which have all been labeled with the same disease based on the tumor growth kinetics.
]]></description>
<dc:creator>Andrea Hawkins-Daarud</dc:creator>
<dc:creator>Kristin R. Swanson</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-01-22</dc:date>
<dc:identifier>doi:10.1101/037424</dc:identifier>
<dc:title><![CDATA[Precision Medicine in the Clinic: Personalizing a Model of Glioblastoma Through Parameterization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-01-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/038273v1?rss=1">
<title>
<![CDATA[
Non-linear tumor-immune interactions arising from spatial metabolic heterogeneity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/038273v1?rss=1"
</link>
<description><![CDATA[
A hybrid multiscale mathematical model of tumor growth is used to investigate how tumoral and microenvironmental heterogeneity affect the response of the immune system. The model includes vascular dynamics and evolution of metabolic tumor phenotypes. Cytotoxic T cells are simulated, and their effect on tumor growth is shown to be dependent on the structure of the microenvironment and the distribution of tumor phenotypes. Importantly, no single immune strategy is best at all stages of tumor growth.
]]></description>
<dc:creator>Mark Robertson-Tessi</dc:creator>
<dc:creator>Robert J Gillies</dc:creator>
<dc:creator>Robert A Gatenby</dc:creator>
<dc:creator>Alexander RA Anderson</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-01-29</dc:date>
<dc:identifier>doi:10.1101/038273</dc:identifier>
<dc:title><![CDATA[Non-linear tumor-immune interactions arising from spatial metabolic heterogeneity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-01-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/041855v1?rss=1">
<title>
<![CDATA[
Dissecting resistance mechanisms in melanoma combination therapy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/041855v1?rss=1"
</link>
<description><![CDATA[
We present a compartment model that explains melanoma cell response and resistance to mono and combination therapies. Model parameters were estimated by utilizing an optimization algorithm to identify parameters that minimized the difference between predicted cell populations and experimentally measured cell numbers. The model was then validated with in vitro experimental data. Our simulations show that although a specific timing of the combination therapy is effective in controlling tumor cell populations over an extended period of time, the treatment eventually fails. We subsequently predict a more optimal combination therapy that incorporates an additional drug at the right moment.
]]></description>
<dc:creator>Eunjung Kim</dc:creator>
<dc:creator>Alexander Anderson</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-02-29</dc:date>
<dc:identifier>doi:10.1101/041855</dc:identifier>
<dc:title><![CDATA[Dissecting resistance mechanisms in melanoma combination therapy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-02-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/038471v1?rss=1">
<title>
<![CDATA[
Reaction-Diffusion Model of PDGF-driven Recruitment in Experimental Glioblastoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/038471v1?rss=1"
</link>
<description><![CDATA[
Platelet-derived growth factor (PDGF) drives the formation of gliomas in an experimental animal model, which notably involves the recruitment of large numbers of glial progenitor cells. (Assanah 2006). In order to understand the underlying mechanism, particularly what factors influence the degree of recruitment, and how varied amounts of PDGF would affect the gross characteristics and overall appearance of tumors in the brain, we adapted a reaction diffusion model of glioma, which has been used for analyzing clinical data, to model the interactions at play in these experimental models.
]]></description>
<dc:creator>Susan Christine Massey</dc:creator>
<dc:creator>Kristin Swanson</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-02-04</dc:date>
<dc:identifier>doi:10.1101/038471</dc:identifier>
<dc:title><![CDATA[Reaction-Diffusion Model of PDGF-driven Recruitment in Experimental Glioblastoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-02-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/043620v1?rss=1">
<title>
<![CDATA[
Hybrid Discrete-Continuum Cellular Automaton (HCA) model of Prostate to Bone Metastasis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/043620v1?rss=1"
</link>
<description><![CDATA[
Prostate to bone metastases induce a "vicious cycle" by promoting excessive osteoclast and osteoblast mediated bone degradation and formation that in turn yields factors that drive cancer growth. Recent advances defining the molecular mechanisms that control the vicious cycle have revealed new therapeutic targeting opportunities. However, given the complex temporal and simultaneous cellular interactions occurring in the bone microenvironment, assessing the impact of putative therapies is challenging. To this end, we have integrated biological and computational approaches to generate an accurate model of normal bone matrix homeostasis and the prostate cancer-bone microenvironment. The model faithfully reproduces the basic multicellular unit (BMU) bone coupling process and introduction of a single prostate cancer cell yields a vicious cycle that is similar in cellular composition and pathophysiology to models of prostate to bone metastasis.
