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Quantitative analysis of intracellular communication and signaling errors in signaling networks.

Habibi I, Emamian ES, Abdi A - BMC Syst Biol (2014)

Bottom Line: This can lead to the identification of novel critical molecules in signal transduction networks.Dysfunction of these critical molecules is likely to be associated with some complex human disorders.Such critical molecules have the potential to serve as proper targets for drug discovery.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Wireless Communications and Signal Processing Research, Department of Electrical and Computer Engineering and Department of Biological Sciences, New Jersey Institute of Technology, 323 King Blvd, Newark 07102, NJ, USA. ali.abdi@njit.edu.

ABSTRACT

Background: Intracellular signaling networks transmit signals from the cell membrane to the nucleus, via biochemical interactions. The goal is to regulate some target molecules, to properly control the cell function. Regulation of the target molecules occurs through the communication of several intermediate molecules that convey specific signals originated from the cell membrane to the specific target outputs.

Results: In this study we propose to model intracellular signaling network as communication channels. We define the fundamental concepts of transmission error and signaling capacity for intracellular signaling networks, and devise proper methods for computing these parameters. The developed systematic methodology quantitatively shows how the signals that ligands provide upon binding can be lost in a pathological signaling network, due to the presence of some dysfunctional molecules. We show the lost signals result in message transmission error, i.e., incorrect regulation of target proteins at the network output. Furthermore, we show how dysfunctional molecules affect the signaling capacity of signaling networks and how the contributions of signaling molecules to the signaling capacity and signaling errors can be computed. The proposed approach can quantify the role of dysfunctional signaling molecules in the development of the pathology. We present experimental data on caspese3 and T cell signaling networks to demonstrate the biological relevance of the developed method and its predictions.

Conclusions: This study demonstrates how signal transmission and distortion in pathological signaling networks can be modeled and studied using the proposed methodology. The new methodology determines how much the functionality of molecules in a network can affect the signal transmission and regulation of the end molecules such as transcription factors. This can lead to the identification of novel critical molecules in signal transduction networks. Dysfunction of these critical molecules is likely to be associated with some complex human disorders. Such critical molecules have the potential to serve as proper targets for drug discovery.

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Analysis of the T cell network. (a) The T cell network [24]. The channel input molecules are TCR lig, CD4 and CD28, whereas the output molecules are AP1, bcat, BclXL, CRE, Cyc1, FKHR, NFκB, p21c, p27k, p38, p70S6K, SRE, NFAT and SHP2. Green arrows represent activatory interactions and red blunt lines show inhibitory interactions. This figure is intended to provide a general picture of the network. For specific details and regulatory mechanisms of each molecule, one can refer to the equations listed in Additional file 1: Table S1. (b) Values of transmission error probability Pe and capacity C are calculated for different molecules in the network with the output node SHP2, as an example output molecule. Pe and C values for those molecules not listed in this table are calculated as 0 and 1, respectively. See Additional file 1 for the list of these molecules. (c) Transmission error probability Pe versus the dominance factor k in the T cell communication channel for the two molecules PI3K and Fyn. When PI3K is the dominant dysfunctional molecule, Pe rapidly increases as the dominance factor k increases. This is in agreement with experimental data, which shows when PI3K is knocked out (inhibited with both Ly294002 and Wortmannin), PKB does not properly receive signals from the input molecules and remains inactive (its phosphorylation is blocked in human T cells) [24]. In contrast, the decrease of Pe when Fyn is the dominant dysfunctional molecule means that even if Fyn is dysfunctional with probability one, there will be no transmission error. This is consistent with the experimental observation that when Fyn is knocked out (Fyn-deficient and heterozygous splenic mouse T cells), stimulation of the input molecules still correctly regulates PKB [24].
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Figure 4: Analysis of the T cell network. (a) The T cell network [24]. The channel input molecules are TCR lig, CD4 and CD28, whereas the output molecules are AP1, bcat, BclXL, CRE, Cyc1, FKHR, NFκB, p21c, p27k, p38, p70S6K, SRE, NFAT and SHP2. Green arrows represent activatory interactions and red blunt lines show inhibitory interactions. This figure is intended to provide a general picture of the network. For specific details and regulatory mechanisms of each molecule, one can refer to the equations listed in Additional file 1: Table S1. (b) Values of transmission error probability Pe and capacity C are calculated for different molecules in the network with the output node SHP2, as an example output molecule. Pe and C values for those molecules not listed in this table are calculated as 0 and 1, respectively. See Additional file 1 for the list of these molecules. (c) Transmission error probability Pe versus the dominance factor k in the T cell communication channel for the two molecules PI3K and Fyn. When PI3K is the dominant dysfunctional molecule, Pe rapidly increases as the dominance factor k increases. This is in agreement with experimental data, which shows when PI3K is knocked out (inhibited with both Ly294002 and Wortmannin), PKB does not properly receive signals from the input molecules and remains inactive (its phosphorylation is blocked in human T cells) [24]. In contrast, the decrease of Pe when Fyn is the dominant dysfunctional molecule means that even if Fyn is dysfunctional with probability one, there will be no transmission error. This is consistent with the experimental observation that when Fyn is knocked out (Fyn-deficient and heterozygous splenic mouse T cells), stimulation of the input molecules still correctly regulates PKB [24].

