Introduction 
Gastrointestinal infections are the second most common cause of childhood mortality in the developing world and Typhoid alone (caused by serovar Typhi) is estimated to result in 500,000 deaths per year [1].
In addition to fluid and electrolyte loss, non-typhoidal Salmonella often results in septicemia in children and in HIV infected adults in developing countries with a fatality rate of 25% or greater [2].
Salmonella enteriditis serotype Typhimurium (referred to simply as Salmonella or Typhimurium below) is a paradigm for understanding intracellular pathogenesis because of its established genetics and simple and inexpensive animal model - the mouse.
All strains of Salmonella enteriditis share at least 95% sequence identity; the differences are associated with growth in a specific host or survival in an environmental niche.
More than 4% of the entire genome is required for Typhimurium to infect the mouse [3].
These genes are widely distributed around the entire circular chromosome including many genes not involved in metabolic processes nor required for growth under laboratory conditions.
Numerous studies have assigned a small fraction of these genes to specific steps in mouse infection but most are still a mystery.
Many virulence genes are attributable to horizontally acquired DNA sequences that are not present in nonpathogenic but related bacteria.
These regions include two 40 kb stretches of DNA termed Salmonella pathogenicity islands 1 (SPI-1) and 2 (SPI-2) [4]-[9].
SPI-1 and SPI-2 encode a secretion apparatus resembling a needle and related to the bacterial flagella that uses the proton motive force to secrete proteins directly into the cytoplasm of the eukaryotic cell [10].
Secretion can take place from extracellular bacteria that are juxtaposed to the surface of the cell or from intracellular bacteria located in vacuoles.
The two type III secretion systems are expressed under different environmental conditions and play distinct roles in pathogenesis.
SPI-2 is known to be required for systemic infection whereas SPI-1 plays an essential role during intestinal infection and in mouse persistence [11]-[14].
During the course of systemic infection in mice, bacteria are found within neutrophils, monocytes, dendritic cells, and B and T cells but are not found extracellularly until the last one or two days immediately before death of the mouse [15]-[17].
How Salmonella survives and replicates within the host and how it expresses virulence genes at the appropriate time during systemic infection is little understood and the subject of this work.
Technological advances in the last 10 years such as microarrays, whole genome sequences, and global proteomics have provided a more complete picture of gene expression for a number of bacteria.
The goal of the current work is to develop a predictive model for host-pathogen interactions that will provide insight into how Salmonella responds to specific conditions in the host.
This approach was based on identification of regulators that were necessary for Salmonella to cause a systemic infection and transcriptional profiling of isogenic derivatives missing the regulator under a variety of growth conditions.
The transcriptional profiles provided more than 300,000 data point, necessitating computer analysis.
We have used SEBINI (Software Environment for Biological Network Inference; [18]) to directly compare multiple network algorithms.
The network inference algorithm that we have used is the context likelihood of relatedness (CLR) to analyze the gene expression profiles [19].
CLR is an extension of the relevance network class of machine learning algorithms [20] and provides the highest precision of several algorithms tested [19].
At a 60% true positive rate, CLR identified 1,079 regulatory interactions in E. coli, of which 338 were in previously known networks and 741 were novel predictions (ibid).
The analysis of our data provided a testable regulatory hierarchy and a list of genes with similar expression profiles as described below.
