## 3 Host-Pathogen Interactions
### Modeling the Immune System  
Mechanistic models are commonly used to predict the outcomes of individual immune responses. Stochastic and mechanistic models can identify cell-to-cell differences, which can determine the response of individual cells [63]. However, other studies focused on dynamic interactions to better explain how invertebrate organisms can optimize an immune strategy to overcome infections. Through the integration of all high-throughput multiomics data, it is possible to construct predictive models of the networks and dynamic interactions between the biological components of host-pathogen systems, allowing for a better understanding of complex biological systems. Systems Biology can define PPIs to unravel interactions amongst several bacterial virulence factors and host proteins involved in the inflammatory process, the complement system, or the immune response [64]. Doubtlessly, both biological systems (host and pathogen) are highly complex and not completely understood, limiting the possibilities of the input data on our models. This complexity causes some important questions to remain unanswered, such as why the same microbe can be commensal or pathogen, how two people from the same community could respond differently to the same microbial threat, and the key immune components that dictate the outcome of the infectious process [65]. The complexity of biological systems requires multidisciplinary teams to tackle vast amounts of data and make sense of them. On the other hand, one the greatest limitations to implementing dynamic projects in immunology and infectious disease is that a variety of high-level skill sets are needed. Furthermore, experiments require many complex techniques in order to understand the mechanism of immune response [66]. Simpler models allow us to include novel processes as new subsystems and enable increasing complexity. For example, [PERSON] et al. modelled the immune response after stimulation with bacterial lipopolysaccharidesaccharides to better understand the inflammatory process [67] (Table A1). In this section, we focus on various immune system-related bacteria, mainly based on proteomics data and databases. Certainly, \"DNA is the blueprint for life, however proteins are the bricks\" [4]. _Streptococcus pyogenes_ has been used as a model of system biology in different studies, because its independent but interconnected virulence mechanisms promote different aspects of the colonization, immune evasion, and spreading processes [68; 69]. Moreover, its role as an immunomodulator of the humoral immunity is well-known [70]. [PERSON] et al. have recently characterized the human plasma-_S. pyogenes_ bacterial surface PPI network by using an adsorption plasma approach in combination with MS [71]. Later, the authors developed a dynamic model to study the stoichiometric relationship between the bacterial surface and its adhered host proteins (Figure 3) [11] (Table A1). In addition, shotgun MS is a highlight the proteogenomic or genomics field, and the strategies developed by [PERSON] et al. work with generic data for integrating genome and proteome data [72]. After constructing a spectral library for _S. pyogenes_, quantification is performed using sequential window acquisition of all theoretical mass spectra (SWATH)-like data independent acquisition (DIA)-MS, to increase insights into host-pathogen interactions. The SWATH-MS strategy was also used by [PERSON] et al. to quantify the proteins involved in pathological conditions, like sepsis, to understand how different pathophysiological processes can twist the PPI network and composition of the plasma [73]. Salmonella enterica has also been used as a pathogen model for PPI networks between the host and pathogen because of its ability to survive into macrophages [74]. The authors first designed two different mathematical models, assuming a Poisson distribution, to describe the dynamics of the different subpopulation systems.