Network medicine

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Network medicine is the application of network science towards identifying, preventing, and treating diseases. This field focuses on using network topology and network dynamics towards identifying diseases and developing medical drugs. Biological networks, such as protein-protein interactions and metabolic pathways, are utilized by network medicine. Disease networks, which map relationships between diseases and biological factors, also play an important role in the field. Epidemiology is extensively studied using network science as well; social networks and transportation networks are used to model the spreading of disease across populations. Network medicine is a medically focused area of systems biology.

Contents

Background

The term "network medicine" was introduced by Albert-László Barabási in an the article "Network Medicine – From Obesity to the 'Diseasome'", published in The New England Journal of Medicine , in 2007. Barabási states that biological systems, similarly to social and technological systems, contain many components that are connected in complicated relationships but are organized by simple principles. Relaying on the tools and principles of network theory, [1] the organizing principles can be analyzed by representing systems as complex networks, which are collections of nodes linked together by a particular biological or molecular relationship. For networks pertaining to medicine, nodes represent biological factors (biomolecules, diseases, phenotypes, etc.) and links (edges) represent their relationships (physical interactions, shared metabolic pathway, shared gene, shared trait, etc.). [2]

Barabasi suggested that understanding human disease requires us to focus on three key networks, the metabolic network, the disease network, and the social network. The network medicine is based on the idea that understanding complexity of gene regulation, metabolic reactions, and protein-protein interactions and that representing these as complex networks will shed light on the causes and mechanisms of diseases. It is possible, for example, to infer a bipartite graph representing the connections of diseases to their associated genes using the OMIM database. [3] The projection of the diseases, called the human disease network (HDN), is a network of diseases connected to each other if they share a common gene. Using the HDN, diseases can be classified and analyzed through the genetic relationships between them. Network medicine has proven to be a valuable tool in analyzing big biomedical data. [4]

Research areas

Interactome

The whole set of molecular interactions in the human cell, also known as the interactome, can be used for disease identification and prevention. [5] These networks have been technically classified as scale-free, disassortative, small-world networks, having a high betweenness centrality. [6]

Protein-protein interactions have been mapped, using proteins as nodes and their interactions between each other as links. [7] These maps utilize databases such as BioGRID and the Human Protein Reference Database. The metabolic network encompasses the biochemical reactions in metabolic pathways, connecting two metabolites if they are in the same pathway. [8] Researchers have used databases such as KEGG to map these networks. Others networks include cell signaling networks, gene regulatory networks, and RNA networks.

Using interactome networks, one can discover and classify diseases, as well as develop treatments through knowledge of its associations and their role in the networks. One observation is that diseases can be classified not by their principle phenotypes (pathophenotype) but by their disease module, which is a neighborhood or group of components in the interactome that, if disrupted, results in a specific pathophenotype. [5] Disease modules can be used in a variety of ways, such as predicting disease genes that have not been discovered yet. Therefore, network medicine looks to identify the disease module for a specific pathophenotype using clustering algorithms.

Diseasome

Human disease networks, also called the diseasome, are networks in which the nodes are diseases and the links, the strength of correlation between them. This correlation is commonly quantified based on associated cellular components that two diseases share. The first-published human disease network (HDN) looked at genes, finding that many of the disease associated genes are non-essential genes, as these are the genes that do not completely disrupt the network and are able to be passed down generations. [3] Metabolic disease networks (MDN), in which two diseases are connected by a shared metabolite or metabolic pathway, have also been extensively studied and is especially relevant in the case of metabolic disorders. [9]

Three representations of the diseasome are: [6]

Some disease networks connect diseases to associated factors outside the human cell. Networks of environmental and genetic etiological factors linked with shared diseases, called the "etiome", can be also used to assess the clustering of environmental factors in these networks and understand the role of the environment on the interactome. [11] The human symptom-disease network (HSDN), published in June 2014, showed that the symptoms of disease and disease associated cellular components were strongly correlated and that diseases of the same categories tend to form highly connected communities, with respect to their symptoms. [12]

