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Try out PMC Labs and tell us what you think. Learn More. With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities e. Network representation is frequently used to capture the presence of these molecules as well as their relationship.
Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time.
We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors.
These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify ly overlooked problems, leading to rapid advances in medicine. Next-generation sequencing NGS and other high-throughput experiments highlight one of the most ificant advances in molecular biology over the past decade.
Such technological improvements enable a large of molecules, including genes, transcripts, and proteins to be simultaneously measured in different conditions over time. The key challenges and bottlenecks of the modern-day molecular biology have shifted from simply gathering information to the analysis and interpretation of large quantities of data that can now be obtained.
Network representations have been widely used in physics and social science for decades, and are now among the most frequently used tools in systems biology. This technique provides not only a systematic representation of both the presence and abundance of biological molecules, but also displays the relationships or interactions between them. Networks have been used to represent the interactions between different types of biological molecules, e. Analyses of network sub-structures have revealed fundamental insights into how biological molecules are organized [ 17 — 20 ], which would not have been possible by studying individual genes or proteins.
Network representation and analysis has been successfully applied to study many systems in molecular biology [ 21 ]; however, the use of these tools in translational medicine and drug discovery is relatively new [ 22 — 24 ].
This might be due in part to the knowledge and understanding gaps between clinicians and systems biologists. By convention, clinicians typically focus on specific sets of key genetic markers associated with diseases, to identify the most probable drug targets. In contrast, systems biologists have strong computational and analytical skills, but frequently lack hands-on experimental experience.
The lack of interaction of systems biologists with patients can prevent a full appreciation of the complexity of the problems and hindrances in biomedical research [ 2526 ]. In this review, we aim to improve the understanding of challenges in biomedical research and establish a common ground between clinicians and systems biologists to further promote the application of network biology in translational medicine. We first outline the fundamental concepts of a network representation. In biology, nodes can represent biological molecules such as genes, proteins, and ligands, or even larger entities such as cells or individual humans.
Edges represent physical interactions or contacts between biological molecules, biochemical processes between substrates and products, genetic interactions between genes, and in some cases, interactions between cells or individual organisms. Interaction networks Left represent direct interactions between biological molecules e. The interactions represented include direct physical interaction e.
Biological information described in a network is not restricted to the presence of nodes and their relationships. The size of node, for instance, can reflect abundance of biological molecules e. Likewise, the thickness of an edge or the distance between nodes may represent the frequency or strength of pairwise interaction e. In addition, edges can be directional or non-directional, solid or dotted, depending on the types of interactions. Thus, networks are information-rich representations, which are widely used to summarize, visualize, and analyze large-scale datasets obtained from high-throughput experiments.
To give an overview of the current application of networks in biomedical-related fields, here we review two major types of biological networks. For instance, protein interaction networks, i.
In humans, analyses of protein—protein interaction networks have shown that dysfunctional interactions can lead to several diseases including neurological disorders such as ataxias [ 29 ], autism [ 30 ], several types of cancers including breast [ 31 ] and colorectal cancers [ 32 ], acute lymphoblastic leukemia [ 33 ], as well as other inheritable genetic diseases [ 34 — 37 ].
Transcriptional regulation networks also known as Gene Regulatory Networks, GRNs are widely used to illustrate the binding events of regulatory proteins, such as transcription factors, to the promoters of targeted genes, and this technique has been employed in the analysis of bacteria [ 38 ], budding yeasts [ 9 ], worms [ 39 ], and embryonic stem cells [ 4041 ].
GRNs are directional, and the relationship between two nodes is represented by an arrow starting from a regulator and pointing toward a targeted gene. Mis-regulation of gene expression le to various diseases especially cancers, as seen in the genome-wide transcription network of the vertebrate transcription factor SOX4 [ 42 ], and the androgen receptor, a transcription factor that regulates the onset and progression of prostate cancer [ 43 ]. Interaction networks have also been used to describe the binding and affinity of ligands or small molecules to targeted proteins.
As seen in a drug-target network [ 44 ], a list of drugs approved by the Food and Drug Administration FDA were linked to proteins according to drug-target binary associations. The analysis of these networks revealed that many drugs have overlapping but not identical sets of targets.
In addition, the network analysis indicated that new drugs tend to be, at least partly, linked to well-characterized proteins already targeted by ly developed drugs. This suggests that the pharmaceutical industry might be shifting toward polypharmacology, to systematically address complex diseases using multiple drugs aimed at multiple specific targets in related pathways to improve treatment efficacy [ 4546 ].
