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n2:pmcid: PMC0
bibo:doi: 10.1038%2Fmsb.2009.87
n5:contains: _:vb6525926 _:vb6525894 _:vb6525908 _:vb6525860

Subject Item: _:vb6525860
rdf:type: n5:Section
dc:title: introduction
n5:contains: _:vb6525892 _:vb6525893 _:vb6525888 _:vb6525889 _:vb6525890 _:vb6525891 _:vb6525876 _:vb6525877 _:vb6525878 _:vb6525879 _:vb6525872 _:vb6525873 _:vb6525874 _:vb6525875 _:vb6525884 _:vb6525885 _:vb6525886 _:vb6525887 _:vb6525880 _:vb6525881 _:vb6525882 _:vb6525883 _:vb6525861 _:vb6525862 _:vb6525863 _:vb6525868 _:vb6525869 _:vb6525870 _:vb6525871 _:vb6525864 _:vb6525865 _:vb6525866 _:vb6525867

Subject Item: _:vb6525861
rdf:type: n2:Context
rdf:value: signalling proteins, and transcription factors mediating the responses of cells to intra- and extracellular ligands has generated a wealth of information about the biochemistry of signal transduction (Hanahan and Weinberg, >>2000<<). However, accumulation of molecular detail does not automatically yield improved understanding of the ways in which signalling circuits process complementary and opposing inputs to control diverse physiological responses.
n2:mentions: n3:10647931

Subject Item: _:vb6525862
rdf:type: n2:Context
rdf:value: One approach to organizing data on large groups of genes and proteins is to create interaction networks using either of two related methods (Pieroni et al, >>2008<<; Cusick et al, 2009).
n2:mentions: n3:18297653

Subject Item: _:vb6525863
rdf:type: n2:Context
rdf:value: One approach to organizing data on large groups of genes and proteins is to create interaction networks using either of two related methods (Pieroni et al, 2008; Cusick et al, >>2009<<). One infers connectivity directly from systematic two-hybrid, affinity purification/mass spectrometry and related high-throughput data (Rual et al, 2005), and the second culls interactions from the literature (Bauer-Mehren et al, 2009).
n2:mentions: n3:19116613

Subject Item: _:vb6525864
rdf:type: n2:Context
rdf:value: One infers connectivity directly from systematic two-hybrid, affinity purification/mass spectrometry and related high-throughput data (Rual et al, >>2005<<), and the second culls interactions from the literature (Bauer-Mehren et al, 2009).
n2:mentions: n3:16189514

Subject Item: _:vb6525865
rdf:type: n2:Context
rdf:value: One infers connectivity directly from systematic two-hybrid, affinity purification/mass spectrometry and related high-throughput data (Rual et al, 2005), and the second culls interactions from the literature (Bauer-Mehren et al, >>2009<<). Literature curation can be performed by expert readers or automatically using ‘bibliome' mining software (Zhou and He, 2008).
n2:mentions: n3:19638971

Subject Item: _:vb6525866
rdf:type: n2:Context
rdf:value: org); see Pathguide for a comprehensive list (Bader et al, >>2006<<)) or in proprietary softwares from companies such as Ingenuity (Redwood City, CA, USA).
n2:mentions: n3:16381921

Subject Item: _:vb6525867
rdf:type: n2:Context
rdf:value: As outlined by Pieroni et al (>>2008<<), protein node-edge graphs can be classified into two families:
n2:mentions: n3:18297653

Subject Item: _:vb6525868
rdf:type: n2:Context
rdf:value: PINs are usually created using data from bibliome mining (Chatr-Aryamontri et al, >>2007<<; Kerrien et al, 2007), large-scale affinity purification (Köcher and Superti-Furga, 2007), protein arrays (MacBeath and Schreiber, 2000), and two-hybrid screening (Rual et al, 2005) or genetic interactions (Jansen et al, 2003), whereas
n2:mentions: n3:17135203

Subject Item: _:vb6525869
rdf:type: n2:Context
rdf:value: PINs are usually created using data from bibliome mining (Chatr-Aryamontri et al, 2007; Kerrien et al, >>2007<<), large-scale affinity purification (Köcher and Superti-Furga, 2007), protein arrays (MacBeath and Schreiber, 2000), and two-hybrid screening (Rual et al, 2005) or genetic interactions (Jansen et al, 2003), whereas PSNs are most commonly
n2:mentions: n3:17145710

