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      A Six‐Stage Workflow for Robust Application of Systems Pharmacology

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          Abstract

          Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

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          A methodology for performing global uncertainty and sensitivity analysis in systems biology.

          Simeone Marino, Ian B Hogue, Christian J Ray … (2008)
          Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.
          Article 0 comments Cited 760 times – based on 0 reviews     Review now
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            Causal protein-signaling networks derived from multiparameter single-cell data.

            Karen Sachs, Omar Perez, Dana Pe'er … (2005)
            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.
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              The Systems Biology Graphical Notation.

              Nicolas Le Novère, Michael Hucka, Huaiyu Mi … (2009)
              Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
              Article 0 comments Cited 351 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): CPT Pharmacometrics Syst Pharmacol
                Journal ID (iso-abbrev): CPT Pharmacometrics Syst Pharmacol
                Journal ID (doi): 10.1002/(ISSN)2163-8306
                Journal ID (publisher-id): PSP4
                Title: CPT: Pharmacometrics & Systems Pharmacology
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Electronic): 2163-8306
                Publication date (Electronic): 16 April 2016
                Publication date (Print): May 2016
                Volume: 5
                Issue: 5 ( doiID: 10.1002/psp4.v5.5 )
                Pages: 235-249
                Affiliations
                [ 1 ] Translational & Systems PharmacologyPKPD, Genentech South San Francisco CaliforniaUSA
                [ 2 ] Department of Pharmaceutical SciencesUniversity at Buffalo, SUNY Buffalo, New York
                [ 3 ] Division of PharmacologyLeiden Academic Centre for Drug Research, Leiden University LeidenThe Netherlands
                [ 4 ]Certara QSP CanterburyUK
                Author notes
                [*] [* ]Correspondence: S Ramanujan ( ramanujan.saroja@ 123456gene.com )
                Article
                Publisher ID: PSP412071
                DOI: 10.1002/psp4.12071
                PMC ID: 4879472
                PubMed ID: 27299936
                SO-VID: f05d99c3-6754-4d29-8205-4a5f774f6879
                Copyright © © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics
                License:

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                Date received : 08 December 2015
                Date accepted : 18 February 2016
                Page count
                Pages: 15
                Categories
                Subject: Tutorial
                Subject: Tutorial
                Custom metadata
                source-schema-version-number 2.0
                component-id psp412071
                cover-date May 2016
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.1 mode:remove_FC converted:14.06.2016

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