9,969 research outputs found
Systems approaches and algorithms for discovery of combinatorial therapies
Effective therapy of complex diseases requires control of highly non-linear
complex networks that remain incompletely characterized. In particular, drug
intervention can be seen as control of signaling in cellular networks.
Identification of control parameters presents an extreme challenge due to the
combinatorial explosion of control possibilities in combination therapy and to
the incomplete knowledge of the systems biology of cells. In this review paper
we describe the main current and proposed approaches to the design of
combinatorial therapies, including the empirical methods used now by clinicians
and alternative approaches suggested recently by several authors. New
approaches for designing combinations arising from systems biology are
described. We discuss in special detail the design of algorithms that identify
optimal control parameters in cellular networks based on a quantitative
characterization of control landscapes, maximizing utilization of incomplete
knowledge of the state and structure of intracellular networks. The use of new
technology for high-throughput measurements is key to these new approaches to
combination therapy and essential for the characterization of control
landscapes and implementation of the algorithms. Combinatorial optimization in
medical therapy is also compared with the combinatorial optimization of
engineering and materials science and similarities and differences are
delineated.Comment: 25 page
Modeling Tumor Clonal Evolution for Drug Combinations Design
Cancer is a clonal evolutionary process. This presents challenges for effective therapeutic intervention, given the constant selective pressure toward drug resistance. Mathematical modeling from population genetics, evolutionary dynamics, and engineering perspectives are being increasingly employed to study tumor progression, intratumoral heterogeneity, drug resistance, and rational drug scheduling and combinations design. In this review we discuss the promising opportunities that these interdisciplinary approaches hold for advances in cancer biology and treatment. We propose that quantitative modeling perspectives can complement emerging experimental technologies to facilitate enhanced understanding of disease progression and improved capabilities for therapeutic drug regimen designs.David H. Koch Cancer Research Fund (Grant P30-CA14051)National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967)National Institute of General Medical Sciences (U.S.). Interdepartmental Biotechnology Training Program (5T32GM008334
Cancer modeling: from optimal cell renewal to immunotherapy
Cancer is a disease caused by mutations in normal cells. According to the National Cancer Institute, in 2016, an estimated 1.6 million people were diagnosed and approximately 0.5 million people died from the disease in the United States. There are many factors that shape cancer at the cellular and organismal level, including genetic, immunological, and environmental components. In this thesis, we show how mathematical modeling can be used to provide insight into some of the key mechanisms underlying cancer dynamics. First, we use mathematical modeling to investigate optimal homeostatic cell renewal in tissues such as the small intestine with an emphasis on division patterns and tissue architecture. We find that the division patterns that delay the accumulation of mutations are strictly associated with the population sizes of the tissue. In particular, patterns with long chains of differentiation delay the time to observe a second-hit mutant, which is important given that for many cancers two mutations are enough to initiate a tumor. We also investigated homeostatic cell renewal under a selective pressure and find that hierarchically organized tissues act as suppressors of selection; we find that an architecture with a small number of stem cells and larger pools of transit amplifying cells and mature differentiated cells, together with long chains of differentiation, form a robust evolutionary strategy to delay the time to observe a second-hit mutant when mutations acquire a fitness advantage or disadvantage. We also formulate a model of the immune response to cancer in the presence of costimulatory and inhibitory signals. We demonstrate that the coordination of such signals is crucial to initiate an effective immune response, and while immunotherapy has become a promising cancer treatment over the past decade, these results offer some explanations for why it can fail
Recipes for calibration and validation of agent-based models in cancer biomedicine
Computational models and simulations are not just appealing because of their
intrinsic characteristics across spatiotemporal scales, scalability, and
predictive power, but also because the set of problems in cancer biomedicine
that can be addressed computationally exceeds the set of those amenable to
analytical solutions. Agent-based models and simulations are especially
interesting candidates among computational modelling strategies in cancer
research due to their capabilities to replicate realistic local and global
interaction dynamics at a convenient and relevant scale. Yet, the absence of
methods to validate the consistency of the results across scales can hinder
adoption by turning fine-tuned models into black boxes. This review compiles
relevant literature to explore strategies to leverage high-fidelity simulations
of multi-scale, or multi-level, cancer models with a focus on validation
approached as simulation calibration. We argue that simulation calibration goes
beyond parameter optimization by embedding informative priors to generate
plausible parameter configurations across multiple dimensions
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Oncogenic senescence: a multi-functional perspective.
Cellular senescence is defined as an irreversible growth arrest with the acquisition of a distinctive secretome. The growth arrest is a potent anticancer mechanism whereas the secretome facilitates wound healing, tissue repair, and development. The senescence response has also become increasingly recognized as an important contributor to aging and age-related diseases, including cancer. Although oncogenic mutations are capable of inducing a beneficial senescence response that prevents the growth of premalignant cells and promotes cancer immune-surveillance, the secretome of senescent cells also includes factors with pro-tumorigenic properties. On June 23rd and 24th, 2016, the Division of Cancer Biology of the National Cancer Institute sponsored a workshop to discuss the complex role of cellular senescence in tumorigenesis with the goal to define the major challenges and opportunities within this important field of cancer research. Additionally, it was noted how the development of novel tools and technologies are required to accelerate research into a mechanistic understanding of senescent cells in carcinogenesis in order to overcome the current limitations in this exciting, yet ill-defined area
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