Contraceptive and Infertility Target DataBase: a contraceptive drug development tool for targeting and analysis of human reproductive specific tissues†.

The long and challenging drug development process begins with discovery biology for the selection of an appropriate target for a specific indication. Target is a broad term that can be applied to a range of biological entities such as proteins, genes, and ribonucleic acids (RNAs). Although there are numerous databases available for mining biological entities, publicly available searchable, downloadable databases to aid in target selection for a specific disease or indication (e.g., developing contraceptives and infertility treatments) are limited. We report the development of the Contraceptive and Infertility Target DataBase (https://www.citdbase.org), which provides investigators an interface to mine existing transcriptomic and proteomic resources to identify high-quality contraceptive/infertility targets. The development of similar databases is applicable to the identification of targets for other diseases and conditions.

Soft Set Theory for Decision Making in Computational Biology under Incomplete Information.

The study of biological systems is complex and of great importance. There exist numerous approaches to signal transduction processes, including symbolic modeling of cellular adaptation. The use of formal methods for computational systems biology eases the analysis of cellular models and the establishment of the causes and consequences of certain cellular situations associated to diseases. In this paper, we define an application of logic modeling with rewriting logic and soft set theory. Our approach to decision making with soft sets offers a novel strategy that complements standard strategies. We implement a metalevel strategy to control and guide the rewriting process of the Maude rewriting engine. In particular, we adapt mathematical methods to capture imprecision, vagueness, and uncertainty in the available data. Using this new strategy, we propose an extension in the biological symbolic models of Pathway Logic. Our ultimate aim is to automatically determine the rules that are most appropriate and adjusted to reality in dynamic systems using decision making with incomplete soft sets.

Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search.

In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing, which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation.

Integrated analysis of breast cancer cell lines reveals unique signaling pathways.

BACKGROUND: Cancer is a heterogeneous disease resulting from the accumulation of genetic defects that negatively impact control of cell division, motility, adhesion and apoptosis. Deregulation in signaling along the EgfR-MAPK pathway is common in breast cancer, though the manner in which deregulation occurs varies between both individuals and cancer subtypes. RESULTS: We were interested in identifying subnetworks within the EgfR-MAPK pathway that are similarly deregulated across subsets of breast cancers. To that end, we mapped genomic, transcriptional and proteomic profiles for 30 breast cancer cell lines onto a curated Pathway Logic symbolic systems model of EgfR-MAPK signaling. This model was composed of 539 molecular states and 396 rules governing signaling between active states. We analyzed these models and identified several subtype-specific subnetworks, including one that suggested Pak1 is particularly important in regulating the MAPK cascade when it is over-expressed. We hypothesized that Pak1 over-expressing cell lines would have increased sensitivity to Mek inhibitors. We tested this experimentally by measuring quantitative responses of 20 breast cancer cell lines to three Mek inhibitors. We found that Pak1 over-expressing luminal breast cancer cell lines are significantly more sensitive to Mek inhibition compared to those that express Pak1 at low levels. This indicates that Pak1 over-expression may be a useful clinical marker to identify patient populations that may be sensitive to Mek inhibitors. CONCLUSIONS: All together, our results support the utility of symbolic system biology models for identification of therapeutic approaches that will be effective against breast cancer subsets.

5'-AMP-activated protein kinase (AMPK) is induced by low-oxygen and glucose deprivation conditions found in solid-tumor microenvironments.

Low oxygen gradients (hypoxia and anoxia) are important determinants of pathological conditions under which the tissue blood supply is deficient or defective, such as in solid tumors. We have been investigating the relationship between the activation of hypoxia-inducible factor 1 (HIF-1), the primary transcriptional regulator of the mammalian response to hypoxia, and 5'-AMP-activated protein kinase (AMPK), another regulatory system important for controlling cellular energy metabolism. In the present study, we used mouse embryo fibroblasts nullizygous for HIF-1alpha or AMPK expression to show that AMPK is rapidly activated in vitro by both physiological and pathophysiological low-oxygen conditions, independently of HIF-1 activity. These findings imply that HIF-1 and AMPK are components of a concerted cellular response to maintain energy homeostasis in low-oxygen or ischemic-tissue microenvironments. Finally, we used transformed derivatives of wild-type and HIF-1alpha- or AMPKalpha-null mouse embryo fibroblasts to determine whether AMPK is activated in vivo. We obtained evidence that AMPK is activated in authentic hypoxic tumor microenvironments and that this activity overlaps with regions of hypoxia detected by a chemical probe. We also showed that AMPK is important for the growth of this tumor model.

Glucose utilization is essential for hypoxia-inducible factor 1 alpha-dependent phosphorylation of c-Jun.

Hypoxia and anoxia are important microenvironmental stresses that contribute to pathological events such as solid-tumor development. We have been investigating the effects of hypoxia and anoxia on expression of the proto-oncogene c-jun and the regulation of c-Jun/AP-1 transcription factors. In earlier work using genetically manipulated mouse embryo fibroblasts (mEFs), we found a functional relationship among c-jun expression, c-Jun N-terminal phosphorylation, and the presence of hypoxia-inducible factor 1 alpha (HIF-1 alpha), the oxygen-regulated subunit of the HIF-1 transcription factor. Both the induction of c-jun mRNA expression and c-Jun N-terminal phosphorylation in cells exposed to hypoxia or anoxia were found to be dependent on the presence of HIF-1 alpha, but this was not the case in cells exposed to less-severe hypoxia. Here we describe new findings concerning HIF-1-dependent c-Jun N-terminal phosphorylation in cells exposed to hypoxia or anoxia. Specifically, we report that hypoxia-inducible c-Jun N-terminal kinase (JNK) activity, which involves JNKs or stress-activated protein kinases (SAPKs), is dependent on enhanced glucose utilization mediated by HIF-1. These results suggest a model in which hypoxia-inducible JNK activity is connected to oxygen sensing through increased glucose absorption and/or glycolytic activity regulated by the HIF-1 system. We also found that basal threonine and tyrosine phosphorylation (within the TEY motif) of extracellular signal-regulated kinases 1 and 2 (ERK1/2) and the corresponding ERK1/2 activity were defective in hypoxic HIF-1 alpha-null mEFs but not in wild-type mEFs, independently of glucose uptake. Therefore, the activities of both JNKs/SAPKs and ERK1/2 are sensitive to HIF-1-dependent processes in cells exposed to hypoxia or anoxia.

Pathway logic: symbolic analysis of biological signaling.

The genomic sequencing of hundreds of organisms including homo sapiens, and the exponential growth in gene expression and proteomic data for many species has revolutionized research in biology. However, the computational analysis of these burgeoning datasets has been hampered by the sparse successes in combinations of data sources, representations, and algorithms. Here we propose the application of symbolic toolsets from the formal methods community to problems of biological interest, particularly signaling pathways, and more specifically mammalian mitogenic and stress responsive pathways. The results of formal symbolic analysis with extremely efficient representations of biological networks provide insights with potential biological impact. In particular, novel hypotheses may be generated which could lead to wet lab validation of new signaling possibilities. We demonstrate the graphic representation of the results of formal analysis of pathways, including navigational abilities, and describe the logical underpinnings of the approach. In summary, we propose and provide an initial description of an algebra and logic of signaling pathways and biologically plausible abstractions that provide the foundation for the application of high-powered tools such as model checkers to problems of biological interest.