Computational tools for assessing gene therapy under branching process models of mutation

  title={Computational tools for assessing gene therapy under branching process models of mutation},
  author={Timothy C Stutz and Janet S. Sinsheimer and Mary E. Sehl and Jason Xu},
  journal={Bulletin of Mathematical Biology},
Multitype branching processes are ideal for studying the population dynamics of stem cell populations undergoing mutation accumulation over the years following transplant. In such stochastic models, several quantities are of clinical interest as insertional mutagenesis carries the potential threat of leukemogenesis following gene therapy with autologous stem cell transplantation. In this paper, we develop a three-type branching process model describing accumulations of mutations in a population… 

Likelihood-based inference for partially observed stochastic epidemics with individual heterogeneity

A likelihood-based inference method based on the stochastic EM algorithm is proposed, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network.



Genetic instability and clonal expansion.

Statistical inference for partially observed branching processes with application to cell lineage tracking of in vivo hematopoiesis

The methodology is transferrable to a large class of stochastic compartmental models and multi-type branching models, commonly used in studies of cancer progression, epidemiology, and many other fields, and should help biologists compare competing hypotheses about how progenitor cells differentiate.

Fate mapping of human glioblastoma reveals an invariant stem cell hierarchy

The clonal evolution of barcoded glioblastoma cells in an unbiased way following serial xenotransplantation is studied to define their individual fate behaviours, and it is shown that chemotherapy facilitates the expansion of pre-existing drug-resistant gliOBlastoma stem cells.

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In the limit of simultaneous small mutation rate and large time scaling, the distribution of the mutant cells develops some atypical properties, including a power law tail and diverging average.

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Drug resistance in cancer: principles of emergence and prevention.

  • N. KomarovaD. Wodarz
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 2005
It is found that resistance arises mainly before the start of treatment and, for cancers with high turnover rates, combination therapy is less likely to yield an advantage over single-drug therapy.

Evolutionary dynamics of cancer in response to targeted combination therapy

It is found that dual therapy results in long-term disease control for most patients, if there are no single mutations that cause cross-resistance to both drugs; in patients with large disease burden, triple therapy is needed.

Towards Predictive Computational Models of Oncolytic Virus Therapy: Basis for Experimental Validation and Model Selection

A general framework to study the dynamics of oncolytic viruses that is independent of uncertain and arbitrary mathematical formulations is presented and can be used as a basis for model selection and validation in the context of future, more detailed experimental studies.

Branching Process Models of Cancer

This chapter uses multitype branching processes with mutation to model cancer, with cancer progression, resistance to therapy, and metastasis in mind, and investigates the time of the first type k mutation and the growth of the number of type k cells.