1,721,282 research outputs found
Measurement and Modeling of Signaling at the Single-Cell Level
Full text linkIt 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
Modeling and computational analysis of EGF receptor-mediated cell communication in Drosophila oogenesis
No full text: Autocrine signaling through the Epidermal Growth Factor Receptor (EGFR) operates at various stages of development across species. A recent hypothesis suggested that a distributed network of EGFR autocrine loops was capable of spatially modulating a simple single-peaked input into a more complex two-peaked signaling pattern, specifying the formation of a pair organ in Drosophila oogenesis (two respiratory appendages on the eggshell). To test this hypothesis, we have integrated genetic and biochemical information about the EGFR network into a mechanistic model of transport and signaling. The model allows us to estimate the relative spatial ranges and time scales of the relevant feedback loops, to interpret the phenotypic transitions in eggshell morphology and to predict the effects of new genetic manipulations. We have found that the proposed mechanism with a single diffusing inhibitor is sufficient to convert a single-peaked extracellular input into a two-peaked pattern of intracellular signaling. Based on extensive computational analysis, we predict that the same mechanism is capable of generating more complex patterns. At least indirectly, this can be used to account for more complex eggshell morphologies observed in related fly species. We propose that versatility in signaling mediated by autocrine loops can be systematically explored using experiment-based mechanistic models and their analysis
Cancer systems biology: a network modeling perspective
No full textCancer 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.National Cancer Institute (U.S.). Integrative Cancer Biology Program (U54-CA112967-03 to D.A.L.)American Cancer Society (PF-08-026-01-CCG
Quantitative modeling perspectives on the ErbB system of cell regulatory processes
No full textThe complexities of the processes involved in ErbB-mediated regulation of cellular phenotype are broadly appreciated, so much so that it might be reasonably argued that this highly studied system provided significant impetus for the systems perspective on cell signaling processes in general. Recent years have seen major advances in the level of characterization of the ErbB system as well as our ability to make measurements of the system. This new data provides significant new insight, while at the same time creating new challenges for making quantitative statements and predictions with certainty. Here, we discuss recent advances in each of these directions and the interplay between them, with a particular focus on quantitative modeling approaches to interpret data and provide predictive power. Our discussion follows the sequential order of ErbB pathway activation, beginning with considerations of receptor/ligand interactions and dynamics, proceeding to the generation of intracellular signals, and ending with determination of cellular phenotype. As discussed herein, these processes become increasingly difficult to describe or interpret in terms of traditional models, and we review emerging methodologies to address this complexity.National Cancer Institute (U.S.). Integrative Cancer Biology Program (U54-CA112967)National Cancer Institute (U.S.) (R01-CA096504
Quantitative analysis of gradient sensing: towards building predictive models of chemotaxis in cancer
Full text linkChemotaxis 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
Physiome-on-a-Chip: The Challenge of “Scaling” in Design, Operation, and Translation of Microphysiological Systems
Full text linkScaling of a microphysiological system (MPS) or physiome-on-a-chip is arguably two interrelated, modeling-based activities: on-platform scaling and in vitro-in vivo translation. This dual approach reduces the need to perfectly rescale and mimic in vivo physiology, an aspiration that is both extremely challenging and not substantively meaningful because of uncertain relevance of any specific physiological condition. Accordingly, this perspective offers a tractable approach for designing interacting MPSs and relating in vitro results to analogous context in vivo.United States. Defense Advanced Research Projects Agency. Microphysiological Systems Program (Grant W911NF-12-2-0039)National Institutes of Health (U.S.) Microphysiological Systems Program (Grant 4-UH3-TR000496-03)United States. Army Research Office (Institute for Collaborative Biotechnologies. Grant W911NF-09- 0001
Models of signalling networks - what cell biologists can gain from them and give to them
No full textComputational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.National Institutes of Health (U.S.) (Director's New Innovator Award Program grant number 1-DP2-OD006464)American Cancer Society (grant number 120668-RSG-11-047-01-DMC)Pew Charitable Trusts (Pew Scholars Program in the Biomedical Sciences)David & Lucile Packard FoundationNational Institutes of Health (U.S.) (NCI Integrative Cancer Biology Program, grant U54-CA112967)National Institutes of Health (U.S.) (NCI Integrative Cancer Biology Program, R24-DK090963)National Institutes of Health (U.S.) (NCI Integrative Cancer Biology Program, grant R01-EB010246