Metabolic networks differs from other networks described earlier in the sense that the edges between two nodes metabolites do not represent physical contacts, but instead biochemical reactions that convert one metabolite to another. Recent studies have reconstructed and explored genome-scale metabolic networks in pathogenic microbes including Staphylococcus aureus [ 47 ], M. These analyses may lead to a better understanding of host-pathogen interactions, and could aid in the de of drugs that specifically target the metabolic pathways of microbes and cause minimal interference with those of the hosts.
Co-expression networks are widely used as a starting point for inferring the cellular functions of uncharacterized genes, as in many cases, genes with related functions show overlapping expression patterns [ 53 ]. New disease markers can be discovered from clusters of genes that are co-expressed with known disease-associated genes, as they frequently show differential expression between the normal and diseased populations [ 54 — 57 ].
Other association networks include drug target-protein networks [ 44 ], where each node is a protein and two proteins are linked if they are targeted by the same compounds. These networks can be computationally derived from the drug-target network described in the section. It provides a complementary protein-centric view by focusing on the proteins that are often co-targeted, and might be involved in related pathways. Conversely, two or more drugs can be linked in a network based on common properties, such as targeting specific proteins or side effects.
It has been shown that documented adverse side effects could be used to infer molecular drug-target interactions [ 58 ]. This type of network has the potential to predict whether or not existing and routinely used drugs have additional unknown off-targets, allowing for these drugs to be candidates for additional, distinct therapeutic. Illustrations of the potential of alternative uses for current drugs are sildenafil, losartan, and fenofibrate.
Sildenafil e. Fenofibrate, a drug mainly used for controlling cholesterol levels in cardiovascular patients, has also been shown to suppress growth of hepatocellular carcinoma [ 61 ]. Global Free text sex chat in Natini networks offer a useful insight into how human disorders are related. Complementarily, the gene-centric version of this network comprises nodes of disease genes, linked if they are associated with the same disorders. Such networks not only represent a framework to visualize all known disease genotype-phenotype associations, but also reveal that human diseases are much more genetically related than ly appreciated [ 63 ].
This is highlighted by a gigantic network comprising over interconnected human diseases [ 7 ]. In addition to being a framework for visualizing and documenting all the known relationships between nodes, earlier analyses of large-scale networks from high-throughput studies have revealed many interesting biologically relevant properties, which cannot be obtained by studying genes and proteins individually [ 64 — 66 ]. In contrast to hubs, the majority of nodes in the network have much fewer connections.
Several studies have documented similar observation for biological networks, including protein—protein interaction networks [ 61773 ] and metabolic networks [ 1574 ]. Because of their connectivity distribution, scale-free networks are robust against random deletion of nodes. That is, the connections between a node and most other nodes remain intact, if nodes are removed randomly. In contrast, scale-free networks quickly become non-functional if hubs are targeted.
Earlier studies have shown that many pathogenic organisms have evolved to target the central components i. Similarly, one would expect drugs that specifically inhibit the central components of the regulatory circuits in a pathogen will rapidly disrupt their homeostatic processes, and thus efficiently eliminate them. As a result, these hubs from pathogenic organisms could be promising candidates for novel drugs. Network connectivity distribution is one of the better-studied areas, and a of insightful reviews and analyses are available [ 7778 ].
Another interesting example of biological network properties are the network motifs, which are sets of well-defined interconnection patterns between nodes [ 19 ]. These connectivity patterns, or network sub-circuits, recur in biological networks at a frequency ificantly higher than in randomized networks [ 79 — 81 ], ifying their important roles as building blocks for the large-scale organization of interactions. The patterns and proportions of sub-circuits used in different networks are distinct, depending on the functionality required under different conditions.
Interestingly, it has been shown in a yeast transcription regulatory network that sub-network structures, facilitating fast al propagation e. DNA damage or diauxic shiftbecause a rapid response is required against the stressors. In contrast, motifs that buffer spurious inputs or only respond to persistent als e.
Using a transistor radio as an analog of a biological system, Yuri Lazebnik described how a biologist would fix a broken radio, assuming no prior knowledge of how the radio components were wired together [ 83 ]. A traditional biological approach would involve removing gene knockout, mutagenesis each part of a functioning radio and track the changes in performance phenotype. In contrast, a typical engineering approach would involve systematic reconstruction of a component diagram from a normal radio e. Can a similar problem-solving mindset help expedite advances in biomedical research?
If regulatory circuits that control biological activities in a human body can be represented using a complex network, then a diseased state would be expected to occur when the normal state of the network is perturbed. Failure of key components e. Diseased perturbations can occur at different regulatory levels, as illustrated in Fig. Firstly, the absence or malfunction in important network components can lead to diseases, such as the loss of a particular gene.Free text sex chat in Natini
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