]]></description>
<dc:creator>Arturo Araujo</dc:creator>
<dc:creator>David Bastanta</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-03-15</dc:date>
<dc:identifier>doi:10.1101/043620</dc:identifier>
<dc:title><![CDATA[Hybrid Discrete-Continuum Cellular Automaton (HCA) model of Prostate to Bone Metastasis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-03-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/045021v1?rss=1">
<title>
<![CDATA[
Mining Large Heterogeneous Cancer Data Sets Using Boolean Implications 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/045021v1?rss=1"
</link>
<description><![CDATA[
Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we describe their usage in mining associations from large, heterogeneous cancer data sets. Next, we illustrate how Boolean implications were used to discover a new causal association between a mutation and aberrant DNA hypermethylation in acute myeloid leukemia as well as the therapeutic implications of this discovery. We conclude with a brief description of how Boolean implications can be extracted from a given data set.
]]></description>
<dc:creator>Subarna Sinha</dc:creator>
<dc:creator>David L Dill</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-03-21</dc:date>
<dc:identifier>doi:10.1101/045021</dc:identifier>
<dc:title><![CDATA[Mining Large Heterogeneous Cancer Data Sets Using Boolean Implications]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-03-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/056341v1?rss=1">
<title>
<![CDATA[
Game theoretic methods in population dynamics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/056341v1?rss=1"
</link>
<description><![CDATA[
Game theoretic methods are used to model the dynamics of cellular populations (tissues) in which it is necessary to account for changes in cellular phenotype (usually proliferation) resulting from cell-cell interaction. Results include prediction of long-term steady-state "equilibria" and transient dynamics. These results can be useful for predicting relapse after cytoreduction, assessing the efficacy of alternating combination therapy, and interpreting biopsy specimens obtained from spatially heterogeneous tissues. Mathematical tools range from simple systems of differential equations to computational techniques (individual-based models).
]]></description>
<dc:creator>David Liao</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-05-31</dc:date>
<dc:identifier>doi:10.1101/056341</dc:identifier>
<dc:title><![CDATA[Game theoretic methods in population dynamics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/049874v1?rss=1">
<title>
<![CDATA[
Using Ordinary Differential Equations to Explore Cancer-Immune Dynamics and Tumor Dormancy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/049874v1?rss=1"
</link>
<description><![CDATA[
Cancer is not solely a disease of the genome, but is a systemic disease that affects the host on many functional levels, including, and perhaps most notably, the function of the immune response, resulting in both tumor-promoting inflammation and tumor-inhibiting cytotoxic action. The dichotomous actions of the immune response induce significant variations in tumor growth dynamics that mathematical modeling can help to understand. Here we present a general method using ordinary differential equations (ODEs) to model and analyze cancer-immune interactions, and in particular, immune-induced tumor dormancy.
]]></description>
<dc:creator>Kathleen Wilkie</dc:creator>
<dc:creator>Philip Hahnfeldt</dc:creator>
<dc:creator>Lynn Hlatky</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-04-22</dc:date>
<dc:identifier>doi:10.1101/049874</dc:identifier>
<dc:title><![CDATA[Using Ordinary Differential Equations to Explore Cancer-Immune Dynamics and Tumor Dormancy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-04-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/048579v1?rss=1">
<title>
<![CDATA[
Long Range Force Transmission in Fibrous Matrices Enabled by Tension-Driven Alignment of Fibers 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/048579v1?rss=1"
</link>
<description><![CDATA[
Cells can sense and respond to mechanical signals over relatively long distances across fibrous extracellular matrices. Recently proposed models suggest that long-range force transmission can be attributed to the nonlinear elasticity or fibrous nature of collagen matrices, yet the mechanism whereby fibers align remains unknown. Moreover, cell shape and anisotropy of cellular contraction are not considered in existing models, although recent experiments have shown that they play crucial roles. Here, we explore all of the key factors that influence long-range force transmission in cell-populated collagen matrices: alignment of collagen fibers, responses to applied force, strain stiffening properties of the aligned fibers, aspect ratios of the cells, and the polarization of cellular contraction. A constitutive law accounting for mechanically-driven collagen fiber reorientation is proposed. We systematically investigate the range of collagen fiber alignment using both finite element simulations and analytical calculations. Our results show that tension-driven collagen fiber alignment plays a crucial role in force transmission. Small critical stretch for fiber alignment, large fiber stiffness and fiber strain-hardening behavior enable long-range interaction. Furthermore, the range of collagen fiber alignment for elliptical cells with polarized contraction is much larger than that for spherical cells with diagonal contraction. A phase diagram showing the range of force transmission as a function of cell shape and polarization and matrix properties is presented. Our results are in good agreement with recent experiments, and highlight the factors that influence long-range force transmission, in particular tension-driven alignment of fibers. Our work has important relevance to biological processes including development, cancer metastasis and wound healing, suggesting conditions whereby cells communicate over long distances.