Mentions: Using the proposed bio-communication methodology developed in this study, we analyzed a large experimentally-verified model of a cellular network described by Saez-Rodriguez et al.[24]. This T cell network is composed of 94 different molecules, 123 interactions and multiple feedback loops, which give rise to a complex map of interactions based upon well-established findings from different studies on primary T cells (Figure 4a). The inputs of the T cell network [24] (Figure 4a) are TCR ligand (T cell receptor ligand) and two other receptors CD4 and CD28, whereas the outputs are AP1, bcat, BclXL, CRE, Cyc1, FKHR, NFκB, p21c, p27k, p38, p70S6K, SRE, NFAT and SHP2. This network is experimentally verified and characterized extensively [24]. There are seventy four intermediate molecules between the inputs and the outputs, which constitute the communication channel in the network (Figure 4a). There are four feedback loops in the network, regulating SHP1, cCblp1, PAG and Gab2. According to Saez-Rodriguez et al.[24] there are some molecules which regulate other molecules but their own regulation mechanisms are not clear: CARD11, GADD45, GAP, CD45, PTEN, BCL10, CDC42, MALT1, SHIP1, AKAP79 and CALPR1. We have similarly [24] included them in the network, with their states [24] specified in Additional file 1: Table S1. Here we present the results of the analysis of this network to show how the findings of proposed communication analysis method for the T cell network are biologically relevant and are also supported by the experimental findings of Saez-Rodriguez et al.[24] and other studies [21]–[24].


Quantitative analysis of intracellular communication and signaling errors in signaling networks.

Habibi I, Emamian ES, Abdi A - BMC Syst Biol (2014)

Analysis of the T cell network. (a) The T cell network [24]. The channel input molecules are TCR lig, CD4 and CD28, whereas the output molecules are AP1, bcat, BclXL, CRE, Cyc1, FKHR, NFκB, p21c, p27k, p38, p70S6K, SRE, NFAT and SHP2. Green arrows represent activatory interactions and red blunt lines show inhibitory interactions. This figure is intended to provide a general picture of the network. For specific details and regulatory mechanisms of each molecule, one can refer to the equations listed in Additional file 1: Table S1. (b) Values of transmission error probability Pe and capacity C are calculated for different molecules in the network with the output node SHP2, as an example output molecule. Pe and C values for those molecules not listed in this table are calculated as 0 and 1, respectively. See Additional file 1 for the list of these molecules. (c) Transmission error probability Pe versus the dominance factor k in the T cell communication channel for the two molecules PI3K and Fyn. When PI3K is the dominant dysfunctional molecule, Pe rapidly increases as the dominance factor k increases. This is in agreement with experimental data, which shows when PI3K is knocked out (inhibited with both Ly294002 and Wortmannin), PKB does not properly receive signals from the input molecules and remains inactive (its phosphorylation is blocked in human T cells) [24]. In contrast, the decrease of Pe when Fyn is the dominant dysfunctional molecule means that even if Fyn is dysfunctional with probability one, there will be no transmission error. This is consistent with the experimental observation that when Fyn is knocked out (Fyn-deficient and heterozygous splenic mouse T cells), stimulation of the input molecules still correctly regulates PKB [24].
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Related In: Results  -  Collection

