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1.  Personalization of cancer treatment using predictive simulation 
Background
The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug.
Methods
We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells.
Results
Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells.
Conclusions
These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches.
Electronic supplementary material
The online version of this article (doi:10.1186/s12967-015-0399-y) contains supplementary material, which is available to authorized users.
doi:10.1186/s12967-015-0399-y
PMCID: PMC4320499  PMID: 25638213
Multiple myeloma; Rational drug design; Personalized therapy
2.  In silico modeling predicts drug sensitivity of patient-derived cancer cells 
Background
Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling (“omics”) data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach.
Methods
Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents.
Results
Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings.
Conclusions
These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.
doi:10.1186/1479-5876-12-128
PMCID: PMC4030016  PMID: 24884660
Glioblastoma; Cancer; In Silico modeling; Deterministic model; Virtual tumor technology; Tumor profiling; Personalized therapy; Targeted therapy
4.  Histatin 5 binds to Porphyromonas gingivalis hemagglutinin B (HagB) and alters HagB-induced chemokine responses 
Scientific Reports  2014;4:3904.
Histatins are human salivary gland peptides with anti-microbial and anti-inflammatory activities. In this study, we hypothesized that histatin 5 binds to Porphyromonas gingivalis hemagglutinin B (HagB) and attenuates HagB-induced chemokine responses in human myeloid dendritic cells. Histatin 5 bound to immobilized HagB in a surface plasmon resonance (SPR) spectroscopy-based biosensor system. SPR spectroscopy kinetic and equilibrium analyses, protein microarray studies, and I-TASSER structural modeling studies all demonstrated two histatin 5 binding sites on HagB. One site had a stronger affinity with a KD1 of 1.9 μM and one site had a weaker affinity with a KD2 of 60.0 μM. Binding has biological implications and predictive modeling studies and exposure of dendritic cells both demonstrated that 20.0 μM histatin 5 attenuated (p < 0.05) 0.02 μM HagB-induced CCL3/MIP-1α, CCL4/MIP-1β, and TNFα responses. Thus histatin 5 is capable of attenuating chemokine responses, which may help control oral inflammation.
doi:10.1038/srep03904
PMCID: PMC3912440  PMID: 24473528
5.  Novel anti-glioblastoma agents and therapeutic combinations identified from a collection of FDA approved drugs 
Background
Glioblastoma (GBM) is a therapeutic challenge, associated with high mortality. More effective GBM therapeutic options are urgently needed. Hence, we screened a large multi-class drug panel comprising the NIH clinical collection (NCC) that includes 446 FDA-approved drugs, with the goal of identifying new GBM therapeutics for rapid entry into clinical trials for GBM.
Methods
Screens using human GBM cell lines revealed 22 drugs with potent anti-GBM activity, including serotonergic blockers, cholesterol-lowering agents (statins), antineoplastics, anti-infective, anti-inflammatories, and hormonal modulators. We tested the 8 most potent drugs using patient-derived GBM cancer stem cell-like lines. Notably, the statins were active in vitro; they inhibited GBM cell proliferation and induced cellular autophagy. Moreover, the statins enhanced, by 40-70 fold, the pro-apoptotic activity of irinotecan, a topoisomerase 1 inhibitor currently used to treat a variety of cancers including GBM. Our data suggest that the mechanism of action of statins was prevention of multi-drug resistance protein MDR-1 glycosylation. This drug combination was synergistic in inhibiting tumor growth in vivo. Compared to animals treated with high dose irinotecan, the drug combination showed significantly less toxicity.
Results
Our data identifies a novel combination from among FDA-approved drugs. In addition, this combination is safer and well tolerated compared to single agent irinotecan.
