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

Morphogen Transport in Epithelia

We present a general theoretical framework to discuss mechanisms of morphogen transport and gradient formation in a cell layer. Trafficking events on the cellular scale lead to transport on larger scales. We discuss in particular the case of transcytosis where morphogens undergo repeated rounds of internalization into cells and recycling. Based on a description on the cellular scale, we derive effective nonlinear transport equations in one and two dimensions which are valid on larger scales. We derive analytic expressions for the concentration dependence of the effective diffusion coefficient and the effective degradation rate. We discuss the effects of a directional bias on morphogen transport and those of the coupling of the morphogen and receptor kinetics. Furthermore, we discuss general properties of cellular transport processes such as the robustness of gradients and relate our results to recent experiments on the morphogen Decapentaplegic (Dpp) that acts in the fruit fly Drosophila

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

Computational translation of genomic responses from experimental model systems to humans

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human "Translation Problems" defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.Boehringer Ingelheim PharmaceuticalsInstitute for Collaborative Biotechnologies (Grant W911NF-09-0001

Qualitatively Different T Cell Phenotypic Responses to IL-2 versus IL-15 Are Unified by Identical Dependences on Receptor Signal Strength and Duration

IL-2 and IL-15 are common γ-chain family cytokines involved in regulation of T cell differentiation and homeostasis. Despite signaling through the same receptors, IL-2 and IL-15 have non-redundant roles in T cell biology, both physiologically and at the cellular level. The mechanisms by which IL-2 and IL-15 trigger distinct phenotypes in T cells remain elusive. To elucidate these mechanisms, we performed a quantitative comparison of the phosphotyrosine signaling network and resulting phenotypes triggered by IL-2 and IL-15. This study revealed that the signaling networks activated by IL-2 or IL-15 are highly similar and that T cell proliferation and metabolism are controlled in a quantitatively distinct manner through IL-2/15R signal strength independent of the cytokine identity. Distinct phenotypes associated with IL-2 or IL-15 stimulation therefore arise through differential regulation of IL-2/15R signal strength and duration because of differences in cytokine–receptor binding affinity, receptor expression levels, physiological cytokine levels, and cytokine–receptor intracellular trafficking kinetics. These results provide important insights into the function of other shared cytokine and growth factor receptors, quantitative regulation of cell proliferation and metabolism through signal transduction, and improved design of cytokine based clinical immunomodulatory therapies for cancer and infectious diseases.National Institutes of Health (U.S.) (Grant U54CA11927)National Institutes of Health (U.S.) (Grant R01 AI065824)United States. Army Research Office (Institute for Collaborative Biotechnologies Grant W911NF-09-0001

Artificial neural networks enable genome-scale simulations of intracellular signaling

Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling. Many diseases are caused by disruptions to the network of biochemical reactions that allow cells to respond to external signals. Here Nilsson et al develop a method to simulate cellular signaling using artificial neural networks to predict cellular responses and activities of signaling molecules

Robust formation of morphogen gradients

We discuss the formation of graded morphogen profiles in a cell layer by nonlinear transport phenomena, important for patterning developing organisms. We focus on a process termed transcytosis, where morphogen transport results from binding of ligands to receptors on the cell surface, incorporation into the cell and subsequent externalization. Starting from a microscopic model, we derive effective transport equations. We show that, in contrast to morphogen transport by extracellular diffusion, transcytosis leads to robust ligand profiles which are insensitive to the rate of ligand production

Modeling HER2 Effects on Cell Behavior from Mass Spectrometry Phosphotyrosine Data

Cellular behavior in response to stimulatory cues is governed by information encoded within a complex intracellular signaling network. An understanding of how phenotype is determined requires the distributed characterization of signaling processes (e.g., phosphorylation states and kinase activities) in parallel with measures of resulting cell function. We previously applied quantitative mass spectrometry methods to characterize the dynamics of tyrosine phosphorylation in human mammary epithelial cells with varying human epidermal growth factor receptor 2 (HER2) expression levels after treatment with epidermal growth factor (EGF) or heregulin (HRG). We sought to identify potential mechanisms by which changes in tyrosine phosphorylation govern changes in cell migration or proliferation, two behaviors that we measured in the same cell system. Here, we describe the use of a computational linear mapping technique, partial least squares regression (PLSR), to detail and characterize signaling mechanisms responsible for HER2-mediated effects on migration and proliferation. PLSR model analysis via principal component inner products identified phosphotyrosine signals most strongly associated with control of migration and proliferation, as HER2 expression or ligand treatment were individually varied. Inspection of these signals revealed both previously identified and novel pathways that correlate with cell behavior. Furthermore, we isolated elements of the signaling network that differentially give rise to migration and proliferation. Finally, model analysis identified nine especially informative phosphorylation sites on six proteins that recapitulated the predictive capability of the full model. A model based on these nine sites and trained solely on data from a low HER2-expressing cell line a priori predicted migration and proliferation in a HER2-overexpressing cell line. We identify the nine signals as a “network gauge,” meaning that when interrogated together and integrated according to the quantitative rules of the model, these signals capture information content in the network sufficiently to predict cell migration and proliferation under diverse ligand treatments and receptor expression levels. Examination of the network gauge in the context of previous literature indicates that endocytosis and activation of phosphoinositide 3-kinase (PI3K)-mediated pathways together represent particularly strong loci for the integration of the multiple pathways mediating HER2′s control of mammary epithelial cell proliferation and migration. Thus, a PLSR modeling approach reveals critical signaling processes regulating HER2-mediated cell behavior

