Network modeling helps to tackle the complexity of drug–disease systems

Abstract

From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug–disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of “disease module”, as well as the introduction of more articulated models, like multi‐scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug–disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models

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“…However, while on one side, multi-omics approaches may appear as innovative strategies useful to interpret the mechanistic details of a disease, on the other, they make very difficult the analysis of related data. Accordingly multi-omics investigations typically depend on both creating complex interactome networks and developing precise models for disease prediction, diagnosis, and prognosis, by using graphs theory and machine learning approaches, respectively ( Recanatini and Menestrina, 2023 ) ( Figure 2 ). Both analyses constitute a very serious challenge ( Recanatini and Menestrina, 2023 ), because firstly the interactions cannot be causal, since most links must be estimated through correlations or co-expressions; second, it is necessary to explore interactions between thousands or millions of entities (e.g., genes, epigenetic factors, proteins, metabolites, etc.)…”