Conclusions
Our study newly identifies several FDA-approved compounds that may potentially be useful in GBM treatment. Our findings provide the basis for the rational combination of statins and topoisomerase inhibitors in GBM.
doi:10.1186/1479-5876-12-13
PMCID: PMC3898565  PMID: 24433351
Glioblastoma; Drug screening; Patient-derived glioblastoma cell lines; Rational combination
6.  Predictive Simulation Approach for Designing Cancer Therapeutic Regimens with Novel Biological Mechanisms 
Journal of Cancer  2014;5(6):406-416.
Introduction Ursolic acid (UA) is a pentacyclic triterpene acid present in many plants, including apples, basil, cranberries, and rosemary. UA suppresses proliferation and induces apoptosis in a variety of tumor cells via inhibition of nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB). Given that single agent therapy is a major clinical obstacle to overcome in the treatment of cancer, we sought to enhance the anti-cancer efficacy of UA through rational design of combinatorial therapeutic regimens that target multiple signaling pathways critical to carcinogenesis.
Methodology Using a predictive simulation-based approach that models cancer disease physiology by integrating signaling and metabolic networks, we tested the effect of UA alone and in combination with 100 other agents across cell lines from colorectal cancer, non-small cell lung cancer and multiple myeloma. Our predictive results were validated in vitro using standard molecular assays. The MTT assay and flow cytometry were used to assess cellular proliferation. Western blotting was used to monitor the combinatorial effects on apoptotic and cellular signaling pathways. Synergy was analyzed using isobologram plots.
Results We predictively identified c-Jun N-terminal kinase (JNK) as a pathway that may synergistically inhibit cancer growth when targeted in combination with NFκB. UA in combination with the pan-JNK inhibitor SP600125 showed maximal reduction in viability across a panel of cancer cell lines, thereby corroborating our predictive simulation assays. In HCT116 colon carcinoma cells, the combination caused a 52% reduction in viability compared with 18% and 27% for UA and SP600125 alone, respectively. In addition, isobologram plot analysis reveals synergy with lowered doses of the drugs in combination. The combination synergistically inhibited proliferation and induced apoptosis as evidenced by an increase in the percentage sub-G1 phase cells and cleavage of caspase 3 and poly ADP ribose polymerase (PARP). Combination treatment resulted in a significant reduction in the expression of cyclin D1 and c-Myc as compared with single agent treatment.
Conclusions Our findings underscore the importance of targeting NFκB and JNK signaling in combination in cancer cells. These results also highlight and validate the use of predictive simulation technology to design therapeutics for targeting novel biological mechanisms using existing or novel chemistry.
doi:10.7150/jca.7680
PMCID: PMC4026994  PMID: 24847381
ursolic acid; c-Jun N-terminal kinase; NFκB; computer modeling; carcinogenesis
7.  Defensin DEFB103 bidirectionally regulates chemokine and cytokine responses to a pro-inflammatory stimulus 
Scientific Reports  2013;3:1232.
Human β defensin DEFB103 acts as both a stimulant and an attenuator of chemokine and cytokine responses: a dichotomy that is not entirely understood. Our predicted results using an in silico simulation model of dendritic cells and our observed results in human myeloid dendritic cells, show that DEFB103 significantly (p < 0.05) enhanced 6 responses, attenuated 7 responses, and both enhanced/attenuated the CXCL1 and TNF responses to Porphyromonas gingivalis hemagglutinin B (HagB). In murine JAWSII dendritic cells, DEFB103 significantly attenuated, yet rarely enhanced, the Cxcl2, Il6, and Csf3 responses to HagB; and in C57/BL6 mice, DEFB103 significantly enhanced, yet rarely attenuated, the Cxcl1, Csf1, and Csf3 responses. Thus, DEFB103 influences pro-inflammatory activities with the concentration of DEFB103 and order of timing of DEFB103 exposure to dendritic cells, with respect to microbial antigen exposure to cells, being paramount in orchestrating the onset, magnitude, and composition of the chemokine and cytokine response.
doi:10.1038/srep01232
PMCID: PMC3565171  PMID: 23390582
8.  Amplification and Demultiplexing in Insulin-regulated Akt Protein Kinase Pathway in Adipocytes* 
The Journal of Biological Chemistry  2011;287(9):6128-6138.