Pharmacology

Network pharmacology is a developing field based in systems pharmacology that looks at the effect of drugs on both the interactome and the diseasome. [13] The topology of a biochemical reaction network determines the shape of drug dose-response curve [14] as well as the type of drug-drug interactions, [15] thus can help design efficient and safe therapeutic strategies. In addition, the drug-target network (DTN) can play an important role in understanding the mechanisms of action of approved and experimental drugs. [16] The network theory view of pharmaceuticals is based on the effect of the drug in the interactome, especially the region that the drug target occupies. Combination therapy for a complex disease (polypharmacology) is suggested in this field since one active pharmaceutical ingredient (API) aimed at one target may not affect the entire disease module. [13] The concept of disease modules can be used to aid in drug discovery, drug design, and the development of biomarkers for disease detection. [2] There can be a variety of ways to identifying drugs using network pharmacology; a simple example of this is the "guilt by association" method. This states if two diseases are treated by the same drug, a drug that treats one disease may treat the other. [17] Drug repurposing, drug-drug interactions and drug side-effects have also been studied in this field. [18] [2] The next iteration of network pharmacology used entirely different disease definitions, defined as dysfunction in signaling modules derived from protein-protein interaction modules. The latter as well as the interactome had many conceptual shortcomings, e.g., each protein appears only once in the interactome, whereas in reality, one protein can occur in different contexts and different cellular locations. Such signaling modules are therapeutically best targeted at several sites, which is now the new and clinically applied definition of network pharmacology. To achieve higher than current precision, patients must not be selected solely on descriptive phenotypes but also based on diagnostics that detect the module dysregulation. Moreover, such mechanism-based network pharmacology has the advantage that each of the drugs used within one module is highly synergistic, which allows for reducing the doses of each drug, which then reduces the potential of these drugs acting on other proteins outside the module and hence the chance for unwanted side effects. [19]

Network epidemics

Network epidemics has been built by applying network science to existing epidemic models, as many transportation networks and social networks play a role in the spread of disease. [20] Social networks have been used to assess the role of social ties in the spread of obesity in populations. [21] Epidemic models and concepts, such as spreading and contact tracing, have been adapted to be used in network analysis. [22] These models can be used in public health policies, in order to implement strategies such as targeted immunization [23] and has been recently used to model the spread of the Ebola virus epidemic in West Africa across countries and continents. [24] [25]

Drug prescription networks (DPNs)

Recently, some researchers tended to represent medication use in form of networks. The nodes in these networks represent medications and the edges represent some sort of relationship between these medications. Cavallo et al. (2013) [26] described the topology of a co-prescription network to demonstrate which drug classes are most co-prescribed. Bazzoni et al. (2015) [27] concluded that the DPNs of co-prescribed medications are dense, highly clustered, modular and assortative. Askar et al. (2021) [28] created a network of the severe drug-drug interactions (DDIs) showing that it consisted of many clusters.

Other networks

The development of organs [29] and other biological systems can be modelled as network structures where the clinical (e.g., radiographic, functional) characteristics can be represented as nodes and the relationships between these characteristics are represented as the links among such nodes. [30] Therefore, it is possible to use networks to model how organ systems dynamically interact.

Educational and clinical implementation

The Channing Division of Network Medicine at Brigham and Women's Hospital was created in 2012 to study, reclassify, and develop treatments for complex diseases using network science and systems biology. [31] It currently involves more than 80 Harvard Medical School (HMS) faculty and focuses on three areas:

Massachusetts Institute of Technology offers an undergraduate course called "Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics". [33] Also, Harvard Catalyst (The Harvard Clinical and Translational Science Center) offers a three-day course entitled "Introduction to Network Medicine", open to clinical and science professionals with doctorate degrees. [34]

Current worldwide efforts in network medicine are coordinated by the Network Medicine Institute and Global Alliance, representing 33 leading universities and institutions around the world committed to improving global health.

See also

Related Research Articles

<span class="mw-page-title-main">Computational biology</span> Branch of biology

Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics. It differs from biological computing, a subfield of computer science and engineering which uses bioengineering to build computers.

<span class="mw-page-title-main">Gene regulatory network</span> Collection of molecular regulators

A generegulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo).

<span class="mw-page-title-main">Systems biology</span> Computational and mathematical modeling of complex biological systems

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.

<span class="mw-page-title-main">Protein complex</span> Type of stable macromolecular complex

A protein complex or multiprotein complex is a group of two or more associated polypeptide chains. Protein complexes are distinct from multidomain enzymes, in which multiple catalytic domains are found in a single polypeptide chain.

<span class="mw-page-title-main">Interactome</span> Complete set of molecular interactions in a biological cell

In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules but can also describe sets of indirect interactions among genes.

Modelling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems. It involves the use of computer simulations of biological systems, including cellular subsystems, to both analyze and visualize the complex connections of these cellular processes.

<span class="mw-page-title-main">Albert-László Barabási</span> Hungarian-American physicist (born 1967)

Albert-László Barabási is a Romanian-born Hungarian-American physicist, best known for his discoveries in network science and network medicine.

<span class="mw-page-title-main">Protein–protein interaction</span> Physical interactions and constructions between multiple proteins

Protein–protein interactions (PPIs) are physical contacts of high specificity established between two or more protein molecules as a result of biochemical events steered by interactions that include electrostatic forces, hydrogen bonding and the hydrophobic effect. Many are physical contacts with molecular associations between chains that occur in a cell or in a living organism in a specific biomolecular context.

<span class="mw-page-title-main">Metabolic network</span> Set of biological pathways

A metabolic network is the complete set of metabolic and physical processes that determine the physiological and biochemical properties of a cell. As such, these networks comprise the chemical reactions of metabolism, the metabolic pathways, as well as the regulatory interactions that guide these reactions.