]]></description>
<dc:creator>Hailong Wang</dc:creator>
<dc:creator>Abhilash AS</dc:creator>
<dc:creator>Christopher S Chen</dc:creator>
<dc:creator>Rebecca G Wells</dc:creator>
<dc:creator>Vivek B Shenoy</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-04-13</dc:date>
<dc:identifier>doi:10.1101/048579</dc:identifier>
<dc:title><![CDATA[Long Range Force Transmission in Fibrous Matrices Enabled by Tension-Driven Alignment of Fibers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-04-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/033977v1?rss=1">
<title>
<![CDATA[
Multiscale Modeling of Cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/033977v1?rss=1"
</link>
<description><![CDATA[
Breast cancer remains the second leading cause of cancer death in women, exceeded only by lung cancer. Specifically, triple-negative breast cancer (TNBC) has the worst prognosis, as it is more invasive and lacks estrogen, progesterone, and HER2 receptors that can be targeted with therapies. Due to the need for effective therapies for this type of breast cancer, it is critical to develop methods to (1) understand how TNBC progresses and (2) facilitate development of effective therapies. Here, we describe a multiscale model focusing on tumor formation. Our approach uses multiple scales to investigate the progression and possible treatments of tumors.
]]></description>
<dc:creator>Kerri-Ann Norton</dc:creator>
<dc:creator>Meghan M McCabe Pryor</dc:creator>
<dc:creator>Aleksander S Popel</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-12-08</dc:date>
<dc:identifier>doi:10.1101/033977</dc:identifier>
<dc:title><![CDATA[Multiscale Modeling of Cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-12-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/049015v1?rss=1">
<title>
<![CDATA[
Network-based Computational Drug Combination Prediction 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/049015v1?rss=1"
</link>
<description><![CDATA[
Cancers are complex diseases that are regulated by multiple signaling pathways. Patients often acquire resistance to single drug treatment. Use of drug combinations that target multiple parallel pathways is a promising strategy to reduce the drug resistance. Pharmacogenomics big data are being generated to uncover complex signaling mechanisms of cancers and correlate cancer-specific signaling with diverse drug responses. Thus, converting pharmacogenomics big data into knowledge can help the discovery of synergistic drug combination. However, it is challenging and remains an open problem due to the enormous number of combination possibilities and noise of genomics data.
]]></description>
<dc:creator>Fuhai Li</dc:creator>
<dc:creator>Lei Huang</dc:creator>
<dc:creator>Jianting Sheng</dc:creator>
<dc:creator>Stephen TC Wong</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-04-16</dc:date>
<dc:identifier>doi:10.1101/049015</dc:identifier>
<dc:title><![CDATA[Network-based Computational Drug Combination Prediction]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-04-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/046664v1?rss=1">
<title>
<![CDATA[
Automated population identification and sorting algorithms for high-dimensional single-cell data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/046664v1?rss=1"
</link>
<description><![CDATA[
Cell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.
]]></description>
<dc:creator>Benedict Anchang</dc:creator>
<dc:creator>Sylvia Plevritis</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-03-31</dc:date>
<dc:identifier>doi:10.1101/046664</dc:identifier>
<dc:title><![CDATA[Automated population identification and sorting algorithms for high-dimensional single-cell data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-03-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/070326v1?rss=1">
<title>
<![CDATA[
Mathematical Methods for Modeling Chemical Reaction Networks 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/070326v1?rss=1"
</link>
<description><![CDATA[
Cancers cellular behavior is driven by alterations in the processes that cells use to sense and respond to diverse stimuli. Underlying these processes are a series of chemical processes (enzyme-substrate, protein-protein, etc.). Here we introduce a set of mathematical techniques for describing and characterizing these processes.
]]></description>
<dc:creator>Justin Carden</dc:creator>
<dc:creator>Casian Pantea</dc:creator>
<dc:creator>Gheorge Craciun</dc:creator>
<dc:creator>Raghu Machiraju</dc:creator>
<dc:creator>Parag Mallick</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-18</dc:date>
<dc:identifier>doi:10.1101/070326</dc:identifier>
<dc:title><![CDATA[Mathematical Methods for Modeling Chemical Reaction Networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/071134v1?rss=1">
<title>
<![CDATA[
Ordinary Differential Equations in Cancer Biology 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/071134v1?rss=1"
</link>
<description><![CDATA[
Ordinary differential equations (ODEs) provide a classical framework to model the dynamics of biological systems, given temporal experimental data. Qualitative analysis of the ODE model can lead to further biological insight and deeper understanding compared to traditional experiments alone. Simulation of the model under various perturbations can generate novel hypotheses and motivate the design of new experiments. This short paper will provide an overview of the ODE modeling framework, and present examples of how ODEs can be used to address problems in cancer biology.
