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Integrative Mathematical Models

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Abstract

Statistical, Population-based and Agent-based Modeling
Data from the four OMIC analyses will be transferred to the statistical modelers for integration and analysis. Integrative statistical modeling is the core of the clinical aspects of the CCE project. The goal is to develop and implement a data analysis framework that will effectively integrate and explore the vast array of patient data, which include the OMIC analyses, clinical treatment regimens and outcomes, and demographic data. Because of the enormous size of the OMIC databases, this framework will support

  • a data repository and data navigation capability tailored for access and integration by exploratory methods that lead to conjectured models
  • methods of model diagnostics that together with cancer domain knowledge reject or accept these conjectured models
  • iterative exploration and modeling until valid models are discovered and, as a result
  • statistical methods of estimation and testing based on the validated models.

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The OMIC infrastructure supports a knowledge discovery environment that provides off-the-shelf analysis, as well as the capability to "program with the data" -- allowing wholly new methods of analysis to be readily developed that are tailored to the data. Cutting across the OMIC data and modeling effort will be tools and methods for data visualization, a critical technology for understanding very large amounts of information.

Contributor Ann Christine Catlin
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Cite this work

Researchers should cite this work as follows:

  • (2008), "Integrative Mathematical Models," http://ccehub.org/resources/104.

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Tags
  1. biomarker discovery
  2. multi-agent based modeling
  3. OMIC analysis
  4. population-based models
  5. statistical models

In This Series

  1. OMIC Data Formats: Notes for the Modeling Research Group

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    20 Feb 2009 | Notes | Contributor(s): Ann Christine Catlin

    Each OMIC analysis laboratory has one or more analysis workflows that begin with instrument-generated datasets, where the format of the instrument dataset made available to modeling researchers for analysis depends on the instrument, the analysis mode, and the available file conversion …

  2. Monte Carlo Simulation for four CRC Incidence Models

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    12 Dec 2008 | Tools | Contributor(s): Eric Sherer

    A Monte Carlo Simulator determines the risk of colorectal cancer (CRC) development in a population by calculating multiple, individual patient trajectories.

  3. GCxGC-MS Data Classification and Alignment

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    02 Dec 2008 | Tools | Contributor(s): Dabao Zhang, Min Zhang, Jason Catlin

    Align and classify GCxGC-MS data

  4. Statistical Modeling of OMIC Data

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    30 Oct 2008 | Teaching Materials | Contributor(s): Min Zhang

    This presentation describes the statistical modeling work of Professor Min Zhang. A discussion of statistical methods for identifying biomarkers is presented first, including Classical methods and Bayesian and Regularized Variable Selection methods. Next, the alignment of GCxGC MS data using the …

  5. Introduction to Population Balance Modeling

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    02 Oct 2008 | Teaching Materials | Contributor(s): Eric Sherer

    This presentation gives an overview of the role and applications of population balance (or structured) models in describing biological phenomenon. These models represent the continuum between purely empirical models and mechanistic models where details are often lumped into the structured …

  6. A Multi-Agent Approach to Modeling of the Indiana CRC Care System

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    19 Jun 2008 | Online Presentations | Contributor(s): Selen Cremaschi

    This presentation introduces one of the projects from the cancer care engineering portfolio. It focuses on colorectal cancer care system modeling using a multi-agent based approach. The project methodology and its current state are explained. Tapas Das, PhD (USF) Brad Doebbeling, MD, MSc, FACP …

  7. Predicting Patient-specific CRC Incidence from Polyp Prevalence

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    17 Jun 2008 | Teaching Materials | Contributor(s): Eric Sherer

    Seminal work on CRC incidence modeling argued that the slope of the linear log-log CRC incidence with age implies that 6 or 7 somatic mutations are required for transformation to CRC. Subsequent modeling efforts have built on this theme by using models of linear, sequential transformations for …

  8. Colorectal Cancer Incidence Prediction Model

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    16 Jun 2008 | Tools | Contributor(s): Eric Sherer, Mohd Haziq Rahmad

    Stochastic simulation of polyp and colorectal cancer (CRC) incidence with patient age.

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