Subject Item: _:vb6525870
rdf:type: n2:Context
rdf:value: PINs are usually created using data from bibliome mining (Chatr-Aryamontri et al, 2007; Kerrien et al, 2007), large-scale affinity purification (Köcher and Superti-Furga, >>2007<<), protein arrays (MacBeath and Schreiber, 2000), and two-hybrid screening (Rual et al, 2005) or genetic interactions (Jansen et al, 2003), whereas PSNs are most commonly assembled by expert annotation of the literature (Ma'ayan et al,
n2:mentions: n3:17901870

Subject Item: _:vb6525871
rdf:type: n2:Context
rdf:value: PINs are usually created using data from bibliome mining (Chatr-Aryamontri et al, 2007; Kerrien et al, 2007), large-scale affinity purification (Köcher and Superti-Furga, 2007), protein arrays (MacBeath and Schreiber, >>2000<<), and two-hybrid screening (Rual et al, 2005) or genetic interactions (Jansen et al, 2003), whereas PSNs are most commonly assembled by expert annotation of the literature (Ma'ayan et al, 2005).
n2:mentions: n3:10976071

Subject Item: _:vb6525872
rdf:type: n2:Context
rdf:value: data from bibliome mining (Chatr-Aryamontri et al, 2007; Kerrien et al, 2007), large-scale affinity purification (Köcher and Superti-Furga, 2007), protein arrays (MacBeath and Schreiber, 2000), and two-hybrid screening (Rual et al, >>2005<<) or genetic interactions (Jansen et al, 2003), whereas PSNs are most commonly assembled by expert annotation of the literature (Ma'ayan et al, 2005).
n2:mentions: n3:16189514

Subject Item: _:vb6525873
rdf:type: n2:Context
rdf:value: et al, 2007; Kerrien et al, 2007), large-scale affinity purification (Köcher and Superti-Furga, 2007), protein arrays (MacBeath and Schreiber, 2000), and two-hybrid screening (Rual et al, 2005) or genetic interactions (Jansen et al, >>2003<<), whereas PSNs are most commonly assembled by expert annotation of the literature (Ma'ayan et al, 2005).
n2:mentions: n3:14564010

Subject Item: _:vb6525874
rdf:type: n2:Context
rdf:value: protein arrays (MacBeath and Schreiber, 2000), and two-hybrid screening (Rual et al, 2005) or genetic interactions (Jansen et al, 2003), whereas PSNs are most commonly assembled by expert annotation of the literature (Ma'ayan et al, >>2005<<). However, PSNs can also be assembled using ‘reverse engineering' methods such as Bayesian network analysis (Sachs et al, 2005) or inferred systems of differential equations (Nelander et al, 2008).
n2:mentions: n3:16099987

Subject Item: _:vb6525875
rdf:type: n2:Context
rdf:value: However, PSNs can also be assembled using ‘reverse engineering' methods such as Bayesian network analysis (Sachs et al, >>2005<<) or inferred systems of differential equations (Nelander et al, 2008).
n2:mentions: n3:15845847

Subject Item: _:vb6525876
rdf:type: n2:Context
rdf:value: The utility of PINs and PSNs is increased by incorporation of Gene Ontology (GO) tags ((Harris et al, >>2004<<) and information from the KEGG database (Kanehisa et al, 2004).
n2:mentions: n3:14681407

Subject Item: _:vb6525877
rdf:type: n2:Context
rdf:value: The utility of PINs and PSNs is increased by incorporation of Gene Ontology (GO) tags ((Harris et al, 2004) and information from the KEGG database (Kanehisa et al, >>2004<<). Nodes and edges can also be referenced to standardized ontologies such as BioPAX. The topologies of PINs and PSNs have been studied from an information-theoretic standpoint, with the goal of extracting principles of network design
n2:mentions: n3:14681412

Subject Item: _:vb6525878
rdf:type: n2:Context
rdf:value: The topologies of PINs and PSNs have been studied from an information-theoretic standpoint, with the goal of extracting principles of network design (Barabási and Oltvai, >>2004<<; Pieroni et al, 2008).
n2:mentions: n3:14735121

Subject Item: _:vb6525879
rdf:type: n2:Context
rdf:value: The topologies of PINs and PSNs have been studied from an information-theoretic standpoint, with the goal of extracting principles of network design (Barabási and Oltvai, 2004; Pieroni et al, >>2008<<). Moreover, overlay of expression data on PINs and PSNs makes it possible to explore differential activation of sub-networks in various conditions and cell types (Luscombe et al, 2004; Bossi and Lehner, 2009); annotating PINs with data
n2:mentions: n3:18297653