]]></description>
<dc:creator>Margaret P Chapman</dc:creator>
<dc:creator>Claire J. Tomlin</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-23</dc:date>
<dc:identifier>doi:10.1101/071134</dc:identifier>
<dc:title><![CDATA[Ordinary Differential Equations in Cancer Biology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/071217v1?rss=1">
<title>
<![CDATA[
Network Identification Methods 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/071217v1?rss=1"
</link>
<description><![CDATA[
Recently, network inference algorithms have grown tremendously in the field of systems biology because network identification is essential for understanding relationships between regulation mechanisms for genes, elucidating functional mechanisms underlying cellular processes, as well as identifying molecular targets for discoveries in medicines. This article provides a brief overview of different approaches used to identify biological networks and reviews recent advances in network identification.
]]></description>
<dc:creator>Young Hwan Chang</dc:creator>
<dc:creator>Claire Tomlin</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-23</dc:date>
<dc:identifier>doi:10.1101/071217</dc:identifier>
<dc:title><![CDATA[Network Identification Methods]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/244319v1?rss=1">
<title>
<![CDATA[
Open source tools and standardized data in cancer systems biology 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/244319v1?rss=1"
</link>
<description><![CDATA[
To reach the full potential of multicellular systems biology, mathematical and computational modelers must pool their efforts to share and curate biophysical measurements, create and combine mathematical models, analyze and visualize model predictions, and validate and refine against shared data. An ecosystem of open source software that reads standardized data is essential. We review the state-of-the-art in open source software and data standards in multicellular systems biology, and point out areas of needed growth to move beyond isolated models to community-driven frameworks that shed light on complex problems in multicellular systems biology.
]]></description>
<dc:creator>Macklin, P.</dc:creator>
<dc:creator>Friedman, S. H.</dc:creator>
<dc:creator>MultiCellDS Project,</dc:creator>
<dc:date>2018-01-07</dc:date>
<dc:identifier>doi:10.1101/244319</dc:identifier>
<dc:title><![CDATA[Open source tools and standardized data in cancer systems biology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/236653v1?rss=1">
<title>
<![CDATA[
Assessment of patient-specific efficacy of chemo- and targeted-therapies: a micropharmacology approach 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/236653v1?rss=1"
</link>
<description><![CDATA[
Both targeted and standard chemotherapy drugs are subject to various intratumoral barriers that impede their effectiveness. The tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, and nonuniform expression of cell membrane receptors hinder drug molecule transport and perturb its cellular uptake. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine these aspects of drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microPKPD models and coupled them with patient-specific data to test personalized treatments.
]]></description>
<dc:creator>Karolak, A. A.</dc:creator>
<dc:creator>Huffstutler, B.</dc:creator>
<dc:creator>Khan, Z.</dc:creator>
<dc:creator>Rejniak, K. A.</dc:creator>
<dc:date>2017-12-20</dc:date>
<dc:identifier>doi:10.1101/236653</dc:identifier>
<dc:title><![CDATA[Assessment of patient-specific efficacy of chemo- and targeted-therapies: a micropharmacology approach]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/175661v1?rss=1">
<title>
<![CDATA[
Hybrid approach for parameter estimation in agent-based models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/175661v1?rss=1"
</link>
<description><![CDATA[
Agent-based models are valuable in cancer research to show how different behaviors emerge from individual interactions between cells and their environment. However, calibrating such models can be difficult, especially if the parameters that govern the underlying interactions are hard to measure experimentally. Herein, we detail a new method to converge on parameter sets that fit an agent-based model to multiscale data using a model of glioblastoma as an example.
]]></description>
<dc:creator>Gallaher, J.</dc:creator>
<dc:creator>Hawkins-Daarud, A.</dc:creator>
<dc:creator>Massey, S. C.</dc:creator>
<dc:creator>Swanson, K.</dc:creator>
<dc:creator>Anderson, A.</dc:creator>
<dc:date>2017-08-13</dc:date>
<dc:identifier>doi:10.1101/175661</dc:identifier>
<dc:title><![CDATA[Hybrid approach for parameter estimation in agent-based models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/209056v1?rss=1">
<title>
<![CDATA[
Days Gained: A Simulation-Based, Response Metric in the Assessment of Glioblastoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/209056v1?rss=1"
</link>
<description><![CDATA[
We show the application of a minimally based, patient-specific mathematical model in the evaluation of glioblastoma response to therapy. Days Gained uses computational models of glioblastoma growth dynamics derived from clinically acquired magnetic resonance imaging (MRI) to compare the post-treatment tumor lesion to the expected untreated tumor lesion at the same time point. It accounts for the inter-patient variability in growth dynamics and response to therapy. This allows for the accurate assessment of therapeutic response and provides insight into overall survival as it relates to treatment response.
