Bioengineering models of cell signaling

Strategies for rationally manipulating cell behavior in cell-based technologies and molecular therapeutics and understanding effects of environmental agents on physiological systems may be derived from a mechanistic understanding of underlying signaling mechanisms that regulate cell functions. Three crucial attributes of signal transduction necessitate modeling approaches for analyzing these systems: an ever-expanding plethora of signaling molecules and interactions, a highly interconnected biochemical scheme, and concurrent biophysical regulation. Because signal flow is tightly regulated with positive and negative feedbacks and is bidirectional with commands traveling both from outside-in and inside-out, dynamic models that couple biophysical and biochemical elements are required to consider information processing both during transient and steady-state conditions. Unique mathematical frameworks will be needed to obtain an integrated perspective on these complex systems, which operate over wide length and time scales. These may involve a two-level hierarchical approach wherein the overall signaling network is modeled in terms of effective "circuit" or "algorithm" modules, and then each module is correspondingly modeled with more detailed incorporation of its actual underlying biochemical/biophysical molecular interactions

Climatological, Hydrological, and Economic Analysis of Agriculture in Montana and the Western U.S.A.

Many studies have addressed the impact of climate on agriculture; however, fewer studies addressed how farmers adapt to climate change, to what extent implementation of adaptation strategies mitigates economic losses or alters the hydrologic system. Analyses of how historical climate affected not only farmer decision making, but also the economic and hydrological consequences of farmers’ adaptations to climate variations, and projections of the spatiotemporal climatic regimes at finer regional scales are critical for aiding in actionable climate change adaptations. This dissertation helps fill knowledge gaps on the impacts of climate change in rural regions of the agricultural western U.S.A. and provides a baseline to understand what crops farmers in the region will prioritize under future climates, and what will be the economic and hydrologic costs of adaptation. The first project modeled producer behavior under end-of-century climate projections. We applied a stochastic, integrated hydro-economic model that simulates land and water allocations to analyze Montana farmer adaptations to a range of projected climate conditions and the response of the hydrologic system to those adaptations. Results show a state-wide increase in agricultural water use leading to decreased summer streamflows. Land use for irrigated crops increased while rainfed crops decreased, implying state-level decrease in planted area. Both irrigated and rainfed crop production and farmer revenue decreased. The second project used historical data to quantify the climate water deficit (CWD) threshold where farmers’ perception swings towards repurposing crops instead of harvesting for grain. We analyzed the relationship between crop repurposing (the ratio of acres harvested for grain to the total planted acres) to seasonal CWD, and to isolate the climate signal from economic factors, our analysis accounted for the influence of crop prices on grain harvest. Results indicate that farmers are less likely to harvest barley and spring wheat for grain when the spring CWD is above average. For the majority of major crop growing regions, grain prices increased with lower levels of grain harvest. The third project used the most current climate change forecasts to predict future climate regimes of important rainfed winter wheat growing regions and compare current yields of climate analog regions. Using a suite of climate models, we evaluate which model(s) best simulated seasonal historical distributions of five climatic variables using the energy distance statistical metric, then use the best performing models to predict and map mid-century climate analog locations across the western U.S.A. Results show significant western and/or southern shifts in analog locations, regardless of season. These shifts to warmer, dryer regions do not conclusively imply decreased yields, however land use devoted to rainfed winter wheat in analog regions was dramatically lower

Measurement and Modeling of Signaling at the Single-Cell Level

It has long been recognized that a deeper understanding of cell function, with respect to execution of phenotypic behaviors and their regulation by the extracellular environment, is likely to be achieved by analyzing the underlying molecular processes for individual cells selected from across a population, rather than averages of many cells comprising that population. In recent years, experimental and computational methods for undertaking these analyses have advanced rapidly. In this review, we provide a perspective on both measurement and modeling facets of biochemistry at a single-cell level. Our central focus is on receptor-mediated signaling networks that regulate cell phenotypic functions.David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Fellowship)National Institutes of Health (U.S.) (grant R01-EB010246)National Institutes of Health (U.S.) (grant P50-GM68762)United States. Army Research Office (Institute for Collaborative Biotechnologies, Grant W911NF-09-0001

Dabigatran and Warfarin for Stroke Prevention in Atrial Fibrillation: Use, Switching, and Clinical Effects Following New Market Entry in Real-World Patients

