Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology.Comment: 20 pages, 5 figures. In Press in Sci Rep 201

Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network

Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.Comment: 22 pages, 10 figure

The Role of Criticality of Gene Regulatory Networks on Emergent Properties of Biological Systems

The relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single cell level has been vigorously studied. However, the relationship between the criticality of GRNs and properties of multicellular organisms at a higher level has not been fully explored. Here we aim at revealing potential roles of the criticality of GRNs at a multicellular and hierarchical level, using a random Boolean network as a GRN. We perform three studies. Firstly, we propose a GRN-based morphogenetic model, and delve into the role of the criticality of GRNs in morphogenesis at a multicellular level. Secondly, we include an evolutionary context in our morphogenetic model by introducing genetic perturbations (e.g., mutations) to GRNs, and examine whether the role of the criticality of GRNs can be maintained even in the presence of the evolutionary perturbations. Also, we look into what the resulting morphologies are like and what kind of biological implications they have from the epigenetic viewpoint in morphology. Lastly, we present multilayer GRNs consisting of an intercellular layer and an intracellular layer. A network in an intercellular layer represents interactions between cells, and a network in an intracellular layer means interactions between genes. All the nodes of an intercellular network have identical intracellular GRNs. We investigate how the criticality of GRNs affects the robustness and evolvability of the multilayer GRNs at a hierarchical level, depending on cellular topologies and the number of links of an intercellular network. From the three studies, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies at a multicellular level, and generated robust and evolvable multilayer GRNs most frequently at a hierarchical level. Our findings indicate that the roles of the criticality of GRNs are hard to be discovered through the single-cell-level studies. It justifies the value of our research on the relationship between criticality of GRNs and properties of organisms in the context of multicellular settings

Etablierung des E2F1-Interaktoms metastasierungsrelevanter Faktoren durch Integration bioinformatischer und experimenteller Methoden

In dieser Arbeit wurde durch intensive Literatur- und Datenbankrecherche ein Protein-Protein/Gen-Interaktionsnetzwerk um den Transkriptionsfaktor E2F1 herum erstellt. Er ist Schlüsselfaktor für die epithelial-mesenchymale Transition (EMT), Voraussetzung für die Metastasierung. Eine anschließende bioinformatische Analyse identifizierte tumorspezifische Signaturen der E2F1-vermittelten EMT, welche experimentell und anhand von Patientendaten validiert wurden. Gemeinsame Zielgene des näher untersuchten E2F1-TGFβ-Interaktoms bieten mögliche Therapieziele für Krebspatienten.By intensive literature and database research we constructed a comprehensive map of interactions around the transcription factor E2F1, a key driver of the epithelial-mesenchymal transition (EMT) as a prerequisite for metastasis. The subsequent bioinformatics analysis of this map lead to the identification of tumour-specific molecular signatures of E2F1-driven EMT. These signatures were validated experimentally as well as on patient data. Common transcriptional targets of the investigated E2F1-TGFβ co-regulome might be suitable therapeutic targets for cancer patients

Robustness and evolvability of the human signaling network.

Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores

Robustness and Evolvability of the Human Signaling Network

Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores.Science Foundation IrelandNational Research Foundation of KoreaKorean Governmen