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Habibi I.,New Jersey Institute of Technology | Abdi A.,New Jersey Institute of Technology | Emamian E.S.,Advanced Technologies for Novel Therapeutics
2011 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2011 | Year: 2011

In this paper a systems biology framework for generalized fault diagnosis in the caspase signaling network of biomolecules is studied. This novel method is capable of identifying critical molecules whose dysfunction can affect the network function detrimentally. The generalized vulnerabilities defined and computed in the paper quantify the role of molecules in a complex network. Impact of network input activities and multiple faults are studied as well. The results and methods are useful for quantitative analysis of functional impacts of individual or a group of molecules on the overall performance of molecular signaling networks. © 2011 IEEE.


Habibi I.,New Jersey Institute of Technology | Emamian E.S.,Advanced Technologies for Novel Therapeutics | Abdi A.,New Jersey Institute of Technology
BMC Systems Biology | Year: 2014

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. © 2014 Habibi et al.; licensee BioMed Central.


Habibi I.,New Jersey Institute of Technology | Emamian E.S.,Advanced Technologies for Novel Therapeutics | Abdi A.,New Jersey Institute of Technology
PLoS ONE | Year: 2014

Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally. © 2014 Habibi et al.


Habibi I.,New Jersey Institute of Technology | Abdi A.,New Jersey Institute of Technology | Emamian E.S.,Advanced Technologies for Novel Therapeutics
Conference Record - Asilomar Conference on Signals, Systems and Computers | Year: 2015

Signaling networks in human cells convey signals from the cell membrane to specific target molecules via biochemical interactions, to control a variety of cellular functions. We have modeled signaling networks as communication channels where molecules communicate with each other to transfer signals. We have defined and computed the fundamental parameters of transmission error probability and signaling capacity in signaling networks. This systematic approach can be used to understand how cell signaling errors and malfunctioning molecules may contribute to the development of complex human disorders with unknown molecular bases. © 2015 IEEE.


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