]]></description>
<dc:creator>De Leon, G.</dc:creator>
<dc:creator>Singleton, K. W.</dc:creator>
<dc:creator>Swanson, K. R.</dc:creator>
<dc:date>2018-01-07</dc:date>
<dc:identifier>doi:10.1101/209056</dc:identifier>
<dc:title><![CDATA[Days Gained: A Simulation-Based, Response Metric in the Assessment of Glioblastoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/263335v1?rss=1">
<title>
<![CDATA[
Competitive release in tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/263335v1?rss=1"
</link>
<description><![CDATA[
Competitive release is a bedrock principle of coevolutionary ecology and population dynamics. It is also the main mechanism by which heterogeneous tumors develop chemotherapeutic resistance. Understanding, controlling, and exploiting this important mechanism represents one of the key challenges and potential opportunities of current medical oncology. The development of sophisticated mathematical and computational models of coevolution among clonal and sub-clonal cell populations in the tumor ecosystem can guide us in predicting and shaping various responses to perturbations in the fitness landscape which is altered by chemo-toxic agents. This in turn can help us design adaptive chemotherapeutic strategies to combat the release resistant cells.
]]></description>
<dc:creator>Ma, Y.</dc:creator>
<dc:creator>West, J.</dc:creator>
<dc:creator>Newton, P. K.</dc:creator>
<dc:date>2018-02-10</dc:date>
<dc:identifier>doi:10.1101/263335</dc:identifier>
<dc:title><![CDATA[Competitive release in tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/263327v1?rss=1">
<title>
<![CDATA[
Optimizing chemo-scheduling based on tumor growth rates 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/263327v1?rss=1"
</link>
<description><![CDATA[
We review the classic tumor growth and regression laws of Skipper and Schable based on fixed exponential growth assumptions, and Norton and Simons law based on a Gompertzian growth assumption. We then discuss ways to optimize chemotherapeutic scheduling using a Moran process evolutionary game-theory model of tumor growth that incorporates more general dynamical and evolutionary features of tumor cell kinetics. Using this model, and employing the quantitative notion of Shannon entropy which assigns high values to low-dose metronomic (LDM) therapies, and low values to maximum tolerated dose (MTD) therapies, we show that low-dose metronomic strategies can outperform maximum tolerated dose strategies, particularly for faster growing tumors. The general concept of designing different chemotherapeutic strategies for tumors with different growth characteristics is discussed.
]]></description>
<dc:creator>West, J.</dc:creator>
<dc:creator>Newton, P. K.</dc:creator>
<dc:date>2018-02-10</dc:date>
<dc:identifier>doi:10.1101/263327</dc:identifier>
<dc:title><![CDATA[Optimizing chemo-scheduling based on tumor growth rates]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/263350v1?rss=1">
<title>
<![CDATA[
Markov chain models of cancer metastasis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/263350v1?rss=1"
</link>
<description><![CDATA[
Abstract.We describe the use of Markov chain models for the purpose of quantitative forecasting of metastatic cancer progression. Each site (node) in the Markov network (directed graph) is an organ site where a secondary tumor could develop with some probability. The Markov matrix is an N x N matrix where each entry represents a transition probability of the disease progressing from one site to another during the course of the disease. The initial state-vector has a 1 at the position corresponding to the primary tumor, and 0s elsewhere (no initial metastases). The spread of the disease to other sites (metastases) is modeled as a directed random walk on the Markov network, moving from site to site with the estimated transition probabilities obtained from longitudinal data. The stochastic model produces probabilistic predictions of the likelihood of each metastatic pathway and corresponding time sequences obtained from computer Monte Carlo simulations. The main challenge is to empirically estimate the N^2 transition probabilities in the Markov matrix using appropriate longitudinal data.
]]></description>
<dc:creator>Mason, J.</dc:creator>
<dc:creator>Newton, P. K.</dc:creator>
<dc:date>2018-02-13</dc:date>
<dc:identifier>doi:10.1101/263350</dc:identifier>
<dc:title><![CDATA[Markov chain models of cancer metastasis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/265959v1?rss=1">
<title>
<![CDATA[
A new paradigm for personalized cancer screening 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/265959v1?rss=1"
</link>
<description><![CDATA[
A series of distinct histologic lesions precedes the onset of malignancy in many common cancers, yet early detection remains a major challenge. Many patients still experience late (stage IV) diagnoses and thus poor prognosis and limited options for therapeutic intervention. For cancers with known biomarkers of premalignant progression, optimized patient-specific screening protocols would minimize the risk of undetected progression to advanced stage disease. Here, we propose simple, cost-effective mathematical and statistical approaches to forecasting disease progression that could guide the personalization of optimal screening times for high-risk patients.
