Effat S. Emamian

Fault Diagnosis Engineering of Digital Circuits Can Identify Vulnerable Molecules in Complex Cellular Pathways

An engineering approach reveals the weakest links in cellular signaling networks. Finding Vulnerability Vulnerability assessment methods, which are commonly used to test digital circuits, were applied to biological signaling networks to identify the molecules that when dysfunctional, would be most likely to disrupt the function of the network. Two signaling well-characterized networks were analyzed (one leading to caspase3 activation and apoptosis and a second leading to activation of p53) and the molecules known to be critical to the response were properly identified. In a third network of neuronal activation of the transcription factor CREB, this analysis led to the identification of Gαi and the P/Q-type calcium channel, which were then experimentally validated as critical molecules in this network. The application of complex system engineering approaches to cell signaling networks should lead to novel understandings and, subsequently, new treatments for complex disorders. In the area of circuit fault diagnosis engineering, there are various methods to identify the defective or vulnerable components of complex digital electronic circuits. In biological systems, however, knowledge is limited regarding the vulnerability of interconnected signaling pathways to the dysfunction of each specific molecule. By developing proper biologically driven digital vulnerability assessment methods, the vulnerability of complex signaling networks to the possible dysfunction of each molecule can be determined. To show the utility of this approach, we analyzed three well-characterized signaling networks—a cellular network that regulates the activity of caspase3, a network that regulates the activity of p53, and a central nervous system network that regulates the activity of the transcription factor CREB (adenosine 3′,5′-monophosphate response element–binding protein). We found important differences among the vulnerability values of different molecules. Most of the identified highly vulnerable molecules are functionally related and known key regulators of these networks. Experimental data confirmed the ability of digital vulnerability assessment to correctly predict key regulators in the CREB network. Because this approach may provide insight into key molecules that contribute to human diseases, it may aid in the identification of critical targets for drug development.

Quantitative analysis of intracellular communication and signaling errors in signaling networks

BackgroundIntracellular 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.ResultsIn 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.ConclusionsThis 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.

Fault Diagnosis Engineering in Molecular Signaling Networks: An Overview and Applications in Target Discovery

Fault diagnosis engineering is a key component of modern industrial facilities and complex systems, and has gone through considerable developments in the past few decades. In this paper, the principles and concepts of molecular fault diagnosis engineering are reviewed. In this area, molecular intracellular networks are considered as complex systems that may fail to function, due to the presence of some faulty molecules. Dysfunction of the system due to the presence of a single or multiple molecules can ultimately lead to the transition from the normal state to the disease state. It is the goal of molecular fault diagnosis engineering to identify the critical components of molecular networks, i.e., those whose dysfunction can interrupt the function of the entire network. The results of the fault analysis of several signaling networks are discussed, and possible connections of the findings with some complex human diseases are examined. Implications of molecular fault diagnosis engineering for target discovery and drug development are outlined as well.

Computation and measurement of cell decision making errors using single cell data

In this study a new computational method is developed to quantify decision making errors in cells, caused by noise and signaling failures. Analysis of tumor necrosis factor (TNF) signaling pathway which regulates the transcription factor Nuclear Factor κB (NF-κB) using this method identifies two types of incorrect cell decisions called false alarm and miss. These two events represent, respectively, declaring a signal which is not present and missing a signal that does exist. Using single cell experimental data and the developed method, we compute false alarm and miss error probabilities in wild-type cells and provide a formulation which shows how these metrics depend on the signal transduction noise level. We also show that in the presence of abnormalities in a cell, decision making processes can be significantly affected, compared to a wild-type cell, and the method is able to model and measure such effects. In the TNF—NF-κB pathway, the method computes and reveals changes in false alarm and miss probabilities in A20-deficient cells, caused by cell’s inability to inhibit TNF-induced NF-κB response. In biological terms, a higher false alarm metric in this abnormal TNF signaling system indicates perceiving more cytokine signals which in fact do not exist at the system input, whereas a higher miss metric indicates that it is highly likely to miss signals that actually exist. Overall, this study demonstrates the ability of the developed method for modeling cell decision making errors under normal and abnormal conditions, and in the presence of transduction noise uncertainty. Compared to the previously reported pathway capacity metric, our results suggest that the introduced decision error metrics characterize signaling failures more accurately. This is mainly because while capacity is a useful metric to study information transmission in signaling pathways, it does not capture the overlap between TNF-induced noisy response curves.

Advanced Fault Diagnosis Methods in Molecular Networks

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.

Complex human disorders and molecular system engineering: Historical perspective and potential impacts

The challenging nature of complex human disorders has taught us that we can not untangle a disorder unless we understand how the “engine” of molecular systems works. After learning the basic physiology of different organs in the human body, a “molecular revolution” occurred, which has now generated a huge amount of information regarding the function of individual molecules in human cells. The difficult task, however, is to understand how thousands of molecules communicate and work together to deliver a specific function, and more importantly, what goes wrong when the system fails and causes different diseases. The emerging field of systems biology is now opening the door for engineers, to join molecular biologists and enter the era of molecular biomedical engineering.

Identification of critical molecules via fault diagnosis engineering

Systems biology envisions that the application of complex system engineering approaches to cell signaling molecular networks can lead to novel understandings of complex human disorders. In this paper we show that by developing biologically-driven vulnerability assessment methods, the vulnerability of complex signaling networks to the dysfunction of each molecule can be determined. We have analyzed signaling networks that regulate mitosis and the activity of the transcription factor CREB. Our results indicate that biologically-relevant critical components of intracellular molecular networks can be identified using the proposed systems biology/fault diagnosis engineering technique. The application of this approach can improve our physiological understanding of the functionality of biological systems, can be used as a tool to identify novel genes associated with complex human disorders, and ultimately, has the potential to find the most prominent targets for drug discovery.

Molecular communication and signaling in human cells

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.

Dependence of functional vulnerabilities to the parameters of the caspase molecular network

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.