Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology

Abstract

BackgroundRecent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations.ResultsWe propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells.ConclusionsThe proposed algorithm provides a robust, scalable and cost effective solution for inferring network topologies from biological data. It can potentially be applied to explore novel pathways which play important roles in life threatening disease like cancer.

BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research

“…Since measurement noise is random ε i ( t ) is a random variable and typically has Gaussian distribution with zero mean and variance σ 2 , i.e. ε i ( t )~ N (0, σ 2 )1218. The error variance ( σ 2 ) depends on many factors such as biological variability and measurement noise, and is typically unknown.…”

“…Although vast majority of model based methods assume that the expressions of a gene and its regulators are linearly dependent59101112, these methods use different model search algorithms e.g. Least Absolute Shrinkage and Selection Operator (LASSO), Dantzig Selector, elastic net, Markov Chain Monte Carlo and Heuristic search51213141516171819. Some of these methods such as LASSO and elastic net choose the best model, others such as MCMC or Heuristic search based Bayesian Model Averaging (BMA) methods512131518 select multiple models that provide close fits to the data and use these to estimate an average model along with its confidence interval.…”

“…Least Absolute Shrinkage and Selection Operator (LASSO), Dantzig Selector, elastic net, Markov Chain Monte Carlo and Heuristic search51213141516171819. Some of these methods such as LASSO and elastic net choose the best model, others such as MCMC or Heuristic search based Bayesian Model Averaging (BMA) methods512131518 select multiple models that provide close fits to the data and use these to estimate an average model along with its confidence interval. It is also possible to incorporate different types of existing data in BMA51218 to increase the accuracy of the reconstructed GRN.…”

Integrated analysis reveals that STAT3 is central to the crosstalk between HER/ErbB receptor signaling pathways in human mammary epithelial cells

“…84 In BVSA, the constitutive MRA equation is changed to Σ k A ik r ik R kj =0, where the added variable A ij represents if direct interaction between modules i and j are present (A ij =1) or absent (A ij =0). For noisy datasets, which is the usual case in biological experiments, the global responses R ij contain uncertainties and the above equality usually does not hold exactly.…”

“…Sandra et al utilized Markov Chain Monte Carlo sampling to obtain the posterior distributions for A ij , and decided on the inferred network topology by defining a threshold equal to the average posterior edge probability. 84 We have used the Gibbs sampler MATLAB program provided as Supplementary Material to their publication in our simulations. Simulations were run for 1000 Gibbs scans (parameter: noit) and 5000 iterations (parameter: times).…”