Subject Item: _:vb6525880
rdf:type: n2:Context
rdf:value: Moreover, overlay of expression data on PINs and PSNs makes it possible to explore differential activation of sub-networks in various conditions and cell types (Luscombe et al, >>2004<<; Bossi and Lehner, 2009); annotating PINs with data has proven useful in predicting outcomes in breast cancer patients (Taylor et al, 2009).
n2:mentions: n3:15372033

Subject Item: _:vb6525881
rdf:type: n2:Context
rdf:value: Moreover, overlay of expression data on PINs and PSNs makes it possible to explore differential activation of sub-networks in various conditions and cell types (Luscombe et al, 2004; Bossi and Lehner, >>2009<<); annotating PINs with data has proven useful in predicting outcomes in breast cancer patients (Taylor et al, 2009).
n2:mentions: n3:19357639

Subject Item: _:vb6525882
rdf:type: n2:Context
rdf:value: differential activation of sub-networks in various conditions and cell types (Luscombe et al, 2004; Bossi and Lehner, 2009); annotating PINs with data has proven useful in predicting outcomes in breast cancer patients (Taylor et al, >>2009<<).
n2:mentions: n3:19182785

Subject Item: _:vb6525883
rdf:type: n2:Context
rdf:value: When the number of species in the network is large (tens to hundreds), parameter estimation becomes very challenging (Aldridge et al, >>2006<<). An alternative approach is to depict the pathway as a logical model in which gates specify how outputs are related to inputs. Two-state discrete (Boolean) logic is the simplest logical representation and has no free parameters: logical
n2:mentions: n3:17060902

Subject Item: _:vb6525884
rdf:type: n2:Context
rdf:value: Boolean modelling has previously been applied to biological regulatory and signalling networks (Kauffman, >>1969<<; Thomas and D'Ari, 1990; Huang and Ingber, 2000; Thomas and Kaufman, 2001; de Jong, 2002; Chaves et al, 2005; Fauré et al, 2006; Fisher and Henzinger, 2007; Gupta et al, 2007; Saez-Rodriguez et al, 2007; Zhang et al, 2008; Samaga et al,
n2:mentions: n3:5803332

Subject Item: _:vb6525885
rdf:type: n2:Context
rdf:value: Boolean modelling has previously been applied to biological regulatory and signalling networks (Kauffman, 1969; Thomas and D'Ari, 1990; Huang and Ingber, >>2000<<; Thomas and Kaufman, 2001; de Jong, 2002; Chaves et al, 2005; Fauré et al, 2006; Fisher and Henzinger, 2007; Gupta et al, 2007; Saez-Rodriguez et al, 2007; Zhang et al, 2008; Samaga et al, 2009), but a significant challenge in modelling
n2:mentions: n3:11082279

Subject Item: _:vb6525886
rdf:type: n2:Context
rdf:value: Boolean modelling has previously been applied to biological regulatory and signalling networks (Kauffman, 1969; Thomas and D'Ari, 1990; Huang and Ingber, 2000; Thomas and Kaufman, 2001; de Jong, >>2002<<; Chaves et al, 2005; Fauré et al, 2006; Fisher and Henzinger, 2007; Gupta et al, 2007; Saez-Rodriguez et al, 2007; Zhang et al, 2008; Samaga et al, 2009), but a significant challenge in modelling biochemical pathways using Boolean logic
n2:mentions: n3:11911796

Subject Item: _:vb6525887
rdf:type: n2:Context
rdf:value: Boolean modelling has previously been applied to biological regulatory and signalling networks (Kauffman, 1969; Thomas and D'Ari, 1990; Huang and Ingber, 2000; Thomas and Kaufman, 2001; de Jong, 2002; Chaves et al, >>2005<<; Fauré et al, 2006; Fisher and Henzinger, 2007; Gupta et al, 2007; Saez-Rodriguez et al, 2007; Zhang et al, 2008; Samaga et al, 2009), but a significant challenge in modelling biochemical pathways using Boolean logic has not yet been
n2:mentions: n3:15882705

