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Published on: August 24, 2013
Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles
Mathäus Dejori1, Martin Stetter
1Siemens AG, Corporate Technology, Information and Communications, CT IC 4, D-81730 Munich, Germany. mathaeus.dejori@siemens.com
Motivation:
The characterization of genetic mechanisms underlying normal cellular function, cancer development, pathogenesis, and the effect of drug treatment is one of the most challenging topics for cancer research and molecular biology. Existing methods for inferring genetic regulatory networks from genome-wide expression profiles provide important information about gene interactions and regulatory relationships. However, these methods do not provide information about the impact of possible interventions or changes on such regulatory networks to study cause-effect relationships at a systems-biology level.
Results:
We present a data-driven method called generative inverse modeling, which simulates the effect of local genetic changes on the global cellular state, as reflected by an altered genome-wide expression profile. For each genetic change we define a pathogenic score by calculating to what extent it transforms the simulated expression patterns into patterns measured for pathologically altered tissues. The method can be used to estimate the relevance of genes for disease-specific genetic mechanisms, e.g., as presented here for pathogenesis. Generative inverse modeling is based on a Bayesian probability density estimation from a set of measured gene-expression patterns.
Insights
This study introduces generative inverse modeling to predict how genetic changes affect cellular states. This method quantifies gene relevance for disease pathogenesis by simulating genetic alterations and scoring their pathological impact.
Area of Science:
- Molecular Biology
- Systems Biology
- Genomics
Background:
- Understanding genetic regulatory networks is crucial for cancer research and molecular biology.
- Current methods infer gene interactions but lack causal insights into network changes.
- Systems-level analysis requires understanding the impact of genetic interventions.
Purpose of the Study:
- To develop a data-driven method for simulating the effects of genetic changes on cellular states.
- To quantify the pathogenic impact of genetic alterations.
- To estimate gene relevance in disease-specific genetic mechanisms.
Main Methods:
- Generative inverse modeling based on Bayesian probability density estimation.
- Simulation of local genetic changes to predict global cellular expression profiles.
- Calculation of a pathogenic score to assess the transformation of simulated patterns towards pathological states.
Main Results:
- The method simulates genome-wide expression profile alterations resulting from genetic changes.
- A pathogenic score is defined to measure the extent of transformation towards pathological expression patterns.
- The approach successfully estimates gene relevance for pathogenesis.
Conclusions:
- Generative inverse modeling provides a novel approach to study cause-effect relationships in genetic regulatory networks.
- This method enhances the understanding of gene function in disease pathogenesis.
- It offers a systems-biology perspective on the impact of genetic alterations.
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