]]></description>
<dc:creator>Walker, R.</dc:creator>
<dc:creator>Enderling, H.</dc:creator>
<dc:date>2018-02-15</dc:date>
<dc:identifier>doi:10.1101/265959</dc:identifier>
<dc:title><![CDATA[A new paradigm for personalized cancer screening]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/283903v1?rss=1">
<title>
<![CDATA[
Mathematical modelling of molecular heterogeneity identifies novel markers and subpopulations in complex tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/283903v1?rss=1"
</link>
<description><![CDATA[
Intratumor heterogeneity, as both a major confounding factor and an underexploited information source, is widely implicated as a key driver of drug resistance. While a handful of reports have demonstrated the potential of supervised methods to deconvolute intratumor heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we developed convex analysis of mixtures (CAM), a fully unsupervised deconvolution method, for identifying marker genes and subpopulations directly from original mixed molecular expressions.
]]></description>
<dc:creator>Chen, L.</dc:creator>
<dc:creator>Wang, N.</dc:creator>
<dc:creator>Clarke, R.</dc:creator>
<dc:creator>Zhang, Z.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:date>2018-03-17</dc:date>
<dc:identifier>doi:10.1101/283903</dc:identifier>
<dc:title><![CDATA[Mathematical modelling of molecular heterogeneity identifies novel markers and subpopulations in complex tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-03-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/323485v1?rss=1">
<title>
<![CDATA[
Cryptsim: Modeling the evolutionary dynamics of the progression of Barrett’s esophagus to esophageal adenocarcinoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/323485v1?rss=1"
</link>
<description><![CDATA[
To alleviate the over-diagnosis and overtreatment of premalignant conditions we need to predict their progression to cancer, and therefore, the dynamics of an evolutionary process. However, monitoring evolutionary processes in vivo is extremely challenging. Computer simulations constitute an attractive alternative, allowing us to study these dynamics based on a set of evolutionary parameters.nnWe introduce CryptSim, a simulator of crypt evolution inspired by Barretts esophagus. We detail the most relevant computational strategies it implements, and perform a simulation study showing that the interaction between neighboring crypts may play a crucial role in carcinogenesis.
]]></description>
<dc:creator>Mallo, D.</dc:creator>
<dc:creator>Kostadinov, R.</dc:creator>
<dc:creator>Cisneros, L.</dc:creator>
<dc:creator>Kuhner, M. K.</dc:creator>
<dc:creator>Maley, C. C.</dc:creator>
<dc:date>2018-05-16</dc:date>
<dc:identifier>doi:10.1101/323485</dc:identifier>
<dc:title><![CDATA[Cryptsim: Modeling the evolutionary dynamics of the progression of Barrett’s esophagus to esophageal adenocarcinoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/324038v1?rss=1">
<title>
<![CDATA[
Using neural networks to bridge scales in cancer: Mapping signaling pathways to phenotypes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/324038v1?rss=1"
</link>
<description><![CDATA[
Cancer is an evolving system subject to mutation and selection. Selection is driven by the microenvironment that the cancer cells are growing in and acts on the cell phenotype, which is in turn modulated by intracellular signaling pathways regulated by the cell genotype. Integrating all of these processes requires bridging different biological scales. We present a mathematical model that uses a neural network as a means to connecting these scales. In particular, we consider the mapping from intracellular pathway activity to phenotype under different microenvironmental conditions.
]]></description>
<dc:creator>Kim, E.</dc:creator>
<dc:creator>Gerlee, P.</dc:creator>
<dc:creator>Anderson, A.</dc:creator>
<dc:date>2018-05-16</dc:date>
<dc:identifier>doi:10.1101/324038</dc:identifier>
<dc:title><![CDATA[Using neural networks to bridge scales in cancer: Mapping signaling pathways to phenotypes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/360800v1?rss=1">
<title>
<![CDATA[
Constructing predictive cancer systems biology models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/360800v1?rss=1"
</link>
<description><![CDATA[
Systems biology combines computational modeling with quantitative experimental measurements to study complex biological processes. Here, we outline an approach for parameterizing and validating a systems biology model to yield predictive tool that can generate testable hypotheses and expand biological understanding.
]]></description>
<dc:creator>Rohrs, J. A.</dc:creator>
<dc:creator>Makaryan, S. Z.</dc:creator>
<dc:creator>Finley, S. D.</dc:creator>
<dc:date>2018-07-04</dc:date>
<dc:identifier>doi:10.1101/360800</dc:identifier>
<dc:title><![CDATA[Constructing predictive cancer systems biology models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-07-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/574848v1?rss=1">
<title>
<![CDATA[
Modeling non-genetic dynamics of cancer cell states measured by single-cell analysis: Uncovering bifurcations that explain why treatment either kills a cancer cell or makes it resistant 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/574848v1?rss=1"
</link>
<description><![CDATA[
Single-cell transcriptomics offers a new vista on non-genetic tumor cell plasticity, a neglected aspect of cancer. The gene expression state of each cell is governed by the gene regulatory network which represents a high-dimensional non-linear dynamical system that generates multiple stable attractor states and undergoes destabilizing bifurcations, manifest as critical transitions. Modeling clonal cell population as statistical ensembles of the same dynamical system, a index IC is derived for detecting destabilization towards critical transitions in single-cell molecular profiles. Therapy-induced bifurcation explains why treatment backfires: a drug-treated cell is imposed the binary choice to either apoptose or become a cancer-stem cell.
