Computational identification of prognostic biomarkers capable of withstanding follow-up validation efforts is still an open challenge in cancer research. For instance, several gene expression profiles analysis methods have been developed to identify gene signatures that can classify cancer sub-phenotypes associated with poor prognosis. However, signatures originating from independent studies show only minimal overlap and perform poorly when classifying datasets other than the ones they were generated from. In this paper, we propose a computational systems biology approach that can infer robust prognostic markers by identifying upstream Master Regulators, causally related to the presentation of the phenotype of interest. Such a strategy effectively extends and complements other existing methods and may help further elucidate the molecular mechanisms of the observed pathophysiological phenotype. Results show that inferred regulators substantially outperform canonical gene signatures both on the original dataset and across distinct datasets.
Detecting relative rather than absolute changes in extracellular signals enables cells to make decisions in constantly fluctuating environments. It is currently not well understood how mammalian signaling networks store the memories of past stimuli and subsequently use them to compute relative signals, that is perform fold change detection. Using the growth factor-activated PI3K-Akt signaling pathway, we develop here computational and analytical models, and experimentally validate a novel non-transcriptional mechanism of relative sensing in mammalian cells. This mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. The surface receptor abundance is regulated by background signal-dependent receptor endocytosis and down-regulation. We show the robustness and specificity of relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. Our results suggest that similar mechanisms of cell memory and fold change detection may be important in diverse signaling cascades and multiple biological contexts.
Highlights d Efficient approach to infer signaling parameter distributions from single-cell data d Nonparametric inference of parameter distributions using maximum entropy principle d Investigation of population heterogeneity in phosphorylation cascades
Predictive models of signaling networks are essential tools for understanding cell population heterogeneity and designing rational interventions in disease. However, using network models to predict signaling dynamics heterogeneity is often challenging due to the extensive variability of signaling parameters across cell populations. Here, we describe a Maximum Entropy-based fRamework for Inference of heterogeneity in Dynamics of sIgAling Networks (MERIDIAN). MERIDIAN allows us to estimate the joint probability distribution over signaling parameters that is consistent with experimentally observed cell-to-cell variability in abundances of network species. We apply the developed approach to investigate the heterogeneity in the signaling network activated by the epidermal growth factor (EGF) and leading to phosphorylation of protein kinase B (Akt). Using the inferred parameter distribution, we also predict heterogeneity of phosphorylated Akt levels and the distribution of EGF receptor abundance hours after EGF stimulation. We discuss how MERIDIAN can be generalized and applied to problems beyond modeling of heterogeneous signaling dynamics.
A synapomorphy is a phylogenetic character that provides evidence of shared descent. Ideally a synapomorphy is ubiquitous within the clade of related organisms and nonexistent outside the clade, implying that it arose after divergence from other extant species and before the last common ancestor of the clade. With the recent proliferation of genetic sequence data, molecular synapomorphies have assumed great importance, yet there is no convenient means to search for them over entire genomes. We have developed a new program called Conserv, which can rapidly assemble orthologous sequences and rank them by various metrics, such as degree of conservation or divergence from out-group orthologs. We have used Conserv to conduct a largescale search for molecular synapomorphies for bacterial clades. The search discovered sequences unique to clades, such as Actinobacteria, Firmicutes and γ-Proteobacteria, and shed light on several open questions, such as whether Symbiobacterium thermophilum belongs with Actinobacteria or Firmicutes. We conclude that Conserv can quickly marshall evidence relevant to evolutionary questions that would be much harder to assemble with other tools.
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