Subject Item: _:vb6525888
rdf:type: n2:Context
rdf:value: Boolean modelling has previously been applied to biological regulatory and signalling networks (Kauffman, 1969; Thomas and D'Ari, 1990; Huang and Ingber, 2000; Thomas and Kaufman, 2001; de Jong, 2002; Chaves et al, 2005; Fauré et al, >>2006<<; Fisher and Henzinger, 2007; Gupta et al, 2007; Saez-Rodriguez et al, 2007; Zhang et al, 2008; Samaga et al, 2009), but a significant challenge in modelling biochemical pathways using Boolean logic has not yet been addressed:
n2:mentions: n3:16873462

Subject Item: _:vb6525889
rdf:type: n2:Context
rdf:value: to biological regulatory and signalling networks (Kauffman, 1969; Thomas and D'Ari, 1990; Huang and Ingber, 2000; Thomas and Kaufman, 2001; de Jong, 2002; Chaves et al, 2005; Fauré et al, 2006; Fisher and Henzinger, 2007; Gupta et al, >>2007<<; Saez-Rodriguez et al, 2007; Zhang et al, 2008; Samaga et al, 2009), but a significant challenge in modelling biochemical pathways using Boolean logic has not yet been addressed:
n2:mentions: n3:17010384

Subject Item: _:vb6525890
rdf:type: n2:Context
rdf:value: signalling networks (Kauffman, 1969; Thomas and D'Ari, 1990; Huang and Ingber, 2000; Thomas and Kaufman, 2001; de Jong, 2002; Chaves et al, 2005; Fauré et al, 2006; Fisher and Henzinger, 2007; Gupta et al, 2007; Saez-Rodriguez et al, >>2007<<; Zhang et al, 2008; Samaga et al, 2009), but a significant challenge in modelling biochemical pathways using Boolean logic has not yet been addressed:
n2:mentions: n3:17722974

Subject Item: _:vb6525891
rdf:type: n2:Context
rdf:value: (Kauffman, 1969; Thomas and D'Ari, 1990; Huang and Ingber, 2000; Thomas and Kaufman, 2001; de Jong, 2002; Chaves et al, 2005; Fauré et al, 2006; Fisher and Henzinger, 2007; Gupta et al, 2007; Saez-Rodriguez et al, 2007; Zhang et al, >>2008<<; Samaga et al, 2009), but a significant challenge in modelling biochemical pathways using Boolean logic has not yet been addressed:
n2:mentions: n3:18852469

Subject Item: _:vb6525892
rdf:type: n2:Context
rdf:value: The training data for this PSN comprises a set of ∼1000 biochemical measurements that conformed to a cue-signal-response (CSR) paradigm (Gaudet et al, >>2005<<). HepG2 hepatocellular carcinoma cells were exposed to combinations of extracellular ligands and small-molecule inhibitors, and the phosphorylation states or abundance of adaptor proteins, intracellular kinases, transcription factors, and
n2:mentions: n3:16030008

Subject Item: _:vb6525893
rdf:type: n2:Context
rdf:value: The training data were stored and processed using an updated version of our recently developed data management application, DataRail (Saez-Rodriguez et al, >>2008<<). Lastly, a set of predictions were made using the trained model and the predictions confirmed experimentally (using data unique to this paper).
n2:mentions: n3:18218655

Subject Item: _:vb6525894
rdf:type: n5:Section
dc:title: materials and methods
n5:contains: _:vb6525904 _:vb6525905 _:vb6525906 _:vb6525907 _:vb6525900 _:vb6525901 _:vb6525902 _:vb6525903 _:vb6525896 _:vb6525897 _:vb6525898 _:vb6525899 _:vb6525895

Subject Item: _:vb6525895
rdf:type: n2:Context
rdf:value: It can be used alone or in combination with DataRail, which manages experimental data according to a previously published MIDAS standard (Saez-Rodriguez et al, >>2008<<). All functions are accessible either via scripting or graphical user interfaces. CNO can import models from ProMoT (Saez-Rodriguez et al, 2006) and CellNetAnalyzer (Klamt et al, 2007). In the near future, automatic population of CNO
n2:mentions: n3:18218655

Subject Item: _:vb6525896
rdf:type: n2:Context
rdf:value: CNO can import models from ProMoT (Saez-Rodriguez et al, >>2006<<) and CellNetAnalyzer (Klamt et al, 2007).
n2:mentions: n3:17109765

Subject Item: _:vb6525897
rdf:type: n2:Context
rdf:value: CNO can import models from ProMoT (Saez-Rodriguez et al, 2006) and CellNetAnalyzer (Klamt et al, >>2007<<). In the near future, automatic population of CNO models from graphs in the BioPAX format (http://www.biopax.org/) and those stored in databases such as Pathway Commons, (http://www.pathwaycommons.org/) will be implemented. The models
n2:mentions: n3:17408509

