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Radulescu, O.

Publications and source records attributed to Radulescu, O..

21 records · Page 2Linked to original sources

Spatio-Genetic and Phenotypic Modelling Elucidates Resistance and Re-Sensitisation to Treatment in Heterogeneous Melanoma

Although novel targeted therapies have significantly improved the overall survival of patients with advanced melanoma, understanding and combatting drug resistance remains a major clinical challenge. Using partial differential equations, we describe the evolution of a cellular population through time, space, and phenotype dimensions, in the presence of various drug species. We then use this framework to explore models in which resistance is attained by either mutations (irreversible) or plasticity (reversible). Numerical results suggest that punctuated evolutionary assumptions are more consistent with results obtained from murine melanoma models than gradual evolution. Furthermore, in the context of an evolving tumour cell population, sequencing the treatment, for instance applying immunotherapy before BRAF inhibitors, can increase treatment effectiveness. However, drug strategies which showed success within a spatially homogeneous tumour environment were unsuccessful under heterogeneous conditions, suggesting that spatio-environmental heterogeneity may be the greatest challenge to tumour therapies. Plastic metabolic models are additionally capable of reproducing the characteristic resistant tumour volume curves and predicting re-sensitisation to secondary waves of treatment observed in patient derived xenograft (PDX) melanomas treated with MEK and BRAF inhibitors. Nevertheless, secondary relapse due to a pre-adapted subpopulation, remaining after the first wave of treatment, results in a more rapid development of resistance. Our model provides a framework through which tumour resistance can be understood and would suggest that carefully phased treatments may be able to overcome the development of long-term resistance in melanoma.

cancer biology↗

Temporal Control of Transcription by Zelda in living Drosophila embryos

Abstract/introPioneer factors have the exquisite ability to engage their target sites at nucleosomal DNA, which leads to a local remodeling of chromatin and the establishment of a transcriptional competence. However, the direct impact of enhancer priming by pioneer factors on the temporal control of gene expression and on mitotic memory remains elusive. In Drosophila embryos, the maternally deposited activator Zelda (Zld) exhibits key pioneer factor properties and indeed regulates the awakening of the zygotic genome. The analysis of thousands of endogenous Zld bound regions in various genetic contexts, as well as the study of isolated synthetic enhancers with static approaches, led to the proposal that Zld could act as a quantitative developmental timer. Here we employ quantitative live imaging methods and mathematical modeling to directly test the effect of Zld on temporal coordination in gene activation and on mitotic memory. Using an automatic tracking software, we quantified the timing of activation in hundreds of nuclei and their progeny in Drosophila embryos. We demonstrate that increasing the number of Zld binding sites accelerates the kinetics of transcriptional activation regardless of their past transcriptional state. In spite of its known pioneering activities, we show that Zld is not a mitotic bookmarker and is neither necessary nor sufficient to foster mitotic memory. Fluorescent recovery after photo-bleaching and fluorescent correlation spectroscopy experiments reveal that, Zld is highly dynamic and exhibits transient binding to chromatin. We propose that Zld low binding rates could be compensated for by local accumulation of Zld in nuclear microenvironments in vivo, thus allowing rapid and coordinated gene activation.

developmental biology↗

Time Dependent Stochastic mRNA and Protein Synthesis in Piecewise-deterministic Models of Gene Networks.

We discuss piecewise-deterministic approximations of gene networks dynamics. These approximations capture in a simple way the stochasticity of gene expression and the propagation of expression noise in networks and circuits. By using partial omega expansions, piecewise deterministic approximations can be formally derived from the more commonly used Markov pure jump processes (chemical master equation). We are interested in time dependent multivariate distributions that describe the stochastic dynamics of the gene networks. This problem is difficult even in the simplified framework of piecewise-determinisitic processes. We consider three methods to compute these distributions: the direct Monte-Carlo, the numerical integration of the Liouville-master equation and the push-forward method. This approach is applied to multivariate fluctuations of gene expression, generated by gene circuits. We find that stochastic fluctuations of the proteome and much less those of the transcriptome can discriminate between various circuit topologies.

systems biology↗