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Figure 4: Analysis of the T cell network. (a) The T cell network [24]. The channel input molecules are TCR lig, CD4 and CD28, whereas the output molecules are AP1, bcat, BclXL, CRE, Cyc1, FKHR, NFκB, p21c, p27k, p38, p70S6K, SRE, NFAT and SHP2. Green arrows represent activatory interactions and red blunt lines show inhibitory interactions. This figure is intended to provide a general picture of the network. For specific details and regulatory mechanisms of each molecule, one can refer to the equations listed in Additional file 1: Table S1. (b) Values of transmission error probability Pe and capacity C are calculated for different molecules in the network with the output node SHP2, as an example output molecule. Pe and C values for those molecules not listed in this table are calculated as 0 and 1, respectively. See Additional file 1 for the list of these molecules. (c) Transmission error probability Pe versus the dominance factor k in the T cell communication channel for the two molecules PI3K and Fyn. When PI3K is the dominant dysfunctional molecule, Pe rapidly increases as the dominance factor k increases. This is in agreement with experimental data, which shows when PI3K is knocked out (inhibited with both Ly294002 and Wortmannin), PKB does not properly receive signals from the input molecules and remains inactive (its phosphorylation is blocked in human T cells) [24]. In contrast, the decrease of Pe when Fyn is the dominant dysfunctional molecule means that even if Fyn is dysfunctional with probability one, there will be no transmission error. This is consistent with the experimental observation that when Fyn is knocked out (Fyn-deficient and heterozygous splenic mouse T cells), stimulation of the input molecules still correctly regulates PKB [24].
Mentions: Using the proposed bio-communication methodology developed in this study, we analyzed a large experimentally-verified model of a cellular network described by Saez-Rodriguez et al.[24]. This T cell network is composed of 94 different molecules, 123 interactions and multiple feedback loops, which give rise to a complex map of interactions based upon well-established findings from different studies on primary T cells (Figure 4a). The inputs of the T cell network [24] (Figure 4a) are TCR ligand (T cell receptor ligand) and two other receptors CD4 and CD28, whereas the outputs are AP1, bcat, BclXL, CRE, Cyc1, FKHR, NFκB, p21c, p27k, p38, p70S6K, SRE, NFAT and SHP2. This network is experimentally verified and characterized extensively [24]. There are seventy four intermediate molecules between the inputs and the outputs, which constitute the communication channel in the network (Figure 4a). There are four feedback loops in the network, regulating SHP1, cCblp1, PAG and Gab2. According to Saez-Rodriguez et al.[24] there are some molecules which regulate other molecules but their own regulation mechanisms are not clear: CARD11, GADD45, GAP, CD45, PTEN, BCL10, CDC42, MALT1, SHIP1, AKAP79 and CALPR1. We have similarly [24] included them in the network, with their states [24] specified in Additional file 1: Table S1. Here we present the results of the analysis of this network to show how the findings of proposed communication analysis method for the T cell network are biologically relevant and are also supported by the experimental findings of Saez-Rodriguez et al.[24] and other studies [21]–[24].

Bottom Line: This can lead to the identification of novel critical molecules in signal transduction networks.Dysfunction of these critical molecules is likely to be associated with some complex human disorders.Such critical molecules have the potential to serve as proper targets for drug discovery.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Wireless Communications and Signal Processing Research, Department of Electrical and Computer Engineering and Department of Biological Sciences, New Jersey Institute of Technology, 323 King Blvd, Newark 07102, NJ, USA. ali.abdi@njit.edu.

ABSTRACT

Background: Intracellular signaling networks transmit signals from the cell membrane to the nucleus, via biochemical interactions. The goal is to regulate some target molecules, to properly control the cell function. Regulation of the target molecules occurs through the communication of several intermediate molecules that convey specific signals originated from the cell membrane to the specific target outputs.

Results: In this study we propose to model intracellular signaling network as communication channels. We define the fundamental concepts of transmission error and signaling capacity for intracellular signaling networks, and devise proper methods for computing these parameters. The developed systematic methodology quantitatively shows how the signals that ligands provide upon binding can be lost in a pathological signaling network, due to the presence of some dysfunctional molecules. We show the lost signals result in message transmission error, i.e., incorrect regulation of target proteins at the network output. Furthermore, we show how dysfunctional molecules affect the signaling capacity of signaling networks and how the contributions of signaling molecules to the signaling capacity and signaling errors can be computed. The proposed approach can quantify the role of dysfunctional signaling molecules in the development of the pathology. We present experimental data on caspese3 and T cell signaling networks to demonstrate the biological relevance of the developed method and its predictions.

Conclusions: This study demonstrates how signal transmission and distortion in pathological signaling networks can be modeled and studied using the proposed methodology. The new methodology determines how much the functionality of molecules in a network can affect the signal transmission and regulation of the end molecules such as transcription factors. This can lead to the identification of novel critical molecules in signal transduction networks. Dysfunction of these critical molecules is likely to be associated with some complex human disorders. Such critical molecules have the potential to serve as proper targets for drug discovery.

Show MeSH
Related in: MedlinePlus