Patients with atrial fibrillation frequently benefit from anticoagulation to prevent stroke and systemic embolism. For decades, warfarin was the primary oral anticoagulant option despite its narrow therapeutic index requiring monitoring and drug-drug interactions. Dabigatran’s recent availability provides practical advantages including no monitoring and fewer interactions; however, it lacks a convenient reversal agent for bleeding events. Currently, it is unclear what factors have driven anticoagulant utilization since dabigatran’s introduction, and little real-world evidence on the agents’ comparative effectiveness and safety is available. The objectives were to describe dabigatran and warfarin’s utilization and switching patterns and assess their comparative effectiveness and safety. A cohort of non-valvular atrial fibrillation patients initiating anticoagulation from a large US database of commercial and Medicare supplement claims from 2009-2012 was extracted. We first examined factors associated with anticoagulant selection using a retrospective cohort design and multivariable regression. We then evaluated the effectiveness and safety of dabigatran compared with warfarin using multivariable Cox proportional hazards regression and propensity score weighting. Finally, we evaluated the clinical effects of switching anticoagulants compared with non-switching using a time-varying exposure design and multivariable Cox proportional hazards regression. Of the 64,935 patients included in the cohort, 32.5% used dabigatran. Dabigatran users were less likely to have high ischemic stroke or bleeding risk or other clinical comorbidities. Switching anticoagulation was also less frequent among patients with higher ischemic stroke or bleeding risk. Dabigatran was associated with a lower risk of ischemic stroke or venous thromboembolism, and no relation was seen between anticoagulant and harmful outcomes including bleeding events or acute myocardial infarction. However, dabigatran was also associated with a higher risk of gastrointestinal bleeding. Compared with non-switchers, no relation was seen between switching anticoagulants and an increased risk of stroke, systemic embolism, bleeding events, or myocardial infarction. Despite the rapid uptake of dabigatran, these results highlight that patients initiating dabigatran were generally healthier than those initiating warfarin. Dabigatran may be considered a safe and possibly more effective alternative to warfarin in patients with atrial fibrillation; despite encouraging results from the observed lack of increased adverse outcomes from switching anticoagulants, caution is still recommended

Quantitative analysis of gradient sensing: towards building predictive models of chemotaxis in cancer

Chemotaxis of tumor cells in response to a gradient of extracellular ligand is an important step in cancer metastasis. The heterogeneity of chemotactic responses in cancer has not been widely addressed by experimental or mathematical modeling techniques. However, recent advancements in chemoattractant presentation, fluorescent-based signaling probes, and phenotypic analysis paradigms provide rich sources for building data-driven relational models that describe tumor cell chemotaxis in response to a wide variety of stimuli. Here we present gradient sensing, and the resulting chemotactic behavior, in a ‘cue-signal-response’ framework and suggest methods for utilizing recently reported experimental methods in data-driven modeling ventures.United States. Dept. of Defense. Breast Cancer Research Program (U.S.) (Fellowship BC087781)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM081336

Cellular Ability to Sense Spatial Gradients in the Presence of Multiple Competitive Ligands

Many eukaryotic and prokaryotic cells can exhibit remarkable sensing ability under small gradient of chemical compound. In this study, we approach this phenomenon by considering the contribution of multiple ligands to the chemical kinetics within Michaelis-Menten model. This work was inspired by the recent theoretical findings from Bo Hu et al. [Phys. Rev. Lett. 105, 048104 (2010)], our treatment with practical binding energies and chemical potential provides the results which are consistent with experimental observations.Comment: 5 pages, 4 figure

Cancer systems biology: a network modeling perspective

Cancer is now appreciated as not only a highly heterogenous pathology with respect to cell type and tissue origin but also as a disease involving dysregulation of multiple pathways governing fundamental cell processes such as death, proliferation, differentiation and migration. Thus, the activities of molecular networks that execute metabolic or cytoskeletal processes, or regulate these by signal transduction, are altered in a complex manner by diverse genetic mutations in concert with the environmental context. A major challenge therefore is how to develop actionable understanding of this multivariate dysregulation, with respect both to how it arises from diverse genetic mutations and to how it may be ameliorated by prospective treatments. While high-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic and metabolomic profiling are now commonly used for molecular-level characterization of tumor cells and surrounding tissues, the resulting data sets defy straightforward intuitive interpretation with respect to potential therapeutic targets or the effects of perturbation. In this review article, we will discuss how significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments and effects of molecular therapeutics

Prioritisation and Network Analysis of Crohn's Disease Susceptibility Genes

Recent Genome-Wide Association Studies (GWAS) have revealed numerous Crohn's disease susceptibility genes and a key challenge now is in understanding how risk polymorphisms in associated genes might contribute to development of this disease. For a gene to contribute to disease phenotype, its risk variant will likely adversely communicate with a variety of other gene products to result in dysregulation of common signaling pathways. A vital challenge is to elucidate pathways of potentially greatest influence on pathological behaviour, in a manner recognizing how multiple relevant genes may yield integrative effect. In this work we apply mathematical analysis of networks involving the list of recently described Crohn's susceptibility genes, to prioritise pathways in relation to their potential development of this disease. Prioritisation was performed by applying a text mining and a diffusion based method (GRAIL, GPEC). Prospective biological significance of the resulting prioritised list of proteins is highlighted by changes in their gene expression levels in Crohn's patients intestinal tissue in comparison with healthy donors.United States. Army Research Office (Institute for Collaborative Biotechnologies Contract W911NF-09-D-0001

Time and length scales of autocrine signals in three dimensions

A model of autocrine signaling in cultures of suspended cells is developed on the basis of the effective medium approximation. The fraction of autocrine ligands, the mean and distribution of distances traveled by paracrine ligands before binding, as well as the mean and distribution of the ligand lifetime are derived. Interferon signaling by dendritic immune cells is considered as an illustration.Comment: 15 page