Major depressive disorder (MDD) is one of the most common debilitating mood disorders worldwide and is becoming increasingly prevalent in children and adolescents. Approximately 15% of the population is estimated to be affected with this disorder at least once in their lifetime [1–4]. MDD has been projected to become the second leading cause of disability worldwide by 2020 according to the World Health Organization (WHO).
MDD is a multifactorial disorder known to be influenced by both genetic and environmental factors. The heritability of MDD is estimated to be 0.37 (95% confidence interval (CI) 0.31-042) based on data from twin studies, and a genetic contribution has also been observed in family studies of individuals with MDD that imply that the probability of suffering the disorder is approximately three times higher if a first-degree family member is affected [5, 6].
Childhood maltreatment and stressful life events (SLEs) have been established as critical environmental factors that profoundly influence the onset of MDD [7, 8]; however, the question of why some individuals develop MDD when exposed to stressful events and some do not has been difficult to answer. An initial attempt to answer this question was made in 2003 by Caspi et al., who elegantly demonstrated that the risk of developing MDD is higher when an individual has experienced various stressor events during childhood. In this work, they evaluated the possible association of a genetic variant of the serotonin transporter (5-HTT) with maltreatment . This gene x environment interaction approach in which 5-HTT gene variations are proposed to influence the likelihood of stressful life events resulting in MDD has not been fully supported by recent studies, suggesting that other pathways, such as those involved in stress responses, might also be involved in the development of MDD [10, 11]. Moreover, one of the most common types of antidepressant used is the Selective Reuptake Inhibitors (SSRIs). SSRIs are designed to block the 5-HTT. Therefore, 5-HT homeostasis in the brain is related to the disease, which makes the 5-HT pathway a strong candidate for studies of MDD .
More recently, it has been demonstrated that neurotrophin signaling directly interacts with the serotonin pathway, therefore making it another good candidate for MDD studies. Brain-derived neurotrophic factor (BDNF) is a growth factor involved in regulating the survival and maturation of serotonin (5-HT) neurons during development and in regulating synaptic plasticity throughout life. In the dorsal raphe nuclei (DRN) cell, BDNF activates genes in the serotonin pathway, such as those encoding the serotonin transporter (5-HTT) and the enzymes required for 5-HT synthesis. The 5-HT released from the raphe nucleus cells activate serotonin post-synaptic receptors and autoreceptors such as 5-HT1A. Autoreceptor stimulation by 5-HT leads to a blockade of BDNF regulatory genes and, therefore, a decrease in BDNF synthesis [13–15].
The stress response activates a series of processes in the hypothalamus-pituitary-adrenal (HPA) axis culminating in the release of cortisol by the adrenal glands . The glucocorticoid receptor is expressed in serotoninergic neurons [17–19], which is indicative of glucocorticoid-exerted control in these neurons. On the other hand, both stress and glucocorticoids are known to decrease brain levels of BDNF in rodents and post-mortem human tissue . Taken together, this information suggests the existence of a cycle involving these three pathways (the 5-HT regulation pathway, the stress response pathway and the BDNF regulation pathway), which are essential to enable the serotonin system to respond to various stimuli throughout life.
We propose a Boolean approximation to analyze the HPA axis-mediated (stress response) interactions between the 5-HT regulation pathway and the neurotrophin signaling pathway. Synchronous Boolean networks (SBNs) have proven to be a valuable model for understanding how a large number of components (nodes) in a network interact with each other. The logic of SBNs is based on the use of binary values, 0 as “inactive” and 1 as “active”, to model a complex system [21, 22]. SBNs are networks that update their nodes synchronously in a discrete time step . Since we are analyzing a large complex network (41 nodes), we chose synchronous update since this type of modeling changes expression, activation or inactivation of different nodes simultaneously in a discrete time , feature observed in various biological systems [25, 26]. Moreover, as we are dealing with a large network, this updating approximation is computationally feasible.
Through this type of network modeling, it is possible to understand and annotate complex pathways. SBN approximations have been used to model the orientation pathways by which neural crest stem cells give rise to two different types of sensorial cells , and it has also been used to model biofilm formation in the E. coli strain K-12 . Furthermore, this approach has also been applied to search for new candidate genes in schizophrenia  and as a modeling technique in cancer studies .
The aim of this work was to use a Boolean approximation to analyze an integrated network involving the 5-HT neurotransmitter pathway, neurotrophin signaling and the HPA cortisol synthesis pathway in the presence and absence of stress and serotonin selective reuptake inhibitors (SSRIs). We also evaluated network stability and the effects that knocked-out genes had on the network to search for probable candidate genes involved in MDD.