Rule-Based Cell Systems Model of Aging using Feedback Loop Motifs Mediated by Stress Responses

Investigating the complex systems dynamics of the aging process requires integration of a broad range of cellular processes describing damage and functional decline co-existing with adaptive and protective regulatory mechanisms. We evolve an integrated generic cell network to represent the connectivity of key cellular mechanisms structured into positive and negative feedback loop motifs centrally important for aging. The conceptual network is casted into a fuzzy-logic, hybrid-intelligent framework based on interaction rules assembled from a priori knowledge. Based upon a classical homeostatic representation of cellular energy metabolism, we first demonstrate how positive-feedback loops accelerate damage and decline consistent with a vicious cycle. This model is iteratively extended towards an adaptive response model by incorporating protective negative-feedback loop circuits. Time-lapse simulations of the adaptive response model uncover how transcriptional and translational changes, mediated by stress sensors NF-κB and mTOR, counteract accumulating damage and dysfunction by modulating mitochondrial respiration, metabolic fluxes, biosynthesis, and autophagy, crucial for cellular survival. The model allows consideration of lifespan optimization scenarios with respect to fitness criteria using a sensitivity analysis. Our work establishes a novel extendable and scalable computational approach capable to connect tractable molecular mechanisms with cellular network dynamics underlying the emerging aging phenotype

Cancer stem cell metabolism

Cancer is now viewed as a stem cell disease. There is still no consensus on the metabolic characteristics of cancer stem cells, with several studies indicating that they are mainly glycolytic and others pointing instead to mitochondrial metabolism as their principal source of energy. Cancer stem cells also seem to adapt their metabolism to microenvironmental changes by conveniently shifting energy production from one pathway to another, or by acquiring intermediate metabolic phenotypes. Determining the role of cancer stem cell metabolism in carcinogenesis has become a major focus in cancer research, and substantial efforts are conducted towards discovering clinical targets

Sex steroid metabolism polymorphisms and mammographic density in pre- and early perimenopausal women

Introduction: We examined the association between mammographic density and single-nucleotide polymorphisms (SNPs) in genes encoding CYP1A1, CYP1B1, aromatase, 17β-HSD, ESR1, and ESR2 in pre- and early perimenopausal white, African-American, Chinese, and Japanese women.Methods: The Study of Women's Health Across the Nation is a longitudinal community-based cohort study. We analyzed data from 451 pre- and early perimenopausal participants of the ancillary SWAN Mammographic Density study for whom we had complete information regarding mammographic density, genotypes, and covariates. With multivariate linear regression, we examined the relation between percentage mammographic breast density (outcome) and each SNP (primary predictor), adjusting for age, race/ethnicity, parity, cigarette smoking, and body mass index (BMI).Results: After multivariate adjustment, the CYP1B1 rs162555 CC genotype was associated with a 9.4% higher mammographic density than the TC/TT genotype (P = 0.04). The CYP19A1 rs936306 TT genotype was associated with 6.2% lower mammographic density than the TC/CC genotype (P = 0.02). The positive association between CYP1A1 rs2606345 and mammographic density was significantly stronger among participants with BMI greater than 30 kg/m2than among those with BMI less than 25 kg/m2(Pinteraction= 0.05). Among white participants, the ESR1 rs2234693 CC genotype was associated with a 7.0% higher mammographic density than the CT/TT genotype (P = 0.01).Conclusions: SNPs in certain genes encoding sex steroid metabolism enzymes and ESRs were associated with mammographic density. Because the encoded enzymes and ESR1 are expressed in breast tissue, these SNPs may influence breast cancer risk by altering mammographic density. © 2009 Crandall; licensee BioMed Central Ltd

Determining duration of HER2-targeted therapy using stem cell extinction models.

IntroductionTrastuzumab dramatically improves survival in breast cancer patients whose tumor overexpresses HER2. A subpopulation of cells in human breast tumors has been identified with characteristics of cancer stem cells. These breast cancer stem-like cells (BCSCs) rely on HER2 signaling for self-renewal, suggesting that HER2-targeted therapy targets BCSCs even when the bulk of the tumor does not overexpress HER2. In order to guide clinical trials examining HER2-targeted therapy in the adjuvant setting, we propose a mathematical model to examine BCSC population dynamics and predict optimal duration of therapy.MethodsVarying the susceptibility of BCSCs to HER2-targeted therapy, we quantify the average time to extinction of BCSCs. We expand our model using stochastic simulation to include the partially differentiated tumor cells (TCs) that represent bulk tumor population and examine effects of plasticity on required duration of therapy.ResultsLower susceptibility of BCSCs and increased rates of dedifferentiation entail longer extinction times, indicating a need for prolonged administration of HER2-targeted therapy. We predict that even when therapy does not appreciably reduce tumor size in the advanced cancer setting, it will eventually eradicate the tumor in the adjuvant setting as long as there is at least a modest effect on BCSCs.ConclusionsWe anticipate that our results will inform clinical trials of targeted therapies in planning the duration of therapy needed to eradicate BCSCs. Our predictions also address safety, as longer duration of therapy entails a greater potential impact on normal stem cells that may also be susceptible to stem cell-targeted therapies

Long-term outcomes among African-American and white women with breast cancer: What is the impact of comorbidity?

ObjectivesWe examined the association between comorbidity and long-term mortality from breast cancer and other causes among African-American and white women with breast cancer. MethodsA total of 170 African-American and 829 white women aged 40-84. years were followed for up to 28. years with median follow-up of 11.3 years in the Health and Functioning in Women (HFW) study. The impact of the Charlson Comorbidity Score (CCS) in the first few months following breast cancer diagnosis on the risk of mortality from breast cancer and other causes was examined using extended Cox models. ResultsMedian follow-up was significantly shorter for African-American women than their white counterparts (median 8.5 years vs. 12.3 years). Compared to white women, African-American women had significantly fewer years of education, greater body mass index, were more likely to have functional limitations and later stage at breast cancer diagnosis, and fewer had adequate financial resources (all P < 0.05). Proportionately more African-American women died of breast cancer than white women (37.1% vs. 31.4%, P = 0.15). A positive and statistically significant time-varying effect of the Charlson Comorbidity Score (CCS) on other-cause mortality persisted throughout the first 5. years of follow-up (P < 0.001) but not for its remainder. Conclusions Higher CCS was associated with increased risk of other-cause mortality, but not breast cancer specific mortality; the association did not differ among African-American and white women. © 2014 Elsevier Inc