Synergistic Communication between CD4+ T Cells and Monocytes Impacts the Cytokine Environment
Full text linkPhysiological cytokine environments arise from factors produced by diverse cell types in coordinated concert. Understanding the contributions of each cell type in the context of cell-cell communication is important for effectively designing disease modifying interventions. Here, we present multi-plexed measurement of 48 cytokines from a coculture system of primary human CD4+ T cells and monocytes across a spectrum of stimuli and for a range of relative T cell/monocyte compositions, coupled with corresponding measurements from PBMCs and plasma from the same donors. Computational analysis of the resulting data-sets elucidated communication-independent and communication-dependent contributions, including both positive and negative synergies. We find that cytokines in cell supernatants were uncorrelated to those found in plasma. Additionally, as an example of positive synergy, production levels of CXCR3 cytokines IP-10 and MIG, depend non-linearly on both IFNγ and TNFα levels in cross-talk between T cells and monocytes. Overall, this work demonstrates that communication between cell types can significantly impact the consequent cytokine environment, emphasizing the value of mixed cell population studies.United States. National Institutes of Health (DP3 DK097681)Institute for Collaborative Biotechnologies (W911NF-09-0001)David H. Koch Institute for Integrative Cancer Research at MITNational Science Foundation (U.S.
In vivo systems biology approaches to chronic immune/inflammatory pathophysiology
No full textThe Authors Systems biology offers an emphasis on integrative computational analysis of complex multi-component processes to enhance capability for predictive insights concerning operation of those processes. The immune system represents a prominent arena in which such processes are manifested for vital roles in physiology and pathology, encompassing dozens of cell types and hundreds of reciprocal interactions. Chronic, debilitating pathologies involving immune system dysregulation have become recognized as increasing in incidence over recent decades. While clinical consequences of immune dysregulation in such pathologies are well characterized, treatment options remain limited and focus on ameliorating symptoms. Because it is difficult to recapitulate more than a severely limited facet of the immune system in vitro, application of systems biology approaches to autoimmune and inflammatory pathophysiology in vivo has opened a new door toward discerning disease sub-groups and developing associated stratification strategies for patient treatment. In particular, early instances of these approaches have demonstrated advances in uncovering previously under-appreciated dysregulation of signaling networks between immune system and tissue cells, raising promise for improving upon current therapeutic approaches.United States. Army Research Office (Grant W911NF-09-0001)National Cancer Institute (U.S.) (Grant U01-CA215798
Molecular Pathways: Receptor Ectodomain Shedding in Treatment, Resistance, and Monitoring of Cancer
No full textProteases known as sheddases cleave the extracellular domains of their substrates from the cell surface. The A Disintegrin and Metalloproteinases ADAM10 and ADAM17 are among the most prominent sheddases, being widely expressed in many tissues, frequently overexpressed in cancer, and promiscuously cleaving diverse substrates. It is increasingly clear that the proteolytic shedding of transmembrane receptors impacts pathophysiology and drug response. Receptor substrates of sheddases include the cytokine receptors TNFR1 and IL6R; the Notch receptors; type-I and -III TGFβ receptors; receptor tyrosine kinases (RTK) such as HER2, HER4, and VEGFR2; and, in particular, MET and TAM-family RTKs AXL and Mer (MerTK). Activation of receptor shedding by mechanical cues, hypoxia, radiation, and phosphosignaling offers insight into mechanisms of drug resistance. This particularly holds for kinase inhibitors targeting BRAF (such as vemurafenib and dabrafenib) and MEK (such as trametinib and cobimetinib), along with direct sheddase inhibitors. Receptor proteolysis can be detected in patient fluids and is especially relevant in melanoma, glioblastoma, lung cancer, and triplenegative breast cancer where RTK substrates, MAPK signaling, and ADAMs are frequently dysregulated. Translatable strategies to exploit receptor shedding include combination kinase inhibitor regimens, recombinant decoy receptors based on endogenous counterparts, and, potentially, immunotherapy.National Cancer Institute (U.S.) (Grant K99-CA207744)National Cancer Institute (U.S.) (Grant R01-CA96504)National Cancer Institute (U.S.) (Grant U54-CA112967
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