Background: Akt plays a major role in insulin regulation of metabolism.
Results: Akt operates at 5–22% of its dynamic range. This lacks concordance with Akt substrate phosphorylation, GLUT4 translocation, and protein synthesis.
Conclusion: Akt is a demultiplexer that splits the insulin signal into discrete outputs.
Significance: This study provides better understanding of the Akt pathway and has implications for the role of Akt in diseases.
Akt plays a major role in insulin regulation of metabolism in muscle, fat, and liver. Here, we show that in 3T3-L1 adipocytes, Akt operates optimally over a limited dynamic range. This indicates that Akt is a highly sensitive amplification step in the pathway. With robust insulin stimulation, substantial changes in Akt phosphorylation using either pharmacologic or genetic manipulations had relatively little effect on Akt activity. By integrating these data we observed that half-maximal Akt activity was achieved at a threshold level of Akt phosphorylation corresponding to 5–22% of its full dynamic range. This behavior was also associated with lack of concordance or demultiplexing in the behavior of downstream components. Most notably, FoxO1 phosphorylation was more sensitive to insulin and did not exhibit a change in its rate of phosphorylation between 1 and 100 nm insulin compared with other substrates (AS160, TSC2, GSK3). Similar differences were observed between various insulin-regulated pathways such as GLUT4 translocation and protein synthesis. These data indicate that Akt itself is a major amplification switch in the insulin signaling pathway and that features of the pathway enable the insulin signal to be split or demultiplexed into discrete outputs. This has important implications for the role of this pathway in disease.
doi:10.1074/jbc.M111.318238
PMCID: PMC3307283  PMID: 22207758
Adipocyte; Akt PKB; Insulin Resistance; Protein Synthesis; Signal Transduction; IRS1; PDGF; mTOR; Rapamycin
9.  Plumbagin inhibits invasion and migration of breast and gastric cancer cells by downregulating the expression of chemokine receptor CXCR4 
Molecular Cancer  2011;10:107.
Background
Increasing evidence indicates that the interaction between the CXC chemokine receptor-4 (CXCR4) and its ligand CXCL12 is critical in the process of metastasis that accounts for more than 90% of cancer-related deaths. Thus, novel agents that can downregulate the CXCR4/CXCL12 axis have therapeutic potential in inhibiting cancer metastasis.
Methods
In this report, we investigated the potential of an agent, plumbagin (5-hydroxy-2-methyl-1, 4-naphthoquinone), for its ability to modulate CXCR4 expression and function in various tumor cells using Western blot analysis, DNA binding assay, transient transfection, real time PCR analysis, chromatin immunoprecipitation, and cellular migration and invasion assays.
Results
We found that plumbagin downregulated the expression of CXCR4 in breast cancer cells irrespective of their HER2 status. The decrease in CXCR4 expression induced by plumbagin was not cell type-specific as the inhibition also occurred in gastric, lung, renal, oral, and hepatocellular tumor cell lines. Neither proteasome inhibition nor lysosomal stabilization had any effect on plumbagin-induced decrease in CXCR4 expression. Detailed study of the underlying molecular mechanism(s) revealed that the regulation of the downregulation of CXCR4 was at the transcriptional level, as indicated by downregulation of mRNA expression, inhibition of NF-κB activation, and suppression of chromatin immunoprecipitation activity. In addition, using a virtual, predictive, functional proteomics-based tumor pathway platform, we tested the hypothesis that NF-κB inhibition by plumbagin causes the decrease in CXCR4 and other metastatic genes. Suppression of CXCR4 expression by plumbagin was found to correlate with the inhibition of CXCL12-induced migration and invasion of both breast and gastric cancer cells.
