Probabilistic reconstruction of the tumor progression process in gene regulatory networks in the presence of uncertainty

Mohammad Shahrokh Esfahani,Edward R Dougherty,Byung-Jun Yoon

doi:10.1186/1471-2105-12-s10-s9

Mohammad Shahrokh Esfahani, Edward R Dougherty + Show 1 more

Open Access

https://doi.org/10.1186/1471-2105-12-s10-s9

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Abstract

BackgroundAccumulation of gene mutations in cells is known to be responsible for tumor progression, driving it from benign states to malignant states. However, previous studies have shown that the detailed sequence of gene mutations, or the steps in tumor progression, may vary from tumor to tumor, making it difficult to infer the exact path that a given type of tumor may have taken.ResultsIn this paper, we propose an effective probabilistic algorithm for reconstructing the tumor progression process based on partial knowledge of the underlying gene regulatory network and the steady state distribution of the gene expression values in a given tumor. We take the BNp (Boolean networks with pertubation) framework to model the gene regulatory networks. We assume that the true network is not exactly known but we are given an uncertainty class of networks that contains the true network. This network uncertainty class arises from our partial knowledge of the true network, typically represented as a set of local pathways that are embedded in the global network. Given the SSD of the cancerous network, we aim to simultaneously identify the true normal (healthy) network and the set of gene mutations that drove the network into the cancerous state. This is achieved by analyzing the effect of gene mutation on the SSD of a gene regulatory network. At each step, the proposed algorithm reduces the uncertainty class by keeping only those networks whose SSDs get close enough to the cancerous SSD as a result of additional gene mutation. These steps are repeated until we can find the best candidate for the true network and the most probable path of tumor progression.ConclusionsSimulation results based on both synthetic networks and networks constructed from actual pathway knowledge show that the proposed algorithm can identify the normal network and the actual path of tumor progression with high probability. The algorithm is also robust to model mismatch and allows us to control the trade-off between efficiency and accuracy.

Highlights

Accumulation of gene mutations in cells is known to be responsible for tumor progression, driving it from benign states to malignant states
We focus on Boolean networks (BNs) with perturbation owing to their role in modeling gene regulatory networks, a key point being that their dynamics can be modeled as Markov chains, thereby facilitating the modeling of genetic alterations in signaling pathways by shifting the network steady state distribution (SSD) from the normal SSD toward the cancerous SSD
This paper introduced an elegant method for extracting the information needed for constructing Boolean networks from biological pathways, it yields a large number of networks since the available network knowledge is often incomplete and not enough to point out the true network

Summary

Introduction

Accumulation of gene mutations in cells is known to be responsible for tumor progression, driving it from benign states to malignant states. If one could obtain wild-type time-course data, one could reduce this uncertainty class by standard Boolean network inference methods. The proposed algorithm operates on the basis of probabilistic sequential fault-detection, which views regulatory alterations, such as gene mutations, as faults in the network wiring [2] It estimates the sequence of faults leading to the current (cancerous) regulatory structure, and from these estimates, a reduced uncertainty class for the original (healthy) network. By taking this approach the algorithm simultaneously accomplishes a dual purpose: network inference and fault detection

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