]]></description>
<dc:creator>Huang, S.</dc:creator>
<dc:date>2019-03-12</dc:date>
<dc:identifier>doi:10.1101/574848</dc:identifier>
<dc:title><![CDATA[Modeling non-genetic dynamics of cancer cell states measured by single-cell analysis: Uncovering bifurcations that explain why treatment either kills a cancer cell or makes it resistant]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-03-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/629907v1?rss=1">
<title>
<![CDATA[
Hypoxia in cancer chemo- and immunotherapy: foe or friend? 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/629907v1?rss=1"
</link>
<description><![CDATA[
Hypoxia, a low level of oxygen in the tissue, is a feature of most solid tumors. It arises due to an imbalance between the oxygen supply from the abnormal vasculature and oxygen demand by the large number of tumor and stromal cells. Hypoxia has been implicated in the development of aggressive tumors and tumor resistance to various therapies. This makes hypoxia a negative marker of patients survival. However, recent advances in designing new hypoxia-activated pro-drugs and adoptive T cell therapies provide an opportunity for exploiting hypoxia in order to improve cancer treatment. We used novel mathematical models of micro-pharmacology and computational optimization techniques for determining the most effective treatment protocols that take advantage of heterogeneous and dynamically changing oxygenation in in vivo tumors. These models were applied to design schedules for a combination of three therapeutic compounds in pancreatic cancers and determine the most effective adoptive T cell therapy protocols in melanomas.
]]></description>
<dc:creator>Vitos, N.</dc:creator>
<dc:creator>Chen, S.</dc:creator>
<dc:creator>Mathur, S.</dc:creator>
<dc:creator>Chamseddine, I.</dc:creator>
<dc:creator>Rejniak, K. A.</dc:creator>
<dc:date>2019-05-07</dc:date>
<dc:identifier>doi:10.1101/629907</dc:identifier>
<dc:title><![CDATA[Hypoxia in cancer chemo- and immunotherapy: foe or friend?]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/630806v1?rss=1">
<title>
<![CDATA[
Predicting patient-specific radiotherapy responses in head and neck cancer to personalize radiation dose fractionation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/630806v1?rss=1"
</link>
<description><![CDATA[
Human papillomavirus (HPV) related oropharyngeal cancer (OPC) is one of the few types of cancers increasing in incidence. HPV+ OPC treatment with radiotherapy (RT) provides 75-95% five-year locoregional control (LRC). Why some but not all patients with similar clinical stage and molecular profile are controlled remains unknown. We propose the proliferation saturation index, PSI, as a mathematical modeling biomarker of tumor growth and RT response. The model predicts that patients with PSI<0.75 are likely to be cured by radiation, and that hyperfractionated radiation could improve response rates for patients with higher PSI that are predicted to fail standard of care RT. Prospective evaluation is currently ongoing.
]]></description>
<dc:creator>Enderling, H.</dc:creator>
<dc:creator>Susassee, E.</dc:creator>
<dc:creator>Caudell, J.</dc:creator>
<dc:date>2019-05-07</dc:date>
<dc:identifier>doi:10.1101/630806</dc:identifier>
<dc:title><![CDATA[Predicting patient-specific radiotherapy responses in head and neck cancer to personalize radiation dose fractionation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/634428v1?rss=1">
<title>
<![CDATA[
Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/634428v1?rss=1"
</link>
<description><![CDATA[
PurposeFor patients with early-stage breast cancer, prediction of the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (e.g. Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for the time to metastatic relapse.

MethodsThe data consisted of 642 patients with 21 clinicopathological variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter ) and dissemination (parameter ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of 5 covariates with best predictive power. These were further considered to individually predict the model parameters, by using a backward selection approach. Predictive performances were compared to classical Cox regression and machine learning algorithms.

ResultsThe mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with  (p=0.001) and EGFR with  (p=0.009). Achieving a c-index of 0.65 (0.60-0.71), the model had similar predictive performance as the random survival forest (c-index 0.66-0.69) and Cox regression (c-index 0.62 - 0.67), as well as machine learning classification algorithms.

ConclusionBy providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool of help for routine management of breast cancer patients.