Conclusions
Overall, our results indicate, for the first time, that plumbagin is a novel blocker of CXCR4 expression and thus has the potential to suppress metastasis of cancer.
doi:10.1186/1476-4598-10-107
PMCID: PMC3175200  PMID: 21880153
10.  Dual inhibition of Akt/mTOR pathway by nab-rapamycin and perifosine induces anti-tumor activity in multiple myeloma 
Molecular cancer therapeutics  2010;9(4):963-975.
The PI3K/Akt/mTOR pathway mediates multiple myeloma (MM) cell proliferation, survival, and development of drug resistance, underscoring the role of mTOR inhibitors such as rapamycin with potential anti-MM activity. However, recent data demonstrate a positive feedback loop from mTOR/S6K1 to Akt, whereby Akt activation confers resistance to mTOR inhibitors. We confirmed that suppression of mTOR signaling in MM cells by rapamycin was associated with upregulation of Akt phosphorylation. We hypothesized that inhibiting this positive feedback by a potent Akt inhibitor perifosine would augment rapamycin-induced cytotoxicity in MM cells. Perifosine inhibited rapamycin-induced p-Akt, resulting in enhanced cytotoxicity in MM.1S cells even in the presence of IL-6, IGF-1 or bone marrow stromal cells. Moreover, rapamycin induced autophagy in MM.1S MM cells as evidenced by electron microscopy and immunocytochemistry, was augmented by perifosine. Combination therapy increased apoptosis detected by Annexin/PI analysis and caspase/PARP cleavage. Importantly, in vivo antitumor activity and prolongation of survival in a MM mouse xenograft model after treatment was enhanced with combination of nab-rapamycin and perifosine. Utilizing the in silico predictive analysis we confirmed our experimental findings of this drug combination on PI3K, Akt, mTOR kinases, and the caspases. Our data suggests that mutual suppression of the PI3K/Akt/mTOR pathway by rapamycin and perifosine combination induces synergistic MM cell cytotoxicity, providing the rationale for clinical trials in patients with relapsed / refractory MM.
doi:10.1158/1535-7163.MCT-09-0763
PMCID: PMC3096071  PMID: 20371718
myeloma; Akt; mTOR; apoptosis; autophagy
11.  A kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli 
Background
A prerequisite for a successful design and discovery of an antibacterial drug is the identification of essential targets as well as potent inhibitors that adversely affect the survival of bacteria. In order to understand how intracellular perturbations occur due to inhibition of essential metabolic pathways, we have built, through the use of ordinary differential equations, a mathematical model of 8 major Escherichia coli pathways.
Results
Individual in vitro enzyme kinetic parameters published in the literature were used to build the network of pathways in such a way that the flux distribution matched that reported from whole cells. Gene regulation at the transcription level as well as feedback regulation of enzyme activity was incorporated as reported in the literature. The unknown kinetic parameters were estimated by trial and error through simulations by observing network stability. Metabolites, whose biosynthetic pathways were not represented in this platform, were provided at a fixed concentration. Unutilized products were maintained at a fixed concentration by removing excess quantities from the platform. This approach enabled us to achieve steady state levels of all the metabolites in the cell. The output of various simulations correlated well with those previously published.
Conclusion
Such a virtual platform can be exploited for target identification through assessment of their vulnerability, desirable mode of target enzyme inhibition, and metabolite profiling to ascribe mechanism of action following a specific target inhibition. Vulnerability of targets in the biosynthetic pathway of coenzyme A was evaluated using this platform. In addition, we also report the utility of this platform in understanding the impact of a physiologically relevant carbon source, glucose versus acetate, on metabolite profiles of bacterial pathogens.
doi:10.2147/AABC.S14368
PMCID: PMC3170011  PMID: 21918631
antibacterial drug; mathematical model; kinetic platform; metabolic dynamics; Escherichia coli
12.  Correction: A Computer Simulation of Progesterone and Cox2 Inhibitor Treatment for Preterm Labor 
PLoS ONE  2010;5(2):10.1371/annotation/d3491dea-c206-4660-940e-7fefdcee5761.
doi:10.1371/annotation/d3491dea-c206-4660-940e-7fefdcee5761
PMCID: PMC2829320
13.  A Computer Simulation of Progesterone and Cox2 Inhibitor Treatment for Preterm Labor 
PLoS ONE  2010;5(1):e8502.
