Michelle L. Wynn

Logic-based models in systems biology: a predictive and parameter-free network analysis method

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a networks dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

Computational modelling of cell chain migration reveals mechanisms that sustain follow-the-leader behaviour

Follow-the-leader chain migration is a striking cell migratory behaviour observed during vertebrate development, adult neurogenesis and cancer metastasis. Although cell–cell contact and extracellular matrix (ECM) cues have been proposed to promote this phenomenon, mechanisms that underlie chain migration persistence remain unclear. Here, we developed a quantitative agent-based modelling framework to test mechanistic hypotheses of chain migration persistence. We defined chain migration and its persistence based on evidence from the highly migratory neural crest model system, where cells within a chain extend and retract filopodia in short-lived cell contacts and move together as a collective. In our agent-based simulations, we began with a set of agents arranged as a chain and systematically probed the influence of model parameters to identify factors critical to the maintenance of the chain migration pattern. We discovered that chain migration persistence requires a high degree of directional bias in both lead and follower cells towards the target. Chain migration persistence was also promoted when lead cells maintained cell contact with followers, but not vice-versa. Finally, providing a path of least resistance in the ECM was not sufficient alone to drive chain persistence. Our results indicate that chain migration persistence depends on the interplay of directional cell movement and biased cell–cell contact.

Follow-the-leader cell migration requires biased cell-cell contact and local microenvironmental signals

Directed cell migration often involves at least two types of cell motility that include multicellular streaming and chain migration. However, what is unclear is how cell contact dynamics and the distinct microenvironments through which cells travel influence the selection of one migratory mode or the other. The embryonic and highly invasive neural crest (NC) are an excellent model system to study this question since NC cells have been observed in vivo to display both of these types of cell motility. Here, we present data from tissue transplantation experiments in chick and in silico modeling that test our hypothesis that cell contact dynamics with each other and the microenvironment promote and sustain either multicellular stream or chain migration. We show that when premigratory cranial NC cells (at the pre-otic level) are transplanted into a more caudal region in the head (at the post-otic level), cells alter their characteristic stream behavior and migrate in chains. Similarly, post-otic NC cells migrate in streams after transplantation into the pre-otic hindbrain, suggesting that local microenvironmental signals dictate the mode of NC cell migration. Simulations of an agent-based model (ABM) that integrates the NC cell behavioral data predict that chain migration critically depends on the interplay of biased cell-cell contact and local microenvironment signals. Together, this integrated modeling and experimental approach suggests new experiments and offers a powerful tool to examine mechanisms that underlie complex cell migration patterns.

Adipocytes promote pancreatic cancer cell proliferation via glutamine transfer

Adipocytes promote progression of multiple cancers, but their role in pancreatic intraepithelial neoplasia (PanIN) and ductal adenocarcinoma (PDAC) is poorly defined. Nutrient transfer is a mechanism underlying stromal cell-cancer crosstalk. We studied the role of adipocytes in regulating in vitro PanIN and PDAC cell proliferation with a focus on glutamine metabolism. Murine 3T3L1 adipocytes were used to model adipocytes. Cell lines derived from PKCY mice were used to model PanIN and PDAC. Co-culture was used to study the effect of adipocytes on PanIN and PDAC cell proliferation in response to manipulation of glutamine metabolism. Glutamine secretion was measured with a bioanalyzer. Western blotting was used to study the effect of PanIN and PDAC cells on expression of glutamine-related enzymes in adipocytes. Adipocytes promote proliferation of PanIN and PDAC cells, an effect that was amplified in nutrient-poor conditions. Adipocytes secrete glutamine and rescue PanIN and PDAC cell proliferation in the absence of glutamine, an effect that was glutamine synthetase-dependent and involved PDAC cell-induced down-regulation of glutaminase expression in adipocytes. These findings suggest glutamine transfer as a potential mechanism underlying adipocyte-induced PanIN and PDAC cell proliferation.

RhoC GTPase Is a Potent Regulator of Glutamine Metabolism and N-Acetylaspartate Production in Inflammatory Breast Cancer Cells

Inflammatory breast cancer (IBC) is an extremely lethal cancer that rapidly metastasizes. Although the molecular attributes of IBC have been described, little is known about the underlying metabolic features of the disease. Using a variety of metabolic assays, including 13C tracer experiments, we found that SUM149 cells, the primary in vitro model of IBC, exhibit metabolic abnormalities that distinguish them from other breast cancer cells, including elevated levels of N-acetylaspartate, a metabolite primarily associated with neuronal disorders and gliomas. Here we provide the first evidence of N-acetylaspartate in breast cancer. We also report that the oncogene RhoC, a driver of metastatic potential, modulates glutamine and N-acetylaspartate metabolism in IBC cells in vitro, revealing a novel role for RhoC as a regulator of tumor cell metabolism that extends beyond its well known role in cytoskeletal rearrangement.

