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author:("Kumar, ans")
1.  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
2.  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
3.  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
4.  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

Results 1-4 (4)