]]></description>
<dc:creator>Nicolo, C.</dc:creator>
<dc:creator>Perier, C.</dc:creator>
<dc:creator>Prague, M.</dc:creator>
<dc:creator>MacGrogan, G.</dc:creator>
<dc:creator>Saut, O.</dc:creator>
<dc:creator>Benzekry, S.</dc:creator>
<dc:date>2019-05-10</dc:date>
<dc:identifier>doi:10.1101/634428</dc:identifier>
<dc:title><![CDATA[Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/633933v1?rss=1">
<title>
<![CDATA[
Heterogeneous multi-scale framework for cancer systems models and clinical applications 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/633933v1?rss=1"
</link>
<description><![CDATA[
Clinical Cancer models need to incorporate a wide variety of patient data and tumor heterogeneity which requires integration of multiple models. Due to differences in time and length scales of individual processes, such a model integration is a challenging task. Here we have developed an integrated framework combining ErbB receptor mediated Ras-MAPK and PI3K/AKT pathway with p53 mediated DNA damage response pathway. We have applied this in a clinical setting to predict patient specific response of different treatments in cancers of prostate, lung and kidney.
]]></description>
<dc:creator>Ghosh, A.</dc:creator>
<dc:creator>Radhakrishnan, R.</dc:creator>
<dc:date>2019-05-16</dc:date>
<dc:identifier>doi:10.1101/633933</dc:identifier>
<dc:title><![CDATA[Heterogeneous multi-scale framework for cancer systems models and clinical applications]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/633750v1?rss=1">
<title>
<![CDATA[
Predictive modeling of co-evolving growing populations 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/633750v1?rss=1"
</link>
<description><![CDATA[
How can we best explore and fit density and frequency dependence in the evolutionary and ecological dynamics of growing tumors? Here, we present an introduction to recent developments in our lab, and give two examples of complex interaction-driven tumor growth combined with statistical outcomes of treatment.
]]></description>
<dc:creator>Altrock, P. M.</dc:creator>
<dc:creator>Ferrall-Fairbanks, M. C.</dc:creator>
<dc:creator>Kimmel, G. J.</dc:creator>
<dc:date>2019-05-10</dc:date>
<dc:identifier>doi:10.1101/633750</dc:identifier>
<dc:title><![CDATA[Predictive modeling of co-evolving growing populations]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/650184v1?rss=1">
<title>
<![CDATA[
Quantifying glioblastoma drug penetrance from experimental data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/650184v1?rss=1"
</link>
<description><![CDATA[
Poor clinical trial outcomes for glioblastoma (GBM) can be attributed to multiple possible causes. GBM is heterogeneous, such that there is a chance of treatment-resistant cells coming to predominate the tumor, and due to the blood brain barrier (BBB) it is also possible that therapy was inadequately delivered to the tumor. Mathematically modeling the dynamics of therapeutic response in patient-derived xenografts (PDX) and fitting the mathematical model to bioluminescence imaging flux data, we may be able to assess the degree to which both drug resistance and drug penetrance are driving varied responses to these therapies.
]]></description>
<dc:creator>Massey, S. C.</dc:creator>
<dc:creator>Urcuyo, J.</dc:creator>
<dc:creator>Marin, B.-M.</dc:creator>
<dc:creator>Sarkaria, J.</dc:creator>
<dc:creator>Swanson, K. R.</dc:creator>
<dc:date>2019-05-24</dc:date>
<dc:identifier>doi:10.1101/650184</dc:identifier>
<dc:title><![CDATA[Quantifying glioblastoma drug penetrance from experimental data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/691238v1?rss=1">
<title>
<![CDATA[
Modeling the Role of Hypoxia in Glioblastoma Growth and Recurrence Patterns 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/691238v1?rss=1"
</link>
<description><![CDATA[
A typical feature of glioblastoma (GBM) growth is local recurrence after surgery. However, some GBMs recur distally. It has been noted that GBM patients with perioperative ischemia are more likely to have distal recurrence and that GBM cells migrate faster under hypoxic conditions. We apply the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model to examine the effect of faster hypoxic cell migration on simulated GBM growth. Results suggest that a highly migratory hypoxic cell population drives the growth of the whole tumor and leads to distant recurrence, as do higher normoxic tumor cell migration and low cellular proliferation rates.
]]></description>
<dc:creator>Curtin, L.</dc:creator>
<dc:creator>Hawkins-Daarud, A.</dc:creator>
<dc:creator>Porter, A.</dc:creator>
<dc:creator>Owen, M. R.</dc:creator>
<dc:creator>van der Zee, K.</dc:creator>
<dc:creator>Swanson, K. R.</dc:creator>
<dc:date>2019-07-05</dc:date>
<dc:identifier>doi:10.1101/691238</dc:identifier>
<dc:title><![CDATA[Modeling the Role of Hypoxia in Glioblastoma Growth and Recurrence Patterns]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-07-05</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
