The liver is one of the important organs in our bodies, playing a vital role in glucose homeostasis, the synthesis of bile acids for the metabolism of cholesterol, and the secretion of proteins to aid clotting 
. Additionally, the liver is primarily responsible for the detoxification of foreign substances, including a variety of environmental toxicants, alcohol, cigarette smoke, and drugs 
. Hepatocytes are the principal cells in the liver, comprising over 80% of its mass and performing several characteristic functions of this organ. Liver culture systems such as hepatocyte monolayers (HMs) and collagen sandwiches (CSs) are routinely used to test adverse effects of drugs and environmental toxicants. In HMs, hepatocytes are cultured on a single collagen gel. Such cells progressively lose their phenotypic characteristics over time 
. In CS systems, hepatocytes are maintained between two collagen gels. Hepatocytes in CS cultures remain stable over extended periods of time, and maintain differentiated hepatic functions 
. While morphological and physiological characteristics of hepatocytes in CS cultures have been studied extensively, comprehensive transcriptional studies of these culture systems do not appear to have been reported. Therefore, in an earlier study, we performed a systematic temporal study of genome-wide gene expression programs in HMs and in CS cultures over an eight-day period 
. We used Gene Set Enrichment Analysis (GSEA) 
to compare the transcriptional programs in the two culture systems. Our results demonstrated that gene expression in hepatocytes in CS cultures steadily and comprehensively diverges from that in HMs 
. Gene sets up-regulated in CS cultures included several hepatic functions, such as metabolism of lipids, amino acids, carbohydrates, and alcohol, and synthesis of bile acids. Monooxygenases such as Cytochrome-P450 enzymes did not show any change between the culture systems after one day, but exhibited significant up-regulation in CS cultures after three days and later in comparison to HMs.
This analysis did not consider the fact that a cell's response to its environment is governed by an intricate network of molecular interactions. These interactions dynamically change in response to a myriad of cues. Therefore, discovering the set of molecular interactions that are active in a given cellular context is a fundamental question in computational systems biology 
. In the current work, we reanalyze the CS-HM transcriptional data in the light of an underlying molecular interaction network. We propose a novel approach called “Contextual Biological Process Linkage Network” (CBPLN) that focuses on computing which processes in the cell are perturbed in a particular context and how these processes are linked to each other. Our approach is predicated on the belief that high-level linkages between pathways and processes make identification of important biological trends more tractable and intuitive than through interactions between individual genes and molecules alone. Our method requires three inputs:
- -values representing the statistical significance of the differential expression of each gene (upon comparing a treatment to a control), which we refer to hereafter as expression -values,
- a functional or physical interaction network connecting genes and proteins, and
- a dataset of functional annotations for genes and proteins.
We extend the method developed by Dotan-Cohen et al. 
to detect directed linkages between gene sets in the context of a functional interaction network. Given two biological processes
and the sets of genes that are members of each, these authors computed the number of genes annotated by
that are themselves not annotated by
and interact with at least one gene annotated by
. They estimated the statistical significance of this count using the one-sided version of Fisher's exact test. Similar methods developed by Pandey et al. 
for regulatory and physical interaction networks are aimed at discovering chains of significantly linked biological processes.
In this work, we extend the ideas of Dotan-Cohen et al.
to incorporate gene expression measurements to determine which inter-process links are significantly perturbed between the measured conditions. Informally, we compute a score for a link from process
based upon the expression
-values of pairs of interacting genes, where one gene belongs to process
and the other to process
. Our score takes estimates of confidence in the interactions into account. High-confidence interactions with highly perturbed incident genes make large contributions to the score. We estimate the statistical significance of the score by computing an empirical distribution of scores under two different hypotheses. The first hypothesis tests the dependence of the score on the particular set of genes annotated by
, i.e., it asks if we would observe a particular score from process
even if we selected the genes annotated by
uniformly at random from the set of all annotated genes. This test directly extends the approach used by Dotan-Cohen et al.
The second hypothesis tests the dependence of the score on the specific interactions in the network, i.e., it asks if we would observe the score from
even with an interaction network drawn from a distribution of networks with the same node degrees. Under either hypothesis, we report the significance of the link, after multiple testing correction, as a
-value. Hereafter, we refer to this quantity as the link -value
, to distinguish it from the expression
-values that are inputs to our method.