Chronic obstructive pulmonary disease (COPD) is a complex disease at the clinical, cellular, and molecular levels. However, its diagnosis, assessment, and therapeutic management are based almost exclusively on the severity of airflow limitation. A better understanding of the multiple dimensions of COPD and its relationship to other diseases is very relevant and of high current interest. Recent theoretical (scale-free networks), technological (high-throughput technology, biocomputing), and analytical improvements (systems biology) provide tools capable of addressing the complexity of COPD. The information obtained from the integrated use of those techniques will be eventually incorporated into routine clinical practice. This review summarizes our current knowledge in this area and offers an insight into the elements needed to progress toward an integrated, multilevel view of COPD based on the novel scientific strategy of systems biology and its potential clinical derivative, P4 medicine (Personalized, Predictive, Preventive, and Participatory).
The diagnosis, assessment of severity, and therapy of chronic obstructive pulmonary disease (COPD) are guided primarily by the degree of airflow limitation (1). However, it has been established that COPD is a complex, multicomponent disease (2) (Figure 1), and that FEV1 fails to adequately express this complexity (3). For instance, some associated extrapulmonary manifestations of COPD, such as weight loss and skeletal muscle dysfunction, influence the course of the disease independently of FEV1 (4), and, importantly, their treatment (by rehabilitation in this particular example) contributes to improve the prognosis and well-being of these patients without altering lung function (5). Thus, a better understanding of the complexity of COPD is important to improve current clinical practice and advance biomedical research and drug development.
A first important step in the direction of assessing this complexity was the development and validation of several multidimensional assessment indices, such as the BODE index (body mass index, FEV1, dyspnea, and exercise capacity) (6), the ADO index (age, dyspnea, FEV1) (7), and the DOSE index (dyspnea, FEV1, smoking status, and exacerbation frequency) (8). All of them, however, are based on clinical and functional variables only, whereas it is well established that COPD is also a complex disease at the molecular and genetic levels (9). For instance, a key pathogenic component of COPD is an enhanced inflammatory response to inhaled particles and gases (mostly tobacco smoking) (1). Interestingly, inflammation persists years after cessation of the initial stimuli (quitting smoking) (10–12). Likewise, it is well known that only a percentage of smokers develop COPD, suggesting a genetically determined susceptibility for the disease (13). However, information at the genotype, molecular, cellular, and phenotype levels (14–16) have not been incorporated into our current understanding and management of COPD.
Over the past few years there have been significant advances in the basic understanding of complex biological networks (17–19) coupled with important technological developments in biology (20) and computing sciences (21). These advances allow us to address the complexity of human diseases in general, and that of COPD in particular, in a comprehensive and dynamic way (systems biology) (22–27). These advances will help catalyze the transition from the current reactive practice of medicine to a Predictive, Personalized, Preventive, and Participatory (P4) medicine centered in preserving health and not solely in combating disease (21).
The aims of this review are to present and discuss some basic concepts (phenotypes, genotypes, biomarkers, scale-free networks) that are essential to progress toward an integrated, multilevel view of COPD based on the novel scientific strategy of systems biology and its potential clinical derivative, P4 medicine.
The terms phenotype and genotype are often used in the medical literature with different meanings. Broadly speaking, a phenotype corresponds to any observed quality of an organism, whereas its genotype refers to the inherited genetic instructions it carries, whether they are expressed or not (28). The phenotype is often composed of traits or characteristics, some of which are controlled entirely by the individual's genes (genotype), whereas others, which are still controlled by genes, are significantly affected by environmental factors (28). Hence, the phenotype is the end result of the random interaction between the genotype and the environment (28).
This simple phenotype definition is not necessarily useful in disease states such as COPD. For instance, unless having brown eyes (an easily detectable phenotype) increases the predisposition to, worsens the natural history of, and/or influences the treatment of COPD (all of them unlikely), it does not provide information that is useful in any way to inform and guide clinical practice. In other words, phenotypes (or phenotypic traits) are not always necessarily relevant clinically. To address these limitations, the term “clinical phenotype” has been recently proposed (29). According to this proposal, a clinical phenotype is “a single or combination of disease attributes that describe differences between individuals with COPD as they relate to clinically meaningful outcomes (symptoms, exacerbations, response to therapy, and rate of disease progression or death)” (29). This implies that clinical phenotypes should: (1) have predictive value, (2) be prospectively validated for each of the outcomes to which they may relate, and (3) be able to classify patients into distinct subgroups that provide prognostic information and allow physicians to better determine the most appropriate therapy to improve clinically meaningful outcomes (29).
