Human Gene Regulatory Network Research Articles

Distributed Heterogeneous Transfer Learning

Transfer learning has proved to be effective for building predictive models even in complex conditions with a low amount of available labeled data, by constructing a predictive model for a target domain also using the knowledge coming from a separate domain, called source domain. However, several existing transfer learning methods assume identical feature spaces between the source and the target domains. This assumption limits the possible real-world applications of such methods, since two separate, although related, domains could be described by totally different feature spaces. Heterogeneous transfer learning methods aim to overcome this limitation, but they usually i) make other assumptions on the features, such as requiring the same number of features, ii) are not generally able to distribute the workload over multiple computational nodes, iii) cannot work in the Positive-Unlabeled (PU) learning setting, which we also considered in this study, or iv) their applicability is limited to specific application domains, i.e., they are not general-purpose methods.In this manuscript, we present a novel distributed heterogeneous transfer learning method, implemented in Apache Spark, that overcomes all the above-mentioned limitations. Specifically, it is able to work also in the PU learning setting by resorting to a clustering-based approach, and can align totally heterogeneous feature spaces, without exploiting peculiarities of specific application domains. Moreover, our distributed approach allows us to process large source and target datasets.Our experimental evaluation was performed in three different application domains that can benefit from transfer learning approaches, namely the reconstruction of the human gene regulatory network, the prediction of cerebral stroke in hospital patients, and the prediction of customer energy consumption in power grids. The results show that the proposed approach is able to outperform 4 state-of-the-art heterogeneous transfer learning approaches and 3 baselines, and exhibits ideal performances in terms of scalability.

C-Biomarker.net: A Cytoscape app for the identification of cancer biomarker genes from cores of large biomolecular networks

Although there have been many studies revealing that biomarker genes for early cancer detection can be found in biomolecular networks, no proper tool exists to discover the cancer biomarker genes from various biomolecular networks. Accordingly, we developed a novel Cytoscape app called C-Biomarker.net, which can identify cancer biomarker genes from cores of various biomolecular networks. Derived from recent research, we designed and implemented the software based on parallel algorithms proposed in this study for working on high-performance computing devices. We tested our software on various network sizes and found the suitable size for each running mode on CPU or GPU. Interestingly, using the software for 17 cancer signaling pathways, we found that on average 70.59% of the top three nodes residing at the innermost core of each pathway are biomarker genes of the cancer respectively to the pathway. Similarly, by the software, we also found 100% of the top ten nodes at both cores of Human Gene Regulatory (HGR) network and Human Protein-Protein Interaction (HPPI) network are multi-cancer biomarkers. These case studies are reliable evidence for performance of cancer biomarker prediction function in the software. Through the case studies, we also suggest that true cores of directed complex networks should be identified by the algorithm of R-core rather than K-core as usual. Finally, we compared the prediction result of our software with those of other researchers and confirmed that our prediction method outperforms the other methods. Taken together, C-Biomarker.net is a reliable tool that efficiently detects biomarker nodes from cores of various large biomolecular networks. The software is available at https://github.com/trantd/C-Biomarker.net.

Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks.

Gene regulatory networks ensure that important genes are expressed at precise levels. When gene expression is sufficiently perturbed, it can lead to disease. To understand how gene expression disruptions percolate through a network, we must first map connections between regulatory genes and their downstream targets. However, we lack comprehensive knowledge of the upstream regulators of most genes. Here, we developed an approach for systematic discovery of upstream regulators of critical immune factors-IL2RA, IL-2 and CTLA4-in primary human T cells. Then, we mapped the network of the target genes of these regulators and putative cis-regulatory elements using CRISPR perturbations, RNA-seq and ATAC-seq. These regulators form densely interconnected networks with extensive feedback loops. Furthermore, this network is enriched for immune-associated disease variants and genes. These results provide insight into how immune-associated disease genes are regulated in T cells and broader principles about the structure of human gene regulatory networks.

DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network.