Background
Sufficient information from in vitro and in vivo studies has become available to permit computer modeling of the processes that occur in the myometrium during labor. This development allows the in silico investigation of pathological mechanisms and the trialing of potential treatments.
Methods/Results
Based on the human literature, we developed a computer model of the immune-endocrine environment of the myometrial cell. The interactions between molecules are represented by differential equations. The model is designed to simulate the estrogen and progesterone receptor changes during pregnancy and particularly the changes in the progesterone receptor (PR) isoforms A and B that are thought to mediate functional progesterone withdrawal in the human at labor. Parturition is represented by an increase in the PRA to PRB ratio to levels seen in women in labor. Infection is shown by inducing inflammation in the system by increasing phospho-IkB kinase concentration (IKK) levels; which lead to increased NF-κB activation, causing an increase in the PRA/PRB ratio. We examined the effects of progesterone or cyclo-oxygenase 2 (Cox2) inhibitor treatments on the PRA/PRB ratio in silico. The model predicted that high doses of progesterone and Cox2 inhibition would be effective in preventing an NF-κB-induced PRA/PRB ratio increase to the levels found during labor.
Conclusions
Our data illustrate the use of dynamic biological computer simulations to test the effectiveness of therapeutic interventions. This may allow the early rejection of ineffective therapies prior to expensive field trials.
doi:10.1371/journal.pone.0008502
PMCID: PMC2811723  PMID: 20111699
14.  Virtual prototyping study shows increased ATPase activity of Hsp90 to be the key determinant of cancer phenotype 
Systems and Synthetic Biology  2009;4(1):25-33.
Hsp90 is an ATP-dependent molecular chaperone that regulates key signaling proteins and thereby impacts cell growth and development. Chaperone cycle of Hsp90 is regulated by ATP binding and hydrolysis through its intrinsic ATPase activities, which is in turn modulated by interaction with its co-chaperones. Hsp90 ATPase activity varies in different organisms and is known to be increased in tumor cells. In this study we have quantitatively analyzed the impact of increasing Hsp90 ATPase activity on the activities of its clients through a virtual prototyping technology, which comprises a dynamic model of Hsp90 interaction with clients involved in proliferation pathways. Our studies highlight the importance of increased ATPase activity of Hsp90 in cancer cells as the key modulator for increased proliferation and survival. A tenfold increase in ATPase activity of Hsp90 often seen in cancer cells increases the levels of active client proteins such as Akt-1, Raf-1 and Cyclin D1 amongst others to about 12-, 8- and 186-folds respectively. Additionally we studied the effect of a competitive inhibitor of Hsp90 activity on the reduction in the client protein levels. Virtual prototyping experiments corroborate with findings that the drug has almost 10- to 100-fold higher affinity as indicated by a lower IC50 value (30–100 nM) in tumor cells with higher ATPase activity. The results also indicate a 15- to 25-fold higher efficacy of the inhibitor in reducing client levels in tumor cells. This analysis provides mechanistic insights into the links between increased Hsp90 ATPase activity, tumor phenotype and the hypersensitivity of tumor Hsp90 to inhibition by ATP analogs.
Electronic supplementary material
The online version of this article (doi:10.1007/s11693-009-9046-3) contains supplementary material, which is available to authorized users.
doi:10.1007/s11693-009-9046-3
PMCID: PMC2816227  PMID: 19856130
Anti-cancer drugs; Heat shock protein; Quantitative analysis; Virtual prototyping; Hsp90 ATPase activity
15.  Virtual prototyping study shows increased ATPase activity of Hsp90 to be the key determinant of cancer phenotype 
Systems and Synthetic Biology  2009;4(1):25-33.