<span class="mw-page-title-main">KEGG</span> Collection of bioinformatics databases

KEGG is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances. KEGG is utilized for bioinformatics research and education, including data analysis in genomics, metagenomics, metabolomics and other omics studies, modeling and simulation in systems biology, and translational research in drug development.

<span class="mw-page-title-main">Biological network inference</span> Type of inference

Biological network inference is the process of making inferences and predictions about biological networks. By using these networks to analyze patterns in biological systems, such as food-webs, we can visualize the nature and strength of these interactions between species, DNA, proteins, and more.

Fluxomics describes the various approaches that seek to determine the rates of metabolic reactions within a biological entity. While metabolomics can provide instantaneous information on the metabolites in a biological sample, metabolism is a dynamic process. The significance of fluxomics is that metabolic fluxes determine the cellular phenotype. It has the added advantage of being based on the metabolome which has fewer components than the genome or proteome.

The Comparative Toxicogenomics Database (CTD) is a public website and research tool launched in November 2004 that curates scientific data describing relationships between chemicals/drugs, genes/proteins, diseases, taxa, phenotypes, GO annotations, pathways, and interaction modules. The database is maintained by the Department of Biological Sciences at North Carolina State University.

<span class="mw-page-title-main">Biological network</span> Method of representing systems

A biological network is a method of representing systems as complex sets of binary interactions or relations between various biological entities. In general, networks or graphs are used to capture relationships between entities or objects. A typical graphing representation consists of a set of nodes connected by edges.

Systems pharmacology is the application of systems biology principles to the field of pharmacology. It seeks to understand how drugs affect the human body as a single complex biological system. Instead of considering the effect of a drug to be the result of one specific drug-protein interaction, systems pharmacology considers the effect of a drug to be the outcome of the network of interactions a drug may have. In 1992, an article on systems medicine and pharmacology was published in China. Networks of interaction may include chemical-protein, protein–protein, genetic, signalling and physiological. Systems pharmacology uses bioinformatics and statistics techniques to integrate and interpret these networks.

In bioinformatics, a Gene Disease Database is a systematized collection of data, typically structured to model aspects of reality, in a way to comprehend the underlying mechanisms of complex diseases, by understanding multiple composite interactions between phenotype-genotype relationships and gene-disease mechanisms. Gene Disease Databases integrate human gene-disease associations from various expert curated databases and text mining derived associations including Mendelian, complex and environmental diseases.

The human interactome is the set of protein–protein interactions that occur in human cells. The sequencing of reference genomes, in particular the Human Genome Project, has revolutionized human genetics, molecular biology, and clinical medicine. Genome-wide association study results have led to the association of genes with most Mendelian disorders, and over 140 000 germline mutations have been associated with at least one genetic disease. However, it became apparent that inherent to these studies is an emphasis on clinical outcome rather than a comprehensive understanding of human disease; indeed to date the most significant contributions of GWAS have been restricted to the “low-hanging fruit” of direct single mutation disorders, prompting a systems biology approach to genomic analysis. The connection between genotype and phenotype remain elusive, especially in the context of multigenic complex traits and cancer. To assign functional context to genotypic changes, much of recent research efforts have been devoted to the mapping of the networks formed by interactions of cellular and genetic components in humans, as well as how these networks are altered by genetic and somatic disease.

The first phenotypic disease network was constructed by Hidalgo et al. (2009) to help understand the origins of many diseases and the links between them. Hidalgo et al. (2009) defined diseases as specific sets of phenotypes that affect one or several physiological systems, and compiled data on pairwise comorbidity correlations for more than 10,000 diseases reconstructed from over 30 million medical records. Hidalgo et al. (2009) presented their data in the form of a network with diseases as the nodes and comorbidity correlations as the links. Intuitively, the phenotypic disease network (PDN) can be seen as a map of the phenotypic space whose structure can contribute to the understanding of disease progression.

References

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  4. ↑ Sonawane, Abhijeet R.; Weiss, Scott T.; Glass, Kimberly; Sharma, Amitabh (2019). "Network Medicine in the Age of Biomedical Big Data". Frontiers in Genetics. 10: 294. doi: 10.3389/fgene.2019.00294 . ISSN   1664-8021. PMC   6470635 . PMID   31031797.
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  12. ↑ Zhou, X., Menche, J., Barabási, A. L., & Sharma, A. (2014). Human symptoms–disease network. Nature Communications, 5.
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  31. ↑ "Channing Division of Network Medicine".
  32. ↑ "Yang-Yu Liu – Harvard Catalyst Profiles – Harvard Catalyst".
  33. ↑ Dr. Michael Lee. "Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics". MIT OpenCourseWare.
  34. ↑ "Introduction to Network Medicine – Harvard Catalyst".
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