A quick review of the literature reveals that most of the phenotypes proposed to date in COPD (16, 30–32) do not fulfill this definition because they have been identified from cross-sectional analysis and have not been validated prospectively against clinically relevant outcomes (33). Interestingly, however, we do have some examples that do fulfill this definition. The oldest one probably corresponds to patients with COPD with chronic respiratory failure, in whom a specific type of treatment (domiciliary oxygen therapy) has been shown to improve prognosis below a given arterial Po2 value (34, 35) but not above it (36, 37). A more recent example is lung volume reduction surgery, which has also been shown to improve prognosis in a well-characterized COPD phenotype (upper lobe emphysema and poor exercise capacity after rehabilitation) (38). Finally, a very recent example of the use of a specific phenotype in COPD is the development of a new antiinflammatory drug (roflumilast) for patients with COPD who had a history of chronic bronchitis (39, 40). In this context, it is worth noting that several national (41, 42) and international initiatives (43) are currently under way to identify potential phenotypes of clinical relevance in COPD.
Finally, three specific issues in relation to phenotypes in COPD are worth discussing: (1) depending on the context, certain attributes (e.g., dyspnea, or exacerbations) could be viewed as outcomes or phenotypes (29); (2) disease severity is not a phenotype. It may be the downstream consequence of a particularly aggressive form of the disease, which is the real phenotype. The precise clinical, functional, imaging, and molecular characterization of this phenotype would be of great clinical value because it would allow its early identification, thus treatment, with the hope that this would in turn improve clinical outcomes (33); and, (3) severity of disease is often mistaken as an indication of disease activity. Severity is likely the consequence of sustained pathobiological activity, but it is entirely conceivable that any disease entity (including COPD) may have different levels of activity at any given stage of severity. Actually, in many other chronic inflammatory conditions, such as rheumatoid arthritis, there is a clear distinction between severity and activity of the disease. How to specifically address this conundrum in COPD is still unresolved (44). We would like to propose, however, that the measurement of COPD severity needs to relate to the extent of loss of function in the lungs (or other organs affected by the disease) that eventually impact on the final prognosis of the patient (e.g., degree of airflow limitation, lung hyperinflation, and/or pulmonary gas exchange impairment). In contrast, the degree of disease activity will have to be necessarily related to the identification of biomarkers that inform on the level of activation of one or more cellular and molecular networks that drive disease progression (for specific examples, see below).
According to a recent European Respiratory Society/American Thoracic Society position paper, a biomarker is “any molecule or material (e.g., cells, tissue) that reflects the disease process” (45). Some aspects of this definition deserve comment. First, it excludes functional or imaging measures and focuses on biological determinations. Second, the term “disease process” is not defined and may range from biological mechanisms to clinically relevant outcomes. Other medical disciplines, however, have been more specific and demanding in their requirements to qualify a clinical biomarker as useful (46). In the cardiovascular arena (and likely in COPD too), to be useful in clinical practice any biomarker has to: (1) be technically measurable with specificity and reliability, (2) convey novel information that cannot be obtained by other (simpler) methods, and (3) be useful for the effective management of the patients (Table 1) (46).
Can the clinician measure the biomarker?