Hsp90 is an ATP-dependent molecular chaperone that regulates key signaling proteins and thereby impacts cell growth and development. Chaperone cycle of Hsp90 is regulated by ATP binding and hydrolysis through its intrinsic ATPase activities, which is in turn modulated by interaction with its co-chaperones. Hsp90 ATPase activity varies in different organisms and is known to be increased in tumor cells. In this study we have quantitatively analyzed the impact of increasing Hsp90 ATPase activity on the activities of its clients through a virtual prototyping technology, which comprises a dynamic model of Hsp90 interaction with clients involved in proliferation pathways. Our studies highlight the importance of increased ATPase activity of Hsp90 in cancer cells as the key modulator for increased proliferation and survival. A tenfold increase in ATPase activity of Hsp90 often seen in cancer cells increases the levels of active client proteins such as Akt-1, Raf-1 and Cyclin D1 amongst others to about 12-, 8- and 186-folds respectively. Additionally we studied the effect of a competitive inhibitor of Hsp90 activity on the reduction in the client protein levels. Virtual prototyping experiments corroborate with findings that the drug has almost 10- to 100-fold higher affinity as indicated by a lower IC50 value (30–100 nM) in tumor cells with higher ATPase activity. The results also indicate a 15- to 25-fold higher efficacy of the inhibitor in reducing client levels in tumor cells. This analysis provides mechanistic insights into the links between increased Hsp90 ATPase activity, tumor phenotype and the hypersensitivity of tumor Hsp90 to inhibition by ATP analogs.
Electronic supplementary material
The online version of this article (doi:10.1007/s11693-009-9046-3) contains supplementary material, which is available to authorized users.
doi:10.1007/s11693-009-9046-3
PMCID: PMC2816227  PMID: 19856130
Anti-cancer drugs; Heat shock protein; Quantitative analysis; Virtual prototyping; Hsp90 ATPase activity
16.  Insights into the effects of α-synuclein expression and proteasome inhibition on glutathione metabolism through a dynamic in silico model of Parkinson's disease: validation by cell culture data 
Free radical biology & medicine  2008;45(9):1290-1301.
Dopaminergic neurodegeneration during Parkinson disease (PD) involves several pathways including proteasome inhibition, α-synuclein (α-syn) aggregation, mitochondrial dysfunction, and glutathione (GSH) depletion. We have utilized a systems biology approach and built a dynamic model to understand and link the various events related to PD pathophysiology. We have corroborated the modeling data by examining the effects of α-syn expression in the absence and presence of proteasome inhibition on GSH metabolism in dopaminergic neuronal cultures. We report here that the expression of the mutant A53T form of α-syn is neurotoxic and causes GSH depletion in cells after proteasome inhibition, compared to wild-type α-syn-expressing cells and vector control. Modeling data predicted that GSH depletion in these cells was due to ATP loss associated with mitochondrial dysfunction. ATP depletion elicited by combined A53T expression and proteasome inhibition results in decreased de novo synthesis of GSH via the rate-limiting enzyme γ-glutamyl cysteine ligase. Based on these data and other recent reports, we propose a novel dynamic model to explain how the presence of mutated α-syn protein or proteasome inhibition may individually impact on mitochondrial function and in combination result in alterations in GSH metabolism via enhanced mitochondrial dysfunction.
doi:10.1016/j.freeradbiomed.2008.08.002
PMCID: PMC2744580  PMID: 18761401
Parkinson's disease; Neurodegeneration; α-Synuclein; Protein aggregation; Proteasome inhibition; Mitochondrial dysfunction; Glutathione; Systems biology; Dynamic model; In silico; Free radicals

Results 1-16 (16)