Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data

Sachs, Karen; Perez, Omar D.; Pe’er, Dana; Lauffenburger, Douglas A.; Nolan, Garry P.

doi:10.1126/science.1105809

Cited by 1,395 publications

(1,514 citation statements)

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Section: Statistical Influence Networkmentioning

confidence: 99%

“…Since not all causal relationships can be inferred from correlation data, meaning that there can be different directed graphs that explain the data equally well, intervention experiments where genes are manipulated by overexpression or deletion have been proposed to learn networks [47]. The Bayesian network formalism has also been used to infer signaling networks from multicolor flow cytometry data [48].There exist a number of other approaches for inferring large-scale molecular regulatory networks from high-throughput data sets. One example is a method, called the Inferelator, that selects the most likely regulators of a given gene using a nonlinear model that can incorporate combinatorial nonlinear influences of a regulator on target gene expression, coupled with a sparse regression approach to avoid overfitting [49].…”

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Biochemical and statistical network models for systems biology

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Shmulevich

2007

Current Opinion in Biotechnology

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IntroductionThe creation of models of the integrated functions of genes and proteins in cells is of fundamental and immediate importance to the emerging field of computational systems biology. Some of the most successful attempts at cell-scale modeling to date have been based on piecing together networks that represent hundreds of experimentally-determined biochemical interactions, while others have been very successful at inferring statistical networks from large amounts of high-throughput data. These networks (metabolic, regulatory, or signaling) can be analyzed, and predictions about cellular behavior made and tested. Many types of models have been built and applied to study cellular behavior and in this review we focus on two broad types: biochemical network models and statistical inference models. Through iterative model prediction, experimentation, and network refinement, the molecular circuitry and functions of biological networks can be elucidated. The construction of genomescale models that integrate the myriad components that produce cellular behavior is a fundamental goal of systems biology today. Biochemical Reaction NetworksBiochemical reaction networks represent the underlying chemistry of the system, and thus at a minimum represent stoichiometric relationships between inter-converted biomolecules. The stoichiometry of biochemical reaction networks can now be reconstructed at the genome-scale, and at smaller scale with sufficient detail to generate kinetic models. These biochemical reaction networks represent many years of accumulated experimental data and can be interrogated in silico to determine their functional states. Genome-scale models based on biochemical networks provide a comprehensive, yet concise, description of cellular functions.For metabolism the reconstruction of the biochemical reaction network is a well-established procedure [1][2][3][4][5][6][7], while methods for the reconstruction of the associated regulatory [8,9] and signaling networks [10][11][12] with stoichiometric detail are being developed. The typically used formalism is to reconstruct the stoichiometric matrix, where each row represents a molecular compound and each column represents a reaction. For metabolism, these networks are often focused on just the metabolites, where the existence of a protein that catalyzes this reaction is used to allow that reaction to be present in the network. It is also possible (and truer to the realities in the system) to represent the proteins themselves as compounds in the network, which enables the integration of proteomics, metabolomics, and flux data (Figure 1). For regulatory and signaling networks, the inclusion of the proteins as compounds is essential. This process * To whom correspondence should be addressed: ishmulevich@sytemsbiology.org. + Present Address: Department of Chemical and Biomolecular Engineering University of Illinois, Urbana-Champaign Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our c...

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Section: Statistical Influence Networkmentioning

confidence: 99%

mentioning

confidence: 99%

Biochemical and statistical network models for systems biology

Price

Shmulevich

2007

Current Opinion in Biotechnology

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“…Top left: measurement output window demonstrating the y0, x0, slope, and intercept results of each sample. This data table can also be exported into Microsoft Excel inferring inter-pathway regulatory relationships among data sets and for the discovery of previously unknown novel molecular interactions [56]. The identification of probabilistic dependence relationships is particularly important in the discovery of new therapeutic targets, particularly in the phosphoproteomic setting, as inhibition of any one endpoint may have significant consequential effects in other pathways.…”

Section: Analysis Of Protein-protein Interactions: Bayesian Networkmentioning

confidence: 99%

“…This methodology is superior in capturing interactions among variables [57]. It has been successfully applied to flow cytometry [56] and could be a valuable technique for deducing points of intervention in the complex phosphoproteomic circuitry derived from protein microarrays.…”

Section: Analysis Of Protein-protein Interactions: Bayesian Networkmentioning

confidence: 99%

Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer

et al. 2006

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The human proteome, due to the enormity of post-translational permutations that result in large numbers of isoforms, is much more complex than the genome and alterations in cancer can occur in ways that are not predictable by translational analysis alone. Proteomic analysis therefore represents a more direct way of investigating disease at the individual patient level. Furthermore, since most novel therapeutic targets are proteins, proteomic analysis potentially has a central role in patient care. At the same time, it is becoming clear that mapping entire networks rather than individual markers may be necessary for robust diagnostics as well as tailoring of therapy. Consequently, there is a need for high-throughput multiplexed proteomic techniques, with the capability of scanning multiple cases and analysing large numbers of endpoints. New types of protein arrays combined with advanced bioinformatics are currently being used to identify molecular signatures of individual tumours based on protein pathways and signalling cascades. It is envisaged that analysing the cellular 'circuitry' of ongoing molecular networks will become a powerful clinical tool in patient management.

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“…This data is often analyzed by clustering over different experiments of wholegenome expression profiles, and that technique has provided important insights into gene function [2]. However, clustering alone cannot resolve gene interactions, and progress in network identification algorithms has revealed aspects of the static wiring of gene networks [3][4][5][6][7][8][9][10][11]. A recent study by Luscombe and colleagues [8] provided a first step towards an understanding of network dynamics by describing when different sub-networks are active during different cellular conditions in Yeast.…”

Section: Introductionmentioning

confidence: 99%