Cancer is a group of diseases that have received much attention in biological research because of its high mortality rate and the lack of accurate identification of its root causes. In such studies, researchers usually try to identify cancer driver genes (CDGs) that start cancer in a cell. The majority of the methods that have ever been proposed for the identification of CDGs are based on gene expression data and the concept of mutation in genomic data. Recently, using networking techniques and the concept of influence maximization, some models have been proposed to identify these genes. We aimed to construct the cancer transcriptional regulatory network and identify cancer driver genes using a network science approach without the use of mutation and genomic data. In this study, we will employ the social influence network theory to identify CDGs in the human gene regulatory network (GRN) that is based on the concept of influence and power of webpages. First, we will create GRN Networks using gene expression data and Existing nodes and edges. Next, we will implement the modified algorithm on GRN networks being studied by weighting the regulatory interaction edges using the influence spread concept. Nodes with the highest ratings will be selected as the CDGs. The results show our proposed method outperforms most of the other computational and network-based methods and show its superiority in identifying CDGs compared to many other methods. In addition, the proposed method canidentifymany CDGs that are overlooked by all previously published methods. Our study demonstrated that the Google's PageRank algorithm can be utilized and modified as a network-based method for identifying cancer driver gene in transcriptional regulatory network. Furthermore, the proposed method can be considered as a complementary method to the computational-based cancer driver gene identification tools.

Identification of Biomarker in Brain-specific Gene Regulatory Network Using Structural Controllability Analysis.

Brain tumor research has been stapled for human health while brain network research is crucial for us to understand brain activity. Here the structural controllability theory is applied to study three human brain-specific gene regulatory networks, including forebrain gene regulatory network, hindbrain gene regulatory network and neuron associated cells cancer related gene regulatory network, whose nodes are neural genes and the edges represent the gene expression regulation among the genes. The nodes are classified into two classes: critical nodes and ordinary nodes, based on the change of the number of driver nodes upon its removal. Eight topological properties (out-degree DO, in-degree DI, degree D, betweenness B, closeness CA, in-closeness CI, out-closeness CO and clustering coefficient CC) are calculated in this paper and the results prove that the critical genes have higher score of topological properties than the ordinary genes. Then two bioinformatic analysis are used to explore the biologic significance of the critical genes. On the one hand, the enrichment scores in several kinds of gene databases are calculated and reveal that the critical nodes are richer in essential genes, cancer genes and the neuron related disease genes than the ordinary nodes, which indicates that the critical nodes may be the biomarker in brain-specific gene regulatory network. On the other hand, GO analysis and KEGG pathway analysis are applied on them and the results show that the critical genes mainly take part in 14 KEGG pathways that are transcriptional misregulation in cancer, pathways in cancer and so on, which indicates that the critical genes are related to the brain tumor. Finally, by deleting the edges or routines in the network, the robustness analysis of node classification is realized, and the robustness of node classification is proved. The comparison of neuron associated cells cancer related GRN (Gene Regulatory Network) and normal brain-specific GRNs (including forebrain and hindbrain GRN) shows that the neuron-related cell cancer-related gene regulatory network is more robust than other types.

The impact of whole genome duplications on the human gene regulatory networks.

This work studies the effects of the two rounds of Whole Genome Duplication (WGD) at the origin of the vertebrate lineage on the architecture of the human gene regulatory networks. We integrate information on transcriptional regulation, miRNA regulation, and protein-protein interactions to comparatively analyse the role of WGD and Small Scale Duplications (SSD) in the structural properties of the resulting multilayer network. We show that complex network motifs, such as combinations of feed-forward loops and bifan arrays, deriving from WGD events are specifically enriched in the network. Pairs of WGD-derived proteins display a strong tendency to interact both with each other and with common partners and WGD-derived transcription factors play a prominent role in the retention of a strong regulatory redundancy. Combinatorial regulation and synergy between different regulatory layers are in general enhanced by duplication events, but the two types of duplications contribute in different ways. Overall, our findings suggest that the two WGD events played a substantial role in increasing the multi-layer complexity of the vertebrate regulatory network by enhancing its combinatorial organization, with potential consequences on its overall robustness and ability to perform high-level functions like signal integration and noise control. Lastly, we discuss in detail the RAR/RXR pathway as an illustrative example of the evolutionary impact of WGD duplications in human.

Human transcriptional gene regulatory network compiled from 14 data resources

The transcriptional regulatory network (TRN) in a cell orchestrates spatio-temporal expression of genes to generate cellular responses for maintenance, reproduction, development and survival of the cell and its hosting organism. Transcription factors (TF) regulate the expression of their target genes (TG) and are the fundamental units of TRN. Several databases have been developed to catalogue human TRN based on low- and high-throughput experimental and computational studies considering their importance in understanding cellular physiology. However, literature lacks their comparative assessment on the strengths and weaknesses. We compared over 2.2 million regulatory pairs between 1379 TF and 22,518 TG from 14 resources. Our study reveals that the TF and TG were common across data resources but not their regulatory pairs. TF and TG of the regulatory pairs showed weak expression correlation, significant gene ontology overlap, co-citations in PubMed and low numbers of TF-TG pairs representing transcriptional repression relationships. We assigned each TF-TG regulatory pair a combined confidence score reflecting its reliability based on its presence in multiple databases. The assembled TRN contains 2,246,598 TF-TG pairs, of which, 44,284 with information on TF's activating or repressing effects on their TG and is available upon request. This study brings the information about transcriptional regulation scattered over the literature and databases at one place in the form of one of the most comprehensive and complete human TRN assembled to date. It will be a valuable resource for benchmarking TRN prediction tools, and to the scientific community working in functional genomics, gene expression and regulation analysis.

Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction.

Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organization across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms. We propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature. The method, the datasets and all the results obtained in this study are available at: https://doi.org/10.6084/m9.figshare.c.5237687. Supplementary data are available at Bioinformatics online.

Extensive NEUROG3 occupancy in the human pancreatic endocrine gene regulatory network

ObjectiveMice lacking the bHLH transcription factor (TF) Neurog3 do not form pancreatic islet cells, including insulin-secreting beta cells, the absence of which leads to diabetes. In humans, homozygous mutations of NEUROG3 manifest with neonatal or childhood diabetes. Despite this critical role in islet cell development, the precise function of and downstream genetic programs regulated directly by NEUROG3 remain elusive. Therefore, we mapped genome-wide NEUROG3 occupancy in human induced pluripotent stem cell (hiPSC)–derived endocrine progenitors and determined NEUROG3 dependency of associated genes to uncover direct targets. MethodsWe generated a novel hiPSC line (NEUROG3-HA-P2A-Venus) where NEUROG3 is HA-tagged and fused to a self-cleaving fluorescent VENUS reporter. We used the CUT&RUN technique to map NEUROG3 occupancy and epigenetic marks in pancreatic endocrine progenitors (PEP) that were differentiated from this hiPSC line. We integrated NEUROG3 occupancy data with chromatin status and gene expression in PEPs as well as their NEUROG3-dependence. In addition, we investigated whether NEUROG3 binds type 2 diabetes mellitus (T2DM)–associated variants at the PEP stage. ResultsCUT&RUN revealed a total of 863 NEUROG3 binding sites assigned to 1263 unique genes. NEUROG3 occupancy was found at promoters as well as at distant cis-regulatory elements that frequently overlapped within PEP active enhancers. De novo motif analyses defined a NEUROG3 consensus binding motif and suggested potential co-regulation of NEUROG3 target genes by FOXA or RFX transcription factors. We found that 22% of the genes downregulated in NEUROG3−/− PEPs, and 10% of genes enriched in NEUROG3-Venus positive endocrine cells were bound by NEUROG3 and thus likely to be directly regulated. NEUROG3 binds to 138 transcription factor genes, some with important roles in islet cell development or function, such as NEUROD1, PAX4, NKX2-2, SOX4, MLXIPL, LMX1B, RFX3, and NEUROG3 itself, and many others with unknown islet function. Unexpectedly, we uncovered that NEUROG3 targets genes critical for insulin secretion in beta cells (e.g., GCK, ABCC8/KCNJ11, CACNA1A, CHGA, SCG2, SLC30A8, and PCSK1). Thus, analysis of NEUROG3 occupancy suggests that the transient expression of NEUROG3 not only promotes islet destiny in uncommitted pancreatic progenitors, but could also initiate endocrine programs essential for beta cell function. Lastly, we identified eight T2DM risk SNPs within NEUROG3-bound regions. ConclusionMapping NEUROG3 genome occupancy in PEPs uncovered unexpectedly broad, direct control of the endocrine genes, raising novel hypotheses on how this master regulator controls islet and beta cell differentiation.

Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks

The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer when the number of labeled examples is low or when no negative examples are available. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two. This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Simultaneously, we solve the issues arising from the limited availability of examples of links by relying on a novel clustering-based approach, able to estimate the degree of certainty of unlabeled examples of links, so that they can be exploited during the training together with the labeled examples. Our experiments show that the proposed method can reconstruct both the human and the mouse GRNs more effectively compared to reconstructing each network separately. Moreover, it significantly outperforms three state-of-the-art transfer learning approaches that, analogously to our method, can exploit the knowledge coming from both organisms. Finally, a specific robustness analysis reveals that, even when the number of labeled examples is very low with respect to the number of unlabeled examples, the proposed method is almost always able to outperform its single-task counterpart.

Comprehensive mapping of the human cytokine gene regulatory network.

