Computation capacities of a broad class of signaling networks are higher than their communication capacities.

Due to structural and functional abnormalities or genetic variations and mutations, there may be dysfunctional molecules within an intracellular signaling network that do not allow the network to correctly regulate its output molecules, such as transcription factors. This disruption in signaling interrupts normal cellular functions and may eventually develop some pathological conditions. In this paper, computation capacity of signaling networks is introduced as a fundamental limit on signaling capability and performance of such networks. In simple terms, the computation capacity measures the maximum number of computable inputs, that is, the maximum number of input values for which the correct functional output values can be recovered from the erroneous network outputs, when the network contains some dysfunctional molecules. This contrasts with the conventional communication capacity that measures instead the maximum number of input values that can be correctly distinguished based on the erroneous network outputs. The computation capacity is higher than the communication capacity whenever the network response function is not a one-to-one function of the input signals, and, unlike the communication capacity, it takes into account the input-output functional relationships of the network. By explicitly incorporating the effect of signaling errors that result in the network dysfunction, the computation capacity provides more information about the network and its malfunction. Two examples of signaling networks are considered in the paper, one regulating caspase3 and another regulating NFκB, for which computation and communication capacities are investigated. Higher computation capacities are observed for both networks. One biological implication of this finding is that signaling networks may have more 'capacity' than that specified by the conventional communication capacity metric. The effect of feedback is studied as well. In summary, this paper reports findings on a new fundamental feature of the signaling capability of cell signaling networks.

Losartan ointment relieves hypertrophic scars and keloid: A pilot study.

Keloid and hypertrophic scars are two types of fibrosis caused by extracellular matrix overexpression, and angiotensin II via AT1 receptor is known to play a key role in stimulation of fibrosis. A pilot placebo controlled single blind study was carried out on patients with hypertrophic scars and keloids. A total of 37 adult volunteers were randomly assigned into losartan 5% or placebo treatment groups. The treatment was performed twice a day for three months and a 6-month follow-up. The treatment was evaluated using Vancouver scar scale method. Totally, 30 participants were analyzed (Losartan ointment n = 20; placebo ointment n = 10; seven placebo volunteers left the study because they thought the treatment was not effective for them). In the losartan group, VSS scores dropped significantly (p < 0.01) both in keloid and hypertrophic scar patients. Vascularity and pliability were significantly reduced by losartan treatment (p < 0.05). It can be concluded that losartan potassium ointment (5%) can alleviate the keloid and hypertrophic scar.

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.

Predictive and prognostic value of TLR9 and NFKBIA gene expression as potential biomarkers for human glioma diagnosis.

BACKGROUND: Malignant gliomas are the most common form of primary intracranial tumors with the highest mortality rates. Various gene alterations are considered as prognostic markers in glioma. But, the relevant molecular mechanisms in this setting are not well-understood. OBJECTIVE: The aim of this study was to assess the association and prognostic value of TLR9 and NFKBIA with clinical significance and also their impact on patient survival in human glioma. METHODS: Expression of TLR9 and NFKBIA mRNA in the tissues was determined by immunohistochemistry and qRT-PCR methods. Kaplan-Meier curves and Cox proportional hazards regression model were used to assess the association of TLR9 and NFKBIA with clinical outcomes of patients. RESULTS: Quantitative real-time PCR analysis showed that TLR9 mRNAs is markedly expressed in glioma tissues than in non-neoplastic tissues (mean±SD: 3.26±0.40 vs. 0.71±0.36, P<0.001). There was also a significant difference between TLR9 mRNAs and high grade glioma (P<0.001).NFKBIA mRNAs was significantly identified in non-neoplastic tissues compared with glioma specimens (mean±SD: 2.76±0.30 vs. 0.94±0.35, P<0.001). Lower levels of NFKBIA mRNA were significantly related to advanced grade of gliomas (P<0.001). Furthermore, Immunoreactivity for high expression of TLR9 was detected in 65% of cases (26/40) that was associated with high grade glioma (P=0.001). No statistically significant correlation was found between TLR9 and other clinical parameters (P>0.05). Immunoreactivity for high expression of NFKBIA was observed in 32.5% (13/40) of cases and NFKBIA expression was decreased in patients with high grad glioma (P=0.014). There was no significant correlation between NFKBIA protein expression and age, sex, and relapse. The Kaplan-Meier analysis indicated that patients with high expression of TLR9 and low expression of NFKBIA are significantly related to poorer OS (P<0.001). In addition, the multivariate Cox regression model revealed that TLR9 and NFKBIA protein expressions (low/high) and tumor grade were potentially an independent predictor of survival in patients (hazard ratio, 2.132, 2.411, 2.13 [95% confidence interval, 1.825-3.782, 1.61-3.231, 1.542-3.92]; P=0.012,P=0.018, P=0.001). CONCLUSION: These data indicate that TLR9 and NFKBIA protein expressions act as independent predictor of survival for the diagnosis of glioma and a prognostic biomarker for those with a tumor at an advanced pathological grade.

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.

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

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.