Subject Item: _:vb6525898
rdf:type: n2:Context
rdf:value: We use the Boolean modelling formalism as introduced by Klamt et al (>>2006<<) for modelling signal transduction networks. Nodes in the Boolean network represent biological species and have an associated logical value (‘on' (1) or ‘off' (0)) determining whether the species is active/present or not.
n2:mentions: n3:16464248

Subject Item: _:vb6525899
rdf:type: n2:Context
rdf:value: A conventional graph is simply a special case of a hypergraph in which the cardinality of the tails and heads is 1 for all edges (Klamt et al, >>2009<<). By using the SOP formalism, a logic network can be converted to a hypergraph in a straightforward manner. Each hyperedge pointing into node i represents one term of the Boolean function (i.e., an AND connection or a single Boolean
n2:mentions: n3:19478865

Subject Item: _:vb6525900
rdf:type: n2:Context
rdf:value: All hyperedges ending in a node are implicitly linked via OR logic (Klamt et al, >>2006<<).
n2:mentions: n3:16464248

Subject Item: _:vb6525901
rdf:type: n2:Context
rdf:value: Qualitatively, these states can be computed as logical steady states in the Boolean network describing the early events (Klamt et al, >>2006<<). We therefore computed, for each model candidate, the logical steady state associated with the input values determined by each experiment k and compared the values of the readouts Bk,l,30M with the normalized experimental values Bk,l,30E
n2:mentions: n3:16464248

Subject Item: _:vb6525902
rdf:type: n2:Context
rdf:value: We compute the logical steady state resulting from the input stimuli by propagating input signals along logical (hyperarc) connections (Klamt et al, >>2006<<). Whether or not we can resolve a complete and unique logical response of all nodes for a given set of input stimuli, depends on the functionality of positive- or negative-feedback loops in the network (e.g., negative-feedback loops may
n2:mentions: n3:16464248

Subject Item: _:vb6525903
rdf:type: n2:Context
rdf:value: routines to perform simulations (in particular, to compute logical steady states that are generated by a certain combination of stimuli and inhibitors) are original to this work or are adapted from CellNetAnalyzer (Klamt et al, >>2007<<).
n2:mentions: n3:17408509

Subject Item: _:vb6525904
rdf:type: n2:Context
rdf:value: The activation of IRS-1Y, which is dependent on IGF-1 stimulation, was considered to be inhibited if the serine site was phosphorylated (Hotamisligil et al, >>1996<<; Saltiel and Kahn, 2001).
n2:mentions: n3:8571133

Subject Item: _:vb6525905
rdf:type: n2:Context
rdf:value: The activation of IRS-1Y, which is dependent on IGF-1 stimulation, was considered to be inhibited if the serine site was phosphorylated (Hotamisligil et al, 1996; Saltiel and Kahn, >>2001<<). We therefore added a node (IRS-1S) for the serine site of IRS-1. IRS-1S, in turn, was considered to be dependent on mTOR activation (Ozes et al, 2001).
n2:mentions: n3:11742412

Subject Item: _:vb6525906
rdf:type: n2:Context
rdf:value: IRS-1S, in turn, was considered to be dependent on mTOR activation (Ozes et al, >>2001<<).
n2:mentions: n3:11287630

Subject Item: _:vb6525907
rdf:type: n2:Context
rdf:value: The training data set can be downloaded as a MIDAS file (Saez-Rodriguez et al, >>2008<<) from http:
n2:mentions: n3:18218655

Subject Item: _:vb6525908
rdf:type: n5:Section
dc:title: results
n5:contains: _:vb6525924 _:vb6525925 _:vb6525920 _:vb6525921 _:vb6525922 _:vb6525923 _:vb6525909 _:vb6525910 _:vb6525911 _:vb6525916 _:vb6525917 _:vb6525918 _:vb6525919 _:vb6525912 _:vb6525913 _:vb6525914 _:vb6525915

Subject Item: _:vb6525909
rdf:type: n2:Context
rdf:value: with the network graph and then used to compute the discretized activities of four signalling proteins following ligand stimulation in the presence or absence of a small-molecule inhibitor of PI3K (e.g., ZSTK474; Yaguchi et al, >>2006<<) or Raf kinase (e.g., Sorafenib; Hotte and Hirte, 2002). The synthetic data corresponded to levels of phosphorylation at activating sites for AKT and ERK, nuclear translocation of NFκB, and cleavage of caspase-8 (Figure 1B).
n2:mentions: n3:16622124

