Multiscale Solutions to Quantitative Systems Biology Models

Systems biology implements a variety of statistical, computational and mathematical techniques to understand how networks of biological systems work together to achieve a function (Westerhoff and Palsson, 2004; Wolkenhauer, 2014). Systems biology is a multi-scale field, as it has no fixed scale in the context of a biological response or cascade, where an ensemble of proteins, cofactors and small molecules concertedly act to achieve function. This is the case of fundamental pathophysiological networks, such as epidemiological responses with host and pathogens (Hillmer, 2015). Understanding the network of interactions that mediate these systems is of the utmost importance for deciphering the mechanisms associated with multifactorial diseases, as well as to address fundamental biological questions. This knowledge can be used for translational research and application in biomedicine (McGillivray et al., 2018). The multi-scale nature of systems biology calls for a multifaced description to bridge the system scale at the cellular level to the molecular scale of individual macromolecules. Among the important biological cascades responsible for severe diseases, we focus here on the complement system, which is an effector arm of the immune system that eliminates pathogens, helps in maintaining host homeostasis, and forms a bridge between innate and adaptive immunity (Bennett et al., 2017; Reis et al., 2019). Complement is composed of three pathways known as alternative, classical and lectin that work in concert to achieve its function (Schatz-Jakobsen et al., 2016a). The complex network of proteins and other macromolecular entities composing the complement system represents an ideal case to build a systems biology workflow predicting the system's response in immunity against invading pathogens, and how under complement deficiencies this same system mediates different pathologies. Here, we report on the development of systems biology predictive models, which describe the intricate biochemical networks and the crosstalk among other elements of the immune system. We also show how the integration of multiscale modeling techniques can help for improving the predictive model, while also providing mechanistic information at the molecular level. Complement dysfunction is associated with several diseases. Among others, the complement components have been associated with neurodegenerative disorders including Alzheimer and Parkinson diseases; as well as multiple sclerosis (Mastellos et al., 2019). Moreover, mutations of complement proteins have been linked to the etiology of renal diseases (De Vriese et al., 2015; Ricklin et al., 2016), while individuals with complement deficiencies develop severe infections, such as meningitis, bacteremia and pneumonia caused by microorganisms, such as Streptococcus pneumoniae, Neisseria meningitidis, and Staphylococcus aureus (Skattum et al., 2011). Clearly, while a proper activation of the complement system is associated with a wide spectrum of beneficial effects, dysfunctional states are associated with severe consequences. Considering that the function of the complement system is regulated by a network of multiple components, whose concerted activity underlies a variety of diseases, accurate models of the interaction network would greatly help therapeutic strategies (Ricklin et al., 2018).