Abstract
Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets and consequently address the issue of network symmetry. This work is aimed at developing an efficient pipeline to reverse-engineer regulatory networks based on the individual patient proteomic data. The first step uses the SCOUT algorithm to infer the pseudo-time trajectory of individual patients. Then the path-consistent method with part mutual information is used to construct a static network that contains the potential protein interactions. To address the issue of network symmetry in terms of undirected symmetric network, a dynamic model of ordinary differential equations is used to further remove false interactions to derive asymmetric networks. In this work a dataset from triple-negative breast cancer patients is used to develop a protein-protein interaction network with 15 proteins.
Highlights
Recent advances in experimental biology studies have produced large amount of molecular activity data [1]
In this work we propose a general pipeline to use individual patient data to infer protein-protein interaction networks
We initially select 60 important proteins from the mitogen-activated protein (MAP) kinase pathway from the 1200 proteins sampled in the database showing large variations [47]
Summary
Recent advances in experimental biology studies have produced large amount of molecular activity data [1]. The single-cell experiments are able to quantify gene expression activities or protein abundances in a large number of single cells in a single experiment, which provides rich information to study the cellular heterogeneity [2]. A similar datum type is the individual patient data that measure the cellular information from the cell lines of each patients [3]. The inference methods for constructing regulatory networks can be mainly classified into three major types, namely the correlation-based methods, dynamic model methods and machine learning methods [9,10,11]. The correlation-based methods use one or more statistical qualities to measure the relationship between pairs of variables in a network
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