Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability

Using indirect methods to constrain symbiotic nitrogen fixation rates : a case study from an Amazonian rain forest

© The Authors 2009. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License. The definitive version was published in Biogeochemistry 99 (2010): 1-13, doi:10.1007/s10533-009-9392-y.Human activities have profoundly altered the global nitrogen (N) cycle. Increases in anthropogenic N have had multiple effects on the atmosphere, on terrestrial, freshwater and marine ecosystems, and even on human health. Unfortunately, methodological limitations challenge our ability to directly measure natural N inputs via biological N fixation (BNF)—the largest natural source of new N to ecosystems. This confounds efforts to quantify the extent of anthropogenic perturbation to the N cycle. To address this gap, we used a pair of indirect methods—analytical modeling and N balance—to generate independent estimates of BNF in a presumed hotspot of N fixation, a tropical rain forest site in central Rondônia in the Brazilian Amazon Basin. Our objectives were to attempt to constrain symbiotic N fixation rates in this site using indirect methods, and to assess strengths and weaknesses of this approach by looking for areas of convergence and disagreement between the estimates. This approach yielded two remarkably similar estimates of N fixation. However, when compared to a previously published bottom-up estimate, our analysis indicated much lower N inputs via symbiotic BNF in the Rondônia site than has been suggested for the tropics as a whole. This discrepancy may reflect errors associated with extrapolating bottom-up fluxes from plot-scale measures, those resulting from the indirect analyses, and/or the relatively low abundance of legumes at the Rondônia site. While indirect methods have some limitations, we suggest that until the technological challenges of directly measuring N fixation are overcome, integrated approaches that employ a combination of model-generated and empirically-derived data offer a promising way of constraining N inputs via BNF in natural ecosystems.We acknowledge and are grateful for financial support from the Andrew W. Mellon Foundation (C.C. and B.H.), the National Science Foundation (NSF DEB-0515744 to C.C. and A.T. and DEB-0315656 to C.N.), and the NASA LBA Program (NCC5-285 to C.N.)

Modeling complex ecological economic systems: toward an evolutionary, dynamic understanding of people and nature

Recent understanding about system dynamics and predictability that has emerged from the study of complex systems is creating new tools for modeling interactions between anthropogenic and natural systems. A range of techniques has become available through advances in computer speed and accessibility and by implementing a broad, interdisciplinary systems view

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p