|Accurate and reproducible analytical method(s)|
|Preanalytical issues (including stability) evaluated and manageable|
|Assay is accessible|
|Available assays provide high-throughput and rapid turnaround|
|Does the biomarker add new information?|
|Strong and consistent association between the biomarker and the outcome or disease of interest in multiple studies|
|Information adds to or improves on existing tests|
|Decision limits are validated in more than one study|
|Evaluation includes data from community-based populations|
|Will the biomarker help the clinician to manage patients?|
|Superior performance to existing diagnostic texts, or|
|Evidence that associated risk is modifiable with specific therapy, or|
|Evidence that biomarker-guided triage or monitoring enhances care|
| Consider each of multiple potential uses of the biomarker (diagnosis, risk stratification; selection of therapy; monitoring disease progression, disease activity, or response to therapy)|
Over the past few years there has been a growing interest in the field of biomarkers in COPD. C-reactive protein (CRP) was probably the first candidate to be studied in these patients. Pinto-Plata and colleagues showed that CRP serum levels were raised in patients with COPD, independently of cigarette smoking (47), whereas Dahl and colleagues demonstrated that raised CRP levels were an independent predictor of future outcomes, including hospitalization and death (48). Whether or not these levels respond to inhaled antiinflammatory therapy is controversial because, although initial studies suggested that this was the case (47, 49), results were not confirmed in later investigations in larger cohorts (50). Other candidate biomarkers studied in COPD include circulating levels of Clara cell secretory protein-16 (CC-16) (51), surfactant protein (SP)-D (52), and serum amyloid A (SAA) (53). The serum levels of CC-16, a marker of Clara cell toxicity, appear to be reduced in patients with COPD (51). In contrast, the serum levels of SP-D, a lung-derived protein associated with the presence of pulmonary inflammation, are increased in smokers (with or without COPD) and are potentially useful to identify patients at risk for exacerbations of COPD (52); interestingly, SP-D levels appear to respond to inhaled steroid therapy (50). Finally, Bozinovski and colleagues recently reported that, in contrast to IL-6, CRP, or procalcitonin levels, SAA can be helpful for the diagnosis of the episodes of exacerbation (53).
These studies focused on one or a few potential biomarker candidates. The use of high-throughput technology will improve our capacity to screen, identify, and eventually validate many different and novel biomarkers simultaneously. In the field of COPD, Pinto-Plata and colleagues used a protein microarray platform and identified a panel of 24 serum markers of inflammation, tissue destruction, and repair that were significantly related to lung function, exercise capacity, the BODE index, and reported exacerbation frequency (54). Unfortunately, their relationship with prospectively determined relevant clinical outcomes was not explored.
Finally, micro ribonucleic acids (micro-RNAs; miRNAs) are small (22-nucleotide), single-stranded, noncoding RNAs that regulate gene expression by inducing mRNA degradation or by inhibiting translation (55). Their potential usefulness as biomarkers has already been established in patients with cancer (56, 57) but has not been investigated yet in COPD.
It is important to realize that different biomarkers may serve different purposes (33), ranging from the early detection of subclinical disease, the diagnosis of acute events (e.g., exacerbations of COPD), risk stratification, selection of the most appropriate therapy for a specific clinical phenotype, and the monitoring of disease progression, disease activity, and/or response to therapy (46). Therefore, it is conceivable (and desirable) that in the near future clinicians will be able to use validated biomarkers to better characterize different aspects of the pulmonary and extrapulmonary dimensions of the disease that will eventually help them make the most appropriate therapeutic decisions for individual patients.
Surprisingly, many diverse systems, such as the cell, the society, the economy, electrical networks, terrorist organizations, and/or the Internet, share a similar internal architecture: the so-called scale-free networks (19, 58). This is a particular type of network characterized by having the majority of its nodes (i.e, the core elements of the network, whether they are genes, proteins, metabolites, people, power plants, terrorists, or computers in the examples given above) connected to other nodes of the network by a relatively small number of links, whereas a few nodes (hubs) present an extremely high number of links (Figure 2). This is very different from the Poisson network, where the majority of nodes have a similar (and relatively small) number of links (Figure 2).
Scale-free networks share two important functional characteristics. First, they are differentially sensitive to damage. This means that if a small, peripheral node stops functioning, the network is very likely to continue working without problem. By contrast, if a hub is damaged, the functionality of the entire network is likely to be jeopardized. As an example, consider the air traffic network shown in Figure 2. If a hub airport (e.g., JFK in New York) is closed (remember last winter's snow storms) most of the United States and many international flights will be affected, whereas if a small, peripheral airport is closed, most flights will continue without problems. This differential sensitivity to damage depending on the centrality of the node is also seen in biological networks (59). To illustrate this point, Figure 3 presents a murine gene network (60). Red and blue dots represent nodes (genes) that were significantly up- or down-regulated, respectively, under the particular experimental conditions of this study (ovalbumin sensitization) (60). Several characteristics of this network of experimentally induced asthma are worth discussing: (1) it is a scale-free network, with clearly identifiable hubs; (2) only a small percentage of the genes change their expression (color code) after the experimental intervention; (3) it is only by having a map of the network that the topology (i.e., location within the network) of a given gene can be determined, and more importantly, whether a specific gene is characterized as a hub or not; and (4) genes (nodes) that change in response to the experimental conditions (colored nodes) tend to have relatively low degrees of connectivity, probably indicating that hubs are key nodes for the functioning and survival of the network (59).
