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Author

Douglas A. Lauffenburger

Other affiliations: Broad Institute, University of Minnesota, Pfizer  ...read more
Bio: Douglas A. Lauffenburger is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Signal transduction & Epidermal growth factor. The author has an hindex of 122, co-authored 705 publications receiving 55326 citations. Previous affiliations of Douglas A. Lauffenburger include Broad Institute & University of Minnesota.


Papers
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Journal ArticleDOI
09 Feb 1996-Cell
TL;DR: The authors are grateful for financial support from the National Institutes of Health (grants GM23244 and GM53905), and to very helpful comments on the manuscript from Elliot Elson, Vlodya Gelfand, Paul Matsudaira, Julie Theriot, and Sally Zigmond.

3,973 citations

Journal ArticleDOI
22 Apr 2005-Science
TL;DR: Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
Abstract: Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.

1,736 citations

Journal ArticleDOI
06 Feb 1997-Nature
TL;DR: It is shown that changes in cell migration speed resulting from three separate variables—substratum ligand level, cell integrin expression level, and integrin–ligand binding affinity—are all quantitatively predictable through the changes they cause in a single unifying parameter: short-term cell– substratum adhesion strength.
Abstract: Migration of cells in higher organisms is mediated by adhesion receptors, such as integrins, that link the cell to extracellular-matrix ligands, transmitting forces and signals necessary for locomotion. Whether cells will migrate or not on a given substratum, and also their speed, depends on several variables related to integrin-ligand interactions, including ligand levels, integrin levels, and integrin-ligand binding affinities. These and other factors affect the way molecular systems integrate to effect and regulate cell migration. Here we show that changes in cell migration speed resulting from three separate variables-substratum ligand level, cell integrin expression level, and integrin-ligand binding affinity-are all quantitatively predictable through the changes they cause in a single unifying parameter: short-term cell-substratum adhesion strength. This finding is consistent with predictions of a mathematical model for cell migration. The ligand concentration promoting maximum migration speed decreases reciprocally as integrin expression increases. Increases in integrin-ligand affinity similarly result in maximal migration at reciprocally lower ligand concentrations. The maximum speed attainable, however, remains unchanged as ligand concentration, integrin expression, or integrin-ligand affinity vary, suggesting that integrin coupling with intracellular motors remains unaltered.

1,425 citations

Journal ArticleDOI
TL;DR: The experimental findings here represent, to the knowledge, discovery of a previously undescribed set of balances of cell and matrix properties that govern the ability of tumor cells to migration in 3D environments.
Abstract: Cell migration on 2D surfaces is governed by a balance between counteracting tractile and adhesion forces. Although biochemical factors such as adhesion receptor and ligand concentration and binding, signaling through cell adhesion complexes, and cytoskeletal structure assembly/disassembly have been studied in detail in a 2D context, the critical biochemical and biophysical parameters that affect cell migration in 3D matrices have not been quantitatively investigated. We demonstrate that, in addition to adhesion and tractile forces, matrix stiffness is a key factor that influences cell movement in 3D. Cell migration assays in which Matrigel density, fibronectin concentration, and β1 integrin binding are systematically varied show that at a specific Matrigel density the migration speed of DU-145 human prostate carcinoma cells is a balance between tractile and adhesion forces. However, when biochemical parameters such as matrix ligand and cell integrin receptor levels are held constant, maximal cell movement shifts to matrices exhibiting lesser stiffness. This behavior contradicts current 2D models but is predicted by a recent force-based computational model of cell movement in a 3D matrix. As expected, this 3D motility through an extracellular environment of pore size much smaller than cellular dimensions does depend on proteolytic activity as broad-spectrum matrix metalloproteinase (MMP) inhibitors limit the migration of DU-145 cells and also HT-1080 fibrosarcoma cells. Our experimental findings here represent, to our knowledge, discovery of a previously undescribed set of balances of cell and matrix properties that govern the ability of tumor cells to migration in 3D environments.

1,166 citations

Journal ArticleDOI
TL;DR: It is found that NR6 cells can migrate on substrata where adhesion is mediated solely by the YGRGD peptide, and the variation of cell speed with spatial presentation of Y GRGD is mediated via its effect on cell adhesion.
Abstract: Integrin adhesion receptors play a crucial role in regulating interactions between cells and extracellular matrix (ECM). Integrin activation initiates multiple intracellular signaling pathways and results in regulation of cell functions such as motility, proliferation and differentiation. Two key observations regarding the biophysical nature of integrin-mediated cell-matrix interactions motivated the present study: (1) cell motility can be regulated by modulating the magnitude of cell-substratum adhesion, by varying cell integrin expression level, integrin-ECM binding affinity or substratum ECM surface density; and (2) integrin clustering enables assembly of multiple cytoplasmic regulatory and structural proteins at sites of aggregated integrin cytoplasmic domains, activating certain intracellular signalling pathways. Here, using a minimal integrin adhesion ligand, YGRGD, we test the hypothesis that ligand clustering can affect cell migration in a manner related to its modulation of cell-substratum adhesion. We employ a synthetic polymer-linking method, which allows us to independently and systematically vary both the average surface density and the local (approx. 50 nm scale) spatial distribution of the YGRGD peptide, against a background otherwise inert with respect to cell adhesion. In this system, the ligand was presented in three alternative spatial distributions: singly, in clusters with an average of five ligands per cluster, or in clusters with an average of nine ligands per cluster; for each of these spatial distributions, a range of average ligand densities (1,000-200,000 ligands/micrometer(2)) were examined. Cluster spacing was adjusted in order to present equivalent average ligand densities independently of cluster size. The murine NR6 fibroblast cell line was used as a model because its migration behavior on ECM in the presence and absence of growth factors has been well-characterized and it expresses integrins known to interact with the YGRGD peptide. Using time-lapse videomicroscopy and analysis of individual cell movement paths, we find that NR6 cells can migrate on substrata where adhesion is mediated solely by the YGRGD peptide. As previously observed for migration of NR6 cells on fibronectin, migration speed on YGRGD is a function of the average surface ligand density. Strikingly, clustering of ligand significantly reduced the average ligand density required to support cell migration. In fact, non-clustered integrin ligands support cell attachment but neither full spreading nor haptokinetic or chemokinetic motility. In addition, by quantifying the strength of cell-substratum adhesion, we find that the variation of cell speed with spatial presentation of YGRGD is mediated via its effect on cell adhesion. These effects on motility and adhesion are also observed in the presence of epidermal growth factor (EGF), a known motility-regulating growth factor. Variation in YGRGD presentation also affects the organization of actin filaments within the cell, with a greater number of cells exhibiting stress fibers at higher cluster sizes of YGRGD. Our observations demonstrate that cell motility may be regulated by varying ligand spatial presentation at the nanoscale level, and suggest that integrin clustering is required to support cell locomotion.

959 citations


Cited by
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Journal ArticleDOI
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

32,980 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
25 Nov 2009-Cell
TL;DR: The mesenchymal state is associated with the capacity of cells to migrate to distant organs and maintain stemness, allowing their subsequent differentiation into multiple cell types during development and the initiation of metastasis.

8,642 citations