An algorithm for score aggregation over causal biological networks based on random walk sampling.

Background: We recently published in BMC Systems Biology an approach for calculating the perturbation amplitudes of causal network models by integrating gene differential expression data. This approach relies on the process of score aggregation, which combines the perturbations at the level of the individual network nodes into a global measure that quantifies the perturbation of the network as a whole. Such "bottom-up" aggregation relates the changes in molecular entities measured by omics technologies to systems-level phenotypes.

Quantitative assessment of biological impact using transcriptomic data and mechanistic network models.

Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances.

Construction of a computable network model for DNA damage, autophagy, cell death, and senescence.

Towards the development of a systems biology-based risk assessment approach for environmental toxicants, including tobacco products in a systems toxicology setting such as the "21st Century Toxicology", we are building a series of computable biological network models specific to non-diseased pulmonary and cardiovascular cells/tissues which capture the molecular events that can be activated following exposure to environmental toxicants. Here we extend on previous work and report on the construction and evaluation of a mechanistic network model focused on DNA damage response and the four main cellular fates induced by stress: autophagy, apoptosis, necroptosis, and senescence. In total, the network consists of 34 sub-models containing 1052 unique nodes and 1538 unique edges which are supported by 1231 PubMed-referenced literature citations.

Early patient stratification and predictive biomarkers in drug discovery and development: a case study of ulcerative colitis anti-TNF therapy.

The current drug discovery paradigm is long, costly, and prone to failure. For projects in early development, lack of efficacy in Phase II is a major contributor to the overall failure rate. Efficacy failures often occur from one of two major reasons: either the investigational agent did not achieve the required pharmacology or the mechanism targeted by the investigational agent did not significantly contribute to the disease in the tested patient population.

A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue.

Background: Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g.

Construction of a computable cell proliferation network focused on non-diseased lung cells.

Background: Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses.

Small molecule activators of SIRT1 replicate signaling pathways triggered by calorie restriction in vivo.

Background: Calorie restriction (CR) produces a number of health benefits and ameliorates diseases of aging such as type 2 diabetes. The components of the pathways downstream of CR may provide intervention points for developing therapeutics for treating diseases of aging. The NAD+-dependent protein deacetylase SIRT1 has been implicated as one of the key downstream regulators of CR in yeast, rodents, and humans.

Excess SeqA leads to replication arrest and a cell division defect in Vibrio cholerae.

Although most bacteria contain a single circular chromosome, some have complex genomes, and all Vibrio species studied so far contain both a large and a small chromosome. In recent years, the divided genome of Vibrio cholerae has proven to be an interesting model system with both parallels to and novel features compared with the genome of Escherichia coli. While factors influencing the replication and segregation of both chromosomes have begun to be elucidated, much remains to be learned about the maintenance of this genome and of complex bacterial genomes generally.

A parA homolog selectively influences positioning of the large chromosome origin in Vibrio cholerae.

A Vibrio cholerae deletion mutant lacking VS2773, a parA partitioning gene homolog located in a parAB operon on the large chromosome, displays altered positioning of the large chromosome origin. Deletion of a second parA homolog on the large chromosome (VC2061) does not affect its origin positioning. The origin position of the small chromosome is unchanged by either or both of these deletions, suggesting that VC2773 function is specific to the replicon on which it is carried.