Second, complex systems are characterized by the presence of emerging properties, which do not correspond to any given individual component (node) of the network but to the existence of the network itself. An easy example of a complex system with emerging properties is an airplane: none of its elements (wings, engines, crew, etc.) can fly by itself. Flying is an emerging property of the system. The concept of emerging properties is extremely important in human biology, because health and disease (life itself, after all) are also emerging properties of a complex system: the human body.
The application of network science to human biology has begun to produce some exciting and intriguing results. On the one hand, the description of the “diseasome” (Figure 5) from the combination of the Human Disease Network and the Disease Gene Network (see Appendix for further explanation) is a paradigmatic example (22) because it offers, for the first time, a single platform to explore all known phenotype and disease gene associations (22). On the other hand, because human biology changes with time (for instance, during the transition from health to disease or during the response of a given disease to a specific form of therapy), a dynamic, continuous, graphical representation is needed. The so-called Phenotypic Disease Network (PDN) recently published by Hidalgo and colleagues (see Appendix for further explanation) is a first attempt in this direction (24), as it showed that: (1) patients develop diseases that are closely located in the network to those they already have, (2) the progression of disease along the links of the network is different for patients of different sex and race, and (3) patients diagnosed with diseases that are highly connected in the PDN tend to die sooner than those affected by less connected diseases (24). All in all, these findings highlight the potential of network science to better understand the origin and evolution of human diseases (24).
Human biology, both in health and disease, is extremely complex (17). Because of this complexity and the lack of adequate tools to handle complex data, biomedical research has traditionally followed a reductionist strategy, moving its focus progressively from the entire human body (anatomy), to the organ (physiology), the isolated cell (cell biology) and, more recently, molecules and genes (molecular biology) (61). This research strategy has been extremely successful and probably reached its summit with the cloning of the entire human genome (62). Yet, it is clear that a detailed inventory of genes, proteins, and metabolites is not sufficient to understand life's complexity, an emergent property of the system (17).
Systems biology is a novel scientific discipline that seeks to backtrack the reductionist path followed historically by biomedical research to: (1) integrate data within and between the different levels of biological complexity (genes, molecules, cells, tissues, organs, entire body, and even society and environment); and (2) model the complexity of the system and its emerging properties. To achieve these goals, systems biology follows an iterative research strategy that generally involves the following steps (25): (1) use high-throughput platforms to collect global genome, transcriptome, proteome, and metabolome information in patients who have been precisely characterized phenotypically (including, if possible, clinical, functional, and imaging data). Whenever these data are not available, they can be complemented with information existing in publically available databases through knowledge management platforms (66); (2) use biocomputing algorithms to generate multiscale (from the molecular to the organ level) predictive mathematical models from the data collected in patients; (3) use these models to formulate novel working hypotheses on the mechanisms and pathways involved in the disease of interest; (4) test these novel hypothesis through perturbation experiments, that can be done in silico (model simulation), in vitro (cell culture), or in vivo (animal models or, in the case of selected perturbations, such as exercise, in healthy subjects or patients); (5) compare the experimental responses observed with those predicted by the initial mathematical models, which are then refined to account better for the results; and, (6) perform novel perturbation experiments designed and tested computationally and experimentally to arbitrate between competing hypotheses (25). This process is iterated until the derived model predicts with reasonable accuracy the observed experimental findings, at which point the model would have allowed a better understanding of the disease of interest and the identification of novel biomarkers. This may in turn translate into the development of novel diagnostic and therapeutic interventions that can be tested and validated in prospective clinical trials (27).
