Thermal effects vary predictably across levels of organization: empirical results and theoretical basis.

Thermal performance curves have provided a common framework to study the impact of temperature in biological systems. However, few generalities have emerged to date. Here, we combine an experimental approach with theoretical analyses to demonstrate that performance curves are expected to vary predictably with the levels of biological organization. We measured rates of enzymatic reactions, organismal performance and population viability in Drosophila acclimated to different thermal conditions and show that performance curves become narrower with thermal optima shifting towards lower temperatures at higher levels or organization. We then explain these results on theoretical grounds, showing that this pattern reflects the cumulative impact of asymmetric thermal effects that piles up with complexity. These results and the proposed framework are important to understand how organisms, populations and ecological communities might respond to changing thermal conditions.

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook.

Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.