1.6. Systems Biology

The immune system is made up of several components that interact with each other through signals, and the efficiency of its response to a challenge directly depends on the integrity of its components and the quality of communication between them. As discussed, immunosenescence represents the complex and continuous remodelling of this system throughout life, and there may be concomitant changes at different levels, both in its components and in its interactions. Such complexity makes traditional reductionist methods, characterized by the study of systems through their isolated components, not the best methodology to deal with the problem in question. The holistic perspective of systems biology, in turn, becomes more appropriate in this scenario as it can deal with information from all its components simultaneously, enabling a better understanding of the behaviour and global characteristics of this phenomenon (KITANO, 2002).

‌ Systems biology methods involve computational analysis that integrates data generated by various types of technologies and different levels of biological complexity. With the advent of high-throughput technologies, it became possible to assess disturbances at practically all molecular levels of a biological system, such as the genome, epigenome, transcriptome, proteome and metabolome.

‌ In transcriptomics studies, analyzes performed to identify groups of genes that behave similarly to each other have the potential to unravel several molecular mechanisms involved in the regulation of diseases, responses to vaccines and cancer. These analyzes are based on the premise that genes that are disturbed together or with expression profiles similar to each other tend to be part of the same transcriptional program, that is, they are related to the same molecular function. Traditionally, such biological processes are identified through expression difference analyses, where groups of genes that suffered disturbances in their expression level between different phenotypic classes are identified, or through co-expression analyses, in which groups of genes are identified with similar expression profiles, through the clustering of genes highly correlated with each other and then enriched for metabolic pathways described in the literature (ZHANG; HORVATH, 2005).

‌ Many studies have used transcriptomics to identify genes and biological processes involved with ageing, as evidenced by GenAge (DE MAGALHÃES; TOUSSAINT, 2004), a database that provides a list of genes that have been identified as related to processes involved in ageing in both humans and animal models. However, even with the gigantic amount of data generated, these studies used cohorts with few samples or with comparisons conducted in arbitrary slices of age groups. Such experimental designs can lead to biased conclusions or limited to the comparisons conducted in each article. Furthermore, temporal discretization can mask concomitant and progressive changes in the biological processes that cause immunosenescence. The only exception found in the study carried out by (IRIZAR et al., 2015), as they study aspects of the immune system continuously. However, they are restricted to studies of non-protein-coding transcripts in dendritic cells.

‌ Therefore, this work proposed to use systems biology paradigms to study the remodelling of the immune system through its transcriptional profile in blood, to understand how the behaviour of its components change throughout life and how such dynamics may be related to immunosenescence. To this end, methodologies will be developed to identify possible age-related markers and biological processes in a time series context, to identify and analyze their behaviour and how they may be related to immunosenescence.