In Vivo Systems Analysis Identifies Spatial and Temporal Aspects of the Modulation of TNF-α-Induced Apoptosis and Proliferation by MAPKs

Cellular responses to external stimuli depend on dynamic features of multipathway network signaling; thus, cell behavior is influenced in a complex manner by the environment and by intrinsic properties. Methods of multivariate systems analysis have provided an understanding of these convoluted effects, but only for relatively simplified examples in vitro. To determine whether such approaches could be successfully used in vivo, we analyzed the signaling network that determines the response of intestinal epithelial cells to tumor necrosis factor–α (TNF-α). We built data-driven, partial least-squares discriminant analysis (PLSDA) models based on signaling, apoptotic, and proliferative responses in the mouse small intestinal epithelium after systemic exposure to TNF-α. The extracellular signal–regulated kinase (ERK) signaling axis was a critical modulator of the temporal variation in apoptosis at different doses of TNF-α and of the spatial variation in proliferation in distinct intestinal regions. Inhibition of MEK, a mitogen-activated protein kinase kinase upstream of ERK, altered the signaling network and changed the temporal and spatial phenotypes consistent with model predictions. Our results demonstrate the dynamic, adaptive nature of in vivo signaling networks and identify natural, tissue-level variation in responses that can be deconvoluted only with quantitative, multivariate computational modeling. This study lays a foundation for the use of systems-based approaches to understand how dysregulation of the cellular network state underlies complex diseases.National Institute of General Medical Sciences (U.S.) (Grant R01-GM088827)National Cancer Institute (U.S.) (Grant U54-CA112967

Network analysis of differential Ras isoform mutation effects on intestinal epithelial responses to TNF-α

Tumor necrosis factor alpha (TNF-α) is an inflammatory cytokine that can elicit distinct cellular behaviors under different molecular contexts. Mitogen activated protein kinase (MAPK) pathways, especially the extracellular signal-regulated kinase (Erk) pathway, help to integrate influences from the environmental context, and therefore modulate the phenotypic effect of TNF-α exposure. To test how variations in flux through the Erk pathway modulate TNF-α-elicited phenotypes in a complex physiological environment, we exposed mice with different Ras mutations (K-Ras activation, N-Ras activation, and N-Ras ablation) to TNF-α and observed phenotypic and signaling changes in the intestinal epithelium. Hyperactivation of Mek1, an Erk kinase, was observed in the intestine of mice with K-Ras activation and, surprisingly, in N-Ras null mice. Nevertheless, these similar Mek1 outputs did not give rise to the same phenotype, as N-Ras null intestine was hypersensitive to TNF-α-induced intestinal cell death while K-Ras mutant intestine was not. A systems biology approach applied to sample the network state revealed that the signaling contexts presented by these two Ras isoform mutations were different. Consistent with our experimental data, N-Ras ablation induced a signaling network state that was mathematically predicted to be pro-death, while K-Ras activation did not. Further modeling by constrained Fuzzy Logic (cFL) revealed that N-Ras and K-Ras activate the signaling network with different downstream distributions and dynamics, with N-Ras effects being more transient and diverted more towards PI3K-Akt signaling and K-Ras effects being more sustained and broadly activating many pathways. Our study highlights the necessity to consider both environmental and genomic contexts of signaling pathway activation in dictating phenotypic responses, and demonstrates how modeling can provide insight into complex in vivo biological mechanisms, such as the complex interplay between K-Ras and N-Ras in their downstream effects.National Institute of General Medical Sciences (U.S.) (Grant R01-GM088827)National Cancer Institute (U.S.) (U54-CA112967)United States. Army Research Office (Institute for Collaborative Biotechnologies Grant W911NF-09-D-000