Inferring the Effects of Honokiol on the Notch Signaling Pathway in SW480 Colon Cancer Cells

In a tumor cell, the development of acquired therapeutic resistance and the ability to survive in extracellular environments that differ from the primary site are the result of molecular adaptations in potentially highly plastic molecular networks. The accurate prediction of intracellular networks in a tumor remains a difficult problem in cancer informatics. In order to make truly rational patient-driven therapeutic decisions, it will be critical to develop methodologies that can accurately infer the molecular circuitry in the cells of a specific tumor. Despite enormous heterogeneity, cellular networks elicit deterministic digital-like responses. We discuss the use and limitations of methodologies that model molecular networks in cancer cells as a digital circuit. We also develop a network model of Notch signaling in colon cancer using a novel reverse engineering logic-based method and published western blot data to elucidate the interactions likely present in the circuits of the SW480 colon cancer cell line. Within this framework, we make predictions related to the role that honokiol may be playing as an anti-cancer drug.

Unraveling the complex regulatory relationships between metabolism and signal transduction in cancer.

Cancer cells exhibit an altered metabolic phenotype, known as the Warburg effect, which is characterized by high rates of glucose uptake and glycolysis, even under aerobic conditions. The Warburg effect appears to be an intrinsic component of most cancers and there is evidence linking cancer progression to mutations, translocations, and alternative splicing of genes that directly code for or have downstream effects on key metabolic enzymes. Many of the same signaling pathways are routinely dysregulated in cancer and a number of important oncogenic signaling pathways play important regulatory roles in central carbon metabolism. Unraveling the complex regulatory relationship between cancer metabolism and signaling requires the application of systems biology approaches. Here we discuss computational approaches for modeling protein signal transduction and metabolism as well as how the regulatory relationship between these two important cellular processes can be combined into hybrid models.

The Impact of Nathan Mantel's "The Detection of Disease Clustering and a Generalized Regression Approach".

Visit the Cancer Research 75th Anniversary [timeline][1]. See related article by Mantel, [Cancer Res 1967;27:209–20][2] . World War II was raging in Europe and it would be 11 months until the United States entered the war, when Cancer Research published its first issue in January 1941. As we

Inferring Intracellular Signal Transduction Circuitry from Molecular Perturbation Experiments

The development of network inference methodologies that accurately predict connectivity in dysregulated pathways may enable the rational selection of patient therapies. Accurately inferring an intracellular network from data remains a very challenging problem in molecular systems biology. Living cells integrate extremely robust circuits that exhibit significant heterogeneity, but still respond to external stimuli in predictable ways. This phenomenon allows us to introduce a network inference methodology that integrates measurements of protein activation from perturbation experiments. The methodology relies on logic-based networks to provide a predictive approximation of the transfer of signals in a network. The approach presented was validated in silico with a set of test networks and applied to investigate the epidermal growth factor receptor signaling of a breast epithelial cell line, MFC10A. In our analysis, we predict the potential signaling circuitry most likely responsible for the experimental readouts of several proteins in the mitogen-activated protein kinase and phosphatidylinositol-3 kinase pathways. The approach can also be used to identify additional necessary perturbation experiments to distinguish between a set of possible candidate networks.

Abstract 1040: Differential levels of glycogen in breast cancer cell lines: A potential new target

Cancer cells have been known to alter their metabolic processes in order to survive and proliferate. Normally in muscle and liver, excess glucose is stored within the cells as glycogen. Elevated levels of glycogen have also been found in various cancers, including breast cancers. Recent studies have implicated glycogen metabolism as important in promoting survival of cancer cells, suggesting targeting of glycogen metabolism as a possible treatment to inhibit cancer cell growth. In general, modulation of cancer metabolism is believed to be an attractive adjunct strategy to conventional or targeted therapies. Here we set out to investigate glycogen levels as well as levels of proteins involved in glycogen synthesis and degradation vary across different breast cancer cell lines. A glucose metabolism qPCR array found differential levels of the alpha subunit of phosphorylase kinase 1, a key enzyme involved in glycogen degradation among three different breast cancer cell lines. Expression levels of glycogen synthesis and degradation enzymes were assessed using qPCR and immunoblot in various breast cancer cell lines. Glycogen levels in these breast cancer cell lines were quantified using an amyloglucosidase reaction coupled with other enzymatic reactions to produce a fluorescent product. It was found that MDA-MB-231, SUM149, and MCF7 cell lines had increased levels of glycogen, between 6.5 and 23.5 μg glycogen per mg protein, whereas SUM190 and normal-like breast epithelial cell line MCF10A had undetectable levels of glycogen. These findings demonstrate that glycogen metabolism can vary widely amongst cancer types, indicating that therapies targeted to disrupt glycogen degradation may produce differential results and that further study of the role of glycogen metabolism in cancer is warranted. Citation Format: Joel A. Yates, Megan Altemus, Zhifen Wu, Michelle L. Wynn, Sofia D. Merajver. Differential levels of glycogen in breast cancer cell lines: A potential new target. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1040.