Merrill Knapp

5′-AMP-Activated Protein Kinase (AMPK) Is Induced by Low-Oxygen and Glucose Deprivation Conditions Found in Solid-Tumor Microenvironments

ABSTRACT 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-1α 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-1α- or AMPKα-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.

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.

Pathway Logic: Executable Models of Biological Networks

Abstract In this paper we describe the use of the rewriting logic based Maude tool to model and analyze mammalian signaling pathways. We discuss the representation of the underlying biological concepts and events and describe the use of the new search and model checking capabilities of Maude 2.0 to analyze the modeled network. We also discuss the use of Maudes reflective capability for meta modeling and analyzing the models themselves. The idea of symbolic biological experiments opens up an exciting new world of challenging applications for formal methods in general and for rewriting logic based formalisms in particular.

Integrated analysis of breast cancer cell lines reveals unique signaling pathways

BackgroundCancer 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.ResultsWe 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.ConclusionsAll together, our results support the utility of symbolic system biology models for identification of therapeutic approaches that will be effective against breast cancer subsets.

Analyzing pathways using SAT-based approaches

A network of reactions is a commonly used paradigm for representing knowledge about a biological process. How does one understand such generic networks and answer queries using them? In this paper, we present a novel approach based on translation of generic reaction networks to Boolean weighted MaxSAT. The Boolean weighted MaxSAT instance is generated by encoding the equilibrium configurations of a reaction network by weighted boolean clauses. The important feature of this translation is that it uses reactions, rather than the species, as the boolean variables. Existing weighted MaxSAT solvers are used to solve the generated instances and find equilibrium configurations. This method of analyzing reaction networks is generic, flexible and scales to large models of reaction networks. We present a few case studies to validate our claims.

The pathalyzer: a tool for analysis of signal transduction pathways

The Pathalyzer is a program for analyzing large-scale signal transduction networks. Reactions and their substrates and products are represented as transitions and places in a safe Petri net. The user can interactively specify goal states, such as activation of a particular protein in a particular cell site, and the system will automatically find and display a pathway that results in the goal state - if possible. The user can also require that the pathway be generated without using certian proteins. The system can also find all individual places and all pairs of places which, if knocked out, would prevent the goals from being achieved. The tool is intended to be used by biologists with no significant understanding of Petri nets or any of the other concepts used in the implementation.

Inferring Executable Models from Formalized Experimental Evidence

Executable symbolic models have been successfully used to analyze networks of biological reactions. However, the process of building an executable model from published experimental findings is still carried out manually. The process is very time consuming and requires expert knowledge. As a first step in addressing this problem, this paper introduces an automated method for deriving executable models from formalized experimental findings called datums. We identify the relevant data in a collection of datums. We then translate the information contained in datums to logical assertions. Together with a logical theory formalizing the interpretation of datums, these assertions are used to infer a knowledge base of reaction rules. These rules can then be assembled into executable models semi-automatically using the Pathway Logic system. We applied our technique to the experimental evidence relevant to Hras activation in response to Egf available in our datum knowledge base. When compared to the Pathway Logic model (curated manually from the same datums by an expert), our model makes most of the same predictions regarding reachability and knockouts. Missing information is due to missing assertions that require reasoning about the effects of mutations and background knowledge to generate. This is being addressed in ongoing work.

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.

Inferring Mechanism of Action of an Unknown Compound from Time Series Omics Data

Identifying the mechanism of action (MoA) of an unknown, possibly novel, substance (chemical, protein, or pathogen) is a significant challenge. Biologists typically spend years working out the MoA for known compounds. MoA determination is especially challenging if there is no prior knowledge and if there is an urgent need to understand the mechanism for rapid treatment and/or prevention of global health emergencies. In this paper, we describe a data analysis approach using Gaussian processes and machine learning techniques to infer components of the MoA of an unknown agent from time series transcriptomics, proteomics, and metabolomics data.

Explaining Response to Drugs Using Pathway Logic

Pathway Logic (PL) is a general system for modeling signal transduction and other cellular processes with the objective of understanding how cells work. Each specific model system builds on a knowledge base of rules formalizing local process steps such as post translational modification. The Pathway Logic Assistant (PLA) is a collection of visualization and reasoning tools that allow users to derive specific executable models by specifying of an initial state. The resulting network of rule instances describes possible behaviors of the modelled system. Subnets and pathways can then be computed (they are not hard wired) by specifying states to reach and/or to avoid. The STM knowledge base is a curated collection of signal transduction rules supported by experimental evidence. In this paper we describe methods for using the PL STM knowledge base and the PLA tools to explain observed perturbations of signaling pathways when cells are treated with drugs targeting specific activities or protein states. We also explore ideas for conjecturing targets of unknown drugs. We illustrate the methods on phosphoproteomics data (RPPA) from SKMEL133 melanoma cancer cells treated with different drugs targeting components of cancer signaling pathways. Existing curated knowledge allowed to us explain many of the responses. Conflicts between the STM model predictions and the data suggest missing requirements for rules to apply.