Proper cytokine gene expression is essential in development, homeostasis and immune responses. Studies on the transcriptional control of cytokine genes have mostly focused on highly researched transcription factors (TFs) and cytokines, resulting in an incomplete portrait of cytokine gene regulation. Here, we used enhanced yeast one-hybrid (eY1H) assays to derive a comprehensive network comprising 1380 interactions between 265 TFs and 108 cytokine gene promoters. Our eY1H-derived network greatly expands the known repertoire of TF–cytokine gene interactions and the set of TFs known to regulate cytokine genes. We found an enrichment of nuclear receptors and confirmed their role in cytokine regulation in primary macrophages. Additionally, we used the eY1H-derived network as a framework to identify pairs of TFs that can be targeted with commercially-available drugs to synergistically modulate cytokine production. Finally, we integrated the eY1H data with single cell RNA-seq and phenotypic datasets to identify novel TF–cytokine regulatory axes in immune diseases and immune cell lineage development. Overall, the eY1H data provides a rich resource to study cytokine regulation in a variety of physiological and disease contexts.

Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach

BackgroundReactivation of the telomerase reverse transcriptase gene TERT is a central feature for unlimited proliferation of the majority of cancers. However, the underlying regulatory processes are only partly understood.ResultsWe assembled regulator binding information from serveral sources to construct a generic human and mouse gene regulatory network. Advancing our “Mixed Integer linear Programming based Regulatory Interaction Predictor” (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter.Our improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/KoenigLabNM/MIPRIP.ConclusionMIPRIP 2.0 identified common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.

GenCLiP 3: mining human genes' functions and regulatory networks from PubMed based on co-occurrences and natural language processing.

We present a web server, GenCLiP 3, which is an updated version of GenCLiP 2.0 to enhance analysis of human gene functions and regulatory networks, with the following improvements: i) accurate recognition of molecular interactions with polarity and directionality from the entire PubMed database; ii) support for Boolean search to customize multiple-term search and to quickly retrieve function related genes; iii) strengthened association between gene and keyword by a new scoring method; and iv) daily updates following literature release at PubMed FTP. The server is freely available for academic use at: http://ci.smu.edu.cn/genclip3/. Supplementary data are available at Bioinformatics online.

Exploiting transfer learning for the reconstruction of the human gene regulatory network.

The reconstruction of gene regulatory networks (GRNs) from gene expression data has received increasing attention in recent years, due to its usefulness in the understanding of regulatory mechanisms involved in human diseases. Most of the existing methods reconstruct the network through machine learning approaches, by analyzing known examples of interactions. However, (i) they often produce poor results when the amount of labeled examples is limited, or when no negative example is available and (ii) they are not able to exploit information extracted from GRNs of other (better studied) related organisms, when this information is available. In this paper, we propose a novel machine learning method that overcomes these limitations, by exploiting the knowledge about the GRN of a source organism for the reconstruction of the GRN of the target organism, by means of a novel transfer learning technique. Moreover, the proposed method is natively able to work in the positive-unlabeled setting, where no negative example is available, by fruitfully exploiting a (possibly large) set of unlabeled examples. In our experiments, we reconstructed the human GRN, by exploiting the knowledge of the GRN of Mus musculus. Results showed that the proposed method outperforms state-of-the-art approaches and identifies previously unknown functional relationships among the analyzed genes. http://www.di.uniba.it/∼mignone/systems/biosfer/index.html. Supplementary data are available at Bioinformatics online.

Somatic mutations in early metazoan genes disrupt regulatory links between unicellular and multicellular genes in cancer.

Extensive transcriptional alterations are observed in cancer, many of which activate core biological processes established in unicellular organisms or suppress differentiation pathways formed in metazoans. Through rigorous, integrative analysis of genomics data from a range of solid tumors, we show many transcriptional changes in tumors are tied to mutations disrupting regulatory interactions between unicellular and multicellular genes within human gene regulatory networks (GRNs). Recurrent point mutations were enriched in regulator genes linking unicellular and multicellular subnetworks, while copy-number alterations affected downstream target genes in distinctly unicellular and multicellular regions of the GRN. Our results depict drivers of tumourigenesis as genes that created key regulatory links during the evolution of early multicellular life, whose dysfunction creates widespread dysregulation of primitive elements of the GRN. Several genes we identified as important in this process were associated with drug response, demonstrating the potential clinical value of our approach.

A Microglial Signature Directing Human Aging and Neurodegeneration-Related Gene Networks.