Subject Item: _:vb6525910
rdf:type: n2:Context
rdf:value: the discretized activities of four signalling proteins following ligand stimulation in the presence or absence of a small-molecule inhibitor of PI3K (e.g., ZSTK474; Yaguchi et al, 2006) or Raf kinase (e.g., Sorafenib; Hotte and Hirte, >>2002<<). The synthetic data corresponded to levels of phosphorylation at activating sites for AKT and ERK, nuclear translocation of NFκB, and cleavage of caspase-8 (Figure 1B).
n2:mentions: n3:12369852

Subject Item: _:vb6525911
rdf:type: n2:Context
rdf:value: These lay on terminal branches of the pathway graph and corresponded to non-observables in systems theory (Kremling and Saez-Rodriguez, >>2007<<). Species whose states were not affected by any of the inputs and perturbations (the ligands and inhibitors in this case) were also eliminated; these corresponded to non-controllable elements. Second, CNO compressed cascades in which a
n2:mentions: n3:17400319

Subject Item: _:vb6525912
rdf:type: n2:Context
rdf:value: The superstructure was represented as a hypergraph using the sum-of-products formalism (see section Materials and methods and reference Klamt et al, >>2006<<) in which multiple OR and AND gates are combined, and inhibition is encoded using NOT operators.
n2:mentions: n3:16464248

Subject Item: _:vb6525913
rdf:type: n2:Context
rdf:value: number of degrees of freedom), but practical implementation of MDL requires making assumptions about how to encode complexity and typically involves the introduction of a tunable parameter that balances fit and complexity (Zhao et al, >>2006<<). We therefore chose to use a bipartite objective function common in reverse engineering (Bonneau et al, 2006; Nelander et al, 2008) and LASSO regression (Tibshirani, 1994) that balances fit and size using a tunable parameter chosen to
n2:mentions: n3:16845143

Subject Item: _:vb6525914
rdf:type: n2:Context
rdf:value: We therefore chose to use a bipartite objective function common in reverse engineering (Bonneau et al, >>2006<<; Nelander et al, 2008) and LASSO regression (Tibshirani, 1994) that balances fit and size using a tunable parameter chosen to maximize the predictive power of the model.
n2:mentions: n3:16686963

Subject Item: _:vb6525915
rdf:type: n2:Context
rdf:value: where and such that Bk,l,tM(P)∈{0,1} is the value (0 or 1) as predicted by computation of the model's logical steady state (Klamt et al, >>2006<<) and Bk,l,tE∈[0,1) is the data value for readout l at time t under the kth experimental condition.
n2:mentions: n3:16464248

Subject Item: _:vb6525916
rdf:type: n2:Context
rdf:value: Next, using routines newly added to the DataRail software (Saez-Rodriguez et al, >>2008<<), we computed the ratio Sk,l,tR (where k is the index for the experimental condition, l for the measurement—the specific xMAP sandwich immunoassay in this case—and t is the time point) between each measurement, Sk,l,t, and Sk,l,0, the
n2:mentions: n3:18218655

Subject Item: _:vb6525917
rdf:type: n2:Context
rdf:value: Specifically, the link TNFR → PI3K (labelled 1; Figure 5) proposed by Marchetti et al (>>2004<<) was missing as was Rac → MAP3K1 (labelled 2; Fanger et al, 1997) and Ras → MAP3K1 (labelled 3; Russell et al, 1995).
n2:mentions: n3:15155767

Subject Item: _:vb6525918
rdf:type: n2:Context
rdf:value: Specifically, the link TNFR → PI3K (labelled 1; Figure 5) proposed by Marchetti et al (2004) was missing as was Rac → MAP3K1 (labelled 2; Fanger et al, >>1997<<) and Ras → MAP3K1 (labelled 3; Russell et al, 1995).
n2:mentions: n3:9305638

Subject Item: _:vb6525919
rdf:type: n2:Context
rdf:value: Specifically, the link TNFR → PI3K (labelled 1; Figure 5) proposed by Marchetti et al (2004) was missing as was Rac → MAP3K1 (labelled 2; Fanger et al, 1997) and Ras → MAP3K1 (labelled 3; Russell et al, >>1995<<). Crosstalk between AKT and the Raf/MEK/ERK cascade (labelled 4; Guan et al, 2000) was also missing. The absence of these interactions from the LD-PSN calibrated to data from HepG2 cells does not appear to be an artefact of our approach
n2:mentions: n3:7744823