The classification of human diseases is currently based on observed correlations between clinical syndromes and pathological and laboratory findings (23). This taxonomic strategy has two significant limitations: (1) it lacks sensitivity to identify preclinical disease, and (2) it lacks specificity to define disease unequivocally because diseases often have different clinical presentations (i.e., phenotypes) (23). Loscalzo and colleagues have pioneered the use of network science and complex systems to better define and understand what phenotypes really are and, eventually, to move toward a novel classification of human diseases (23). Given that any given phenotype/disease reflects the dynamic consequences of the random interaction between the genotype and the environment, Loscalzo and colleagues (23) suggest that it is important to consider two different types of disease-modifying genes: (1) those genetic mutations and polymorphisms primarily located in the disease-specific functional modules that characterize that particular phenotype/disease (22); and, (2) those genes (or networks of genes) whose actions reflect generic responses to organism stress, evoked either by the principal mutation and/or environmental exposures (23). These generic responses constitute the so-called intermediate pathophenotypes and include (Figure 4): inflammation, thrombosis and hemorrhage, fibrosis, the immune response, proliferation, and apoptosis/necrosis (23). Given that these intermediate pathophenotypes constitute the entire armamentarium that we humans have to respond to injury, it should not be at all surprising that basically all diseases known to date include a varying proportion of one or more of these responses.
A systems-based network analysis that considers all these elements provides a mechanistic basis for defining phenotypic differences among individuals with the same disease through consideration of unique genetic and environmental factors that govern intermediate phenotypes contributing to disease expression (23). Specifically, such analysis has the potential to: (1) identify the determinants (nodes) or combinations of determinants that influence network behavior (emergent properties) and disease expression (phenotype); (2) provide insight into disease mechanism and potential therapeutic targets, because regulatory determinants may not always be obvious from reductionist principles; and, (3) quantify the relationships within the network genome, environmental exposures, and environmental effects that define the specific pathophenotype(s) involved in a given clinical condition (23). The application of these principles to specific diseases, including COPD, is in its infancy, but concepts are internally consistent and early results encouraging (23).
Each of the intermediate pathophenotypes discussed above is in itself a complex network. To exemplify this concept, we discuss the case of the inflammatory response, believed to play a key role in the pathogenesis of COPD (1, 11). Calvano and colleagues were the first to apply a network-based analysis in the study of the inflammatory response in humans (67). They analyzed changes in blood leukocyte gene expression patterns in healthy human volunteers before and after the administration of bacterial endotoxin. They identified significant functional module perturbation following the challenge, particularly a transient dysregulation of leukocyte bioenergetics and modulation of the translational machinery (67). A few years later, McDunn and colleagues explored if circulating leukocyte transcriptional profiles (riboleukograms) can be used to monitor the host response to and the recovery from acute infection. They found that: (1) an infection-specific transcriptional program (i.e, a specific riboleukogram) precedes the clinical diagnosis of pneumonia in critically ill patients, and (2) disease trajectories derived from the riboleukograms can be used to quantitatively track the clinical course of the disease and to identify a state of immune recovery (68). Polpitiya and colleagues extended these observations and developed the concept of immune cartography (69). Using mouse models of abdominal and pulmonary sepsis, they showed that riboleukograms differentiated infected and sterile animals, and were specific for gram-negative or gram-positive infections (69). In addition, they observed that the endotoxin response can be mapped at the level of gene expression, and it was possible to follow septic patients and quantitatively determine their immune recovery (69). The potential application of immune cartography to the inflammatory response that characterizes COPD is being investigated currently.
As stated from the beginning of this review, COPD is a complex disease with pulmonary and extrapulmonary (comorbidities) manifestations (2). A systems-based approach can thus facilitate the understanding of this complexity. The work by Lee and colleagues (70) illustrates this possibility. Following similar principles to those discussed above for the diseasome (22), these investigators explored the implications of the Human Metabolic Network (HMN) topology for disease comorbidity (70) (see Appendix for further explanation) and showed that: (1) comorbidity was higher in connected diseases than in those that have no metabolic link between them, and (2) the more connected a disease is, the higher is its prevalence and associated mortality rate (70). Furthermore, Park and colleagues combined information on cellular interactions, disease–gene associations, and population-level disease patterns extracted from Medicare data to confirm statistically significant correlations between the underlying structure of cellular networks and disease comorbidity patterns (71). Taken together, these results highlight that a network topology-based approach helps to uncover potential mechanisms that contribute to the shared pathophysiology of seemingly distinct diseases. Until now, these diseases were categorized as comorbidities simply because the shared mechanistics linking them were unknown. As our knowledge of the underlying cellular and molecular networks improves, it is likely that in the future some comorbidities of COPD will in fact be an integral part of a specific phenotype because they share abnormalities in different modules of the disease. Such modules may, of course, change with disease severity or, it is hoped, in response to therapy.