Aging is regarded as a major risk factor for neurodegenerative diseases. Thus, a better understanding of the similarities between the aging process and neurodegenerative diseases at the cellular and molecular level may reveal better understanding of this detrimental relationship. In the present study, we mined publicly available gene expression datasets from healthy individuals and patients affected by neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease) across a broad age spectrum and compared those with mouse aging and mouse cell-type specific gene expression profiles. We performed weighted gene co-expression network analysis (WGCNA) and found a gene network strongly related with both aging and neurodegenerative diseases. This network was significantly enriched with a microglial signature as imputed from cell type-specific sequencing data. Since mouse models are extensively used for the study of human diseases, we further compared these human gene regulatory networks with age-specific mouse brain transcriptomes. We discovered significantly preserved networks with both human aging and human disease and identified 17 shared genes in the top-ranked immune/microglia module, among which we found five human hub genes TYROBP, FCER1G, ITGB2, MYO1F, PTPRC, and two mouse hub genes Trem2 and C1qa. Taken together, these results support the hypothesis that microglia are key players involved in human aging and neurodegenerative diseases, and suggest that mouse models should be appropriate for studying these microglial changes in human.

Optimized time-lag differential method for constructing gene regulatory network

One major molecular genetics control mechanism is transcriptional gene regulation where regulatory genes control other genes over time. The biological approaches to determine the Gene Regulatory Networks (GRN), require costly experimental setup and time for generating voluminous time series gene expression data. Computational model has been developed to generate GRN by analyzing time series gene expression data. Sometimes gene to gene interaction not performing in order due to change of activity in the cell, resulting regulatory error, creating difficulty to identify the target genes. Searching of regulatory genes with permissible error, producing maximum number of target genes is the real challenge for predicting the most important and instant regulating pathway of the human GRNs. The aim of the paper is to establish a computational methodology to reconstruct the GRN from short time series gene expression data with an objective to explain the observed behavior of biological systems. We employ nondominated sorting genetic algorithm (NSGA-II) to develop the proposed Optimized time-lag differential (OTD) algorithm, which identifies maximum number of target genes for the selected regulatory genes within minimum regulating time. Experiments have been conducted using eight different time series microarray data set showing better performance compare to others.

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

Systematic target function annotation of human transcription factors

BackgroundTranscription factors (TFs), the key players in transcriptional regulation, have attracted great experimental attention, yet the functions of most human TFs remain poorly understood. Recent capabilities in genome-wide protein binding profiling have stimulated systematic studies of the hierarchical organization of human gene regulatory network and DNA-binding specificity of TFs, shedding light on combinatorial gene regulation. We show here that these data also enable a systematic annotation of the biological functions and functional diversity of TFs.ResultWe compiled a human gene regulatory network for 384 TFs covering the 146,096 TF–target gene (TF–TG) relationships, extracted from over 850 ChIP-seq experiments as well as the literature. By integrating this network of TF–TF and TF–TG relationships with 3715 functional concepts from six sources of gene function annotations, we obtained over 9000 confident functional annotations for 279 TFs. We observe extensive connectivity between TFs and Mendelian diseases, GWAS phenotypes, and pharmacogenetic pathways. Further, we show that TFs link apparently unrelated functions, even when the two functions do not share common genes. Finally, we analyze the pleiotropic functions of TFs and suggest that the increased number of upstream regulators contributes to the functional pleiotropy of TFs.ConclusionOur computational approach is complementary to focused experimental studies on TF functions, and the resulting knowledge can guide experimental design for the discovery of unknown roles of TFs in human disease and drug response.

Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis.

The development of disease involves a systematic disturbance inside cells and is associated with changes in the interactions or regulations among genes forming biological networks. The bridges inside a network are critical in shortening the distances between nodes. We observed that, inside the human gene regulatory network, one strongly connected core bridged the whole network. Other regulations outside the core formed a weakly connected component surrounding the core like a peripheral structure. Furthermore, the regulatory feedback loops (FBLs) inside the core compose an interface-like structure between the core and periphery. We then denoted the regulatory FBLs as the interface core. Notably, both the cancer-associated and essential biomolecules and regulations were significantly overrepresented in the interface core. These results implied that the interface core is not only critical for the network structure but central in cellular systems. Furthermore, the enrichment of the cancer-associated and essential regulations in the interface core might be attributed to its bridgeness in the network. More importantly, we identified one regulatory FBL between HNF4A and NR2F2 that possesses the highest bridgeness in the interface core. Further investigation suggested that the disturbance of the HNF4A-NR2F2 FBL might protect tumor cells from apoptotic processes. Our results emphasize the relevance of the regulatory network properties to cellular systems and might reveal a critical role of the interface core in cancer.