Subject Item: _:vb6525920
rdf:type: n2:Context
rdf:value: Crosstalk between AKT and the Raf/MEK/ERK cascade (labelled 4; Guan et al, >>2000<<) was also missing.
n2:mentions: n3:10869359

Subject Item: _:vb6525921
rdf:type: n2:Context
rdf:value: Although absent from LD-PSN, we nonetheless found literature support for a connection between TRAF6 and MEK in non-transformed human colonic epithelial cells (corresponding to the green line in Figure 5, labelled 5; Rhee et al, >>2004<<), and for a connection between ERK and IRS-1 phospho-serine in primary adipocytes (Figure 5, magenta line, labelled 6; Hers and Tavaré, 2005).
n2:mentions: n3:15069060

Subject Item: _:vb6525922
rdf:type: n2:Context
rdf:value: epithelial cells (corresponding to the green line in Figure 5, labelled 5; Rhee et al, 2004), and for a connection between ERK and IRS-1 phospho-serine in primary adipocytes (Figure 5, magenta line, labelled 6; Hers and Tavaré, >>2005<<). Hers and Tavaré (2005) also report the absence of a link between mTOR and IRS-1 phospho-serine, in agreement with our models, but different from what has been observed for other cell lines (Ozes et al, 2001; Hers and Tavaré, 2005).
n2:mentions: n3:15713122

Subject Item: _:vb6525923
rdf:type: n2:Context
rdf:value: Hers and Tavaré (>>2005<<) also report the absence of a link between mTOR and IRS-1 phospho-serine, in agreement with our models, but different from what has been observed for other cell lines (Ozes et al, 2001; Hers and Tavaré, 2005).
n2:mentions: n3:15713122

Subject Item: _:vb6525924
rdf:type: n2:Context
rdf:value: Hers and Tavaré (2005) also report the absence of a link between mTOR and IRS-1 phospho-serine, in agreement with our models, but different from what has been observed for other cell lines (Ozes et al, >>2001<<; Hers and Tavaré, 2005).
n2:mentions: n3:11287630

Subject Item: _:vb6525925
rdf:type: n2:Context
rdf:value: Hers and Tavaré (2005) also report the absence of a link between mTOR and IRS-1 phospho-serine, in agreement with our models, but different from what has been observed for other cell lines (Ozes et al, 2001; Hers and Tavaré, >>2005<<).
n2:mentions: n3:15713122

Subject Item: _:vb6525926
rdf:type: n5:Section
dc:title: discussion
n5:contains: _:vb6525940 _:vb6525941 _:vb6525936 _:vb6525937 _:vb6525938 _:vb6525939 _:vb6525932 _:vb6525933 _:vb6525934 _:vb6525935 _:vb6525928 _:vb6525929 _:vb6525930 _:vb6525931 _:vb6525927

Subject Item: _:vb6525927
rdf:type: n2:Context
rdf:value: For example, a network of signalling pathways in neurons contained 545 nodes and 1259 interactions (2.3 edges per node; Ma'ayan et al, >>2005<<), and undirected PINs and interactomes are even richer in edges per node:
n2:mentions: n3:16099987

Subject Item: _:vb6525928
rdf:type: n2:Context
rdf:value: for a network involving 121 disease-associated proteins (Rual et al, >>2005<<), and 4.6 for a network constructed around tumour suppressors of breast cancer (Pujana et al, 2007).
n2:mentions: n3:16189514

Subject Item: _:vb6525929
rdf:type: n2:Context
rdf:value: for a network involving 121 disease-associated proteins (Rual et al, 2005), and 4.6 for a network constructed around tumour suppressors of breast cancer (Pujana et al, >>2007<<). We therefore speculate that significant reductions in network complexity will be observed by applying the Boolean modelling approach described in this paper to PSNs and PINs covering other aspects of eukaryotic biology.
n2:mentions: n3:17922014

Subject Item: _:vb6525930
rdf:type: n2:Context
rdf:value: The use of prior knowledge distinguishes our work from standard reverse engineering (Sachs et al, >>2005<<; Bonneau et al, 2006; Perkins et al, 2006; Bansal et al, 2007; Cho et al, 2007; Nelander et al, 2008), including reverse engineering using Boolean logic (D'haeseleer et al, 2000; Laubenbacher and Stigler, 2004).
n2:mentions: n3:15845847