Standing on the shoulders of the theoretical (scale-free networks), technological (high-throughput technology, biocomputing), and analytical improvements (systems biology), it is conceivable that over the next few years the practice of medicine will evolve from its traditional reactive mode (i.e., doctors diagnose and treat established diseases) to an anticipatory mode (i.e., centered in preserving health) (21). The term “P4 medicine” (Personalized, Predictive, Preventive, and Participatory) has been coined to describe this new form of medicine (21). It is anticipated that it will be personalized because it will be based on the personal genome data; it will be predictive because the analysis of these personal data will allow accurate risk predictions for several diseases; it will be preventive because, from that prediction, preventive measures, either in the form of regular screening and/or specific interventions, could be implemented; and it will be participatory because the participation of the individual is essential for all of the above, for instance, when lifestyle changes are advised or when compliance with chronic treatments are needed (21).
This review has addressed different issues related to the complexity of COPD (phenotypes, biomarkers) and has discussed some of the scientific changes that are currently occurring (scale-free networks, systems biology, the diseasome, intermediate pathophenotypes, riboleukogram, and immune cartography). All of these are likely to significantly affect our understanding and management of the disease (P4 medicine). We are not there yet, but it may not take long to witness what will likely be a true revolution for scientists, doctors, nurses, patients, individuals, and society. No doubt the findings emanating from this endeavor will have many and very significant ethical, legal, social, and economic implications. And, in closing, for the skeptics, just remember that only a few years ago none of us had a mobile telephone, e-mail, or access to the Internet.
Biomarker: any molecule or material (e.g., cells, tissue) that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
Complex system: system composed of interconnected parts that as a whole exhibit one or more properties not obvious from the properties of the individual parts (emerging properties).
COPD phenotype: a single or combination of disease attributes (traits) that describe differences between individuals with COPD as they relate to clinically meaningful outcomes (symptoms, exacerbations, response to therapy, and rate of disease progression or death).
Disease severity: extent of the functional loss of the target organ.
Disease activity: level of activation of the biological processes that drive disease progression.
Diseasome: Combined set of all known disorder/disease gene associations that results from linking the Human Disease Network (HDN) and the Disease Gene Network (DGN).
Disease Gene Network (DGN): a scale-free network whose nodes (genes) are connected if they are involved in the same disease.
Emerging properties: Functional characteristics of a complex system that cannot be explained by any given individual component (node) of the network but by the existence of the network itself.
Genotype: inherited genetic instructions of an organism, cell, or individual, whether they are expressed or not.
Hub: node with a large number of links.
Human Disease Network (HDN): a scale-free network whose nodes (diseases) are connected if there is at least one gene that has been implicated in both.
Human Metabolic Network (HMN): a scale-free network whose nodes (diseases) are connected if mutated enzymes associated with them catalyze adjacent metabolic reactions.
Immune cartography: disease trajectories derived from riboleukograms to quantitatively track the course of immune evolution.
Link: connector between nodes. Also often referred to as “edges.”
Network: collection of vertices (or “nodes”) and edges (or links) that connect pairs of vertices.
Node: core element of a network. Also often referred to as “vertices.”
Phenotype: any observable physical or biochemical quality of an organism, resulting from the random interaction between the genotype and the environment.
Phenotypic Disease Network (PDN): A network approach to the dynamic (this is, changing) nature of clinical presentation of diseases.
P4 medicine: a proposed new form of medical practice that combines Personalized, Predictive, Preventive, and Participatory elements.
Riboleukogram: transcriptional profiles of circulating leukocytes.
Scale-free networks: a particular type of network characterized by the presence of few nodes (elements of the network) that have many links (hubs), whereas most nodes have few links.
System: set of interdependent components that form an integrated whole.
Systems biology: scientific discipline that integrates and models high-throughput data within and between the different levels of biological complexity (genes, molecules, cells, tissues, organs, entire body, and even society and environment).
Trait: a distinguishing feature of a given disease.
This paper is in itself a living proof of the importance of networks. Its first draft was written in Fornalutx (Balearic Islands, Spain) surrounded by a network of beautiful orange, lemon, and olive trees (http://www.ajfornalutx.net) that provided the necessary environmental network conditions for reading, thinking and writing (biological network). Then, it was discussed overseas by using the facilities of a key technological network: the Internet. Finally, the input of a social network of many colleagues and friends helped it reach its final form. Without these four networks, this paper would have been different or may not even have existed.
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