Subject Item: _:vb6525931
rdf:type: n2:Context
rdf:value: The use of prior knowledge distinguishes our work from standard reverse engineering (Sachs et al, 2005; Bonneau et al, >>2006<<; Perkins et al, 2006; Bansal et al, 2007; Cho et al, 2007; Nelander et al, 2008), including reverse engineering using Boolean logic (D'haeseleer et al, 2000; Laubenbacher and Stigler, 2004).
n2:mentions: n3:16686963

Subject Item: _:vb6525932
rdf:type: n2:Context
rdf:value: The use of prior knowledge distinguishes our work from standard reverse engineering (Sachs et al, 2005; Bonneau et al, 2006; Perkins et al, >>2006<<; Bansal et al, 2007; Cho et al, 2007; Nelander et al, 2008), including reverse engineering using Boolean logic (D'haeseleer et al, 2000; Laubenbacher and Stigler, 2004).
n2:mentions: n3:16710449

Subject Item: _:vb6525933
rdf:type: n2:Context
rdf:value: The use of prior knowledge distinguishes our work from standard reverse engineering (Sachs et al, 2005; Bonneau et al, 2006; Perkins et al, 2006; Bansal et al, >>2007<<; Cho et al, 2007; Nelander et al, 2008), including reverse engineering using Boolean logic (D'haeseleer et al, 2000; Laubenbacher and Stigler, 2004).
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rdf:value: The use of prior knowledge distinguishes our work from standard reverse engineering (Sachs et al, 2005; Bonneau et al, 2006; Perkins et al, 2006; Bansal et al, 2007; Cho et al, >>2007<<; Nelander et al, 2008), including reverse engineering using Boolean logic (D'haeseleer et al, 2000; Laubenbacher and Stigler, 2004).
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rdf:value: our work from standard reverse engineering (Sachs et al, 2005; Bonneau et al, 2006; Perkins et al, 2006; Bansal et al, 2007; Cho et al, 2007; Nelander et al, 2008), including reverse engineering using Boolean logic (D'haeseleer et al, >>2000<<; Laubenbacher and Stigler, 2004).
n2:mentions: n3:11099257

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rdf:value: (Sachs et al, 2005; Bonneau et al, 2006; Perkins et al, 2006; Bansal et al, 2007; Cho et al, 2007; Nelander et al, 2008), including reverse engineering using Boolean logic (D'haeseleer et al, 2000; Laubenbacher and Stigler, >>2004<<). Moreover, we include in our decompressed graphs intermediate species that are not subject to experimental measurement, whereas latent or hidden variables are rarely included in reverse-engineered networks because they are supported only
n2:mentions: n3:15246788

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rdf:value: not be the most appropriate way to capture the causality of dynamics of processes (e.g., it cannot describe phenomena such as oscillations), but it can be used to model reactions that operate on different time scales (Klamt et al, >>2006<<). Second, we are implementing tools that extract PSNs from public sources such as Pathway Commons and to add data from PINs (Rual et al, 2005).
n2:mentions: n3:16464248

Subject Item: _:vb6525938
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rdf:value: Second, we are implementing tools that extract PSNs from public sources such as Pathway Commons and to add data from PINs (Rual et al, >>2005<<). These undirected graphs can serve as a source of information for adding edges to optimized models, but in this case we would need to evaluate four possible edges for each undirected edge, representing two directions and two signs. The
n2:mentions: n3:16189514

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rdf:value: Bayesian approaches obviously represent an effective means to encode both the prior and consequent probabilities in such a network; progress in this direction can be found in the work of Gat-Viks and Shamir (>>2007<<). Moreover, probabilistic Boolean networks (Shmulevich et al, 2002) are a natural extension of the deterministic Boolean networks used in this work. Finally, it may prove useful to add multilevel logic (Thomas and D'Ari, 1990), discrete
n2:mentions: n3:17267811

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rdf:value: Moreover, probabilistic Boolean networks (Shmulevich et al, >>2002<<) are a natural extension of the deterministic Boolean networks used in this work.
n2:mentions: n3:11847074

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rdf:value: Finally, it may prove useful to add multilevel logic (Thomas and D'Ari, 1990), discrete Petri nets (Fisher and Henzinger, 2007; Chaouiya et al, 2008), Fuzzy-logic (Aldridge et al, >>2009<<), or Boolean-based ODE systems (Mendoza and Xenarios, 2006; Wittmann et al, 2009) that can encode intermediate levels of protein activity.
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