 More recently, attempts to integrate molecular biology with systems analysis challenge the apparent conflict between reductionist and holist strategies (Green et al. 2017; Mazzocchi 2012). After all, attempts to explain complex immunological phenomena in terms of isolated parts and interactions without taking into consideration the entirety of structural and functional complexity that these phenomena exhibit have been ineffective. For example, vaccine development studies, when limited to investigation of the structural correlates of antigen-antibody interaction without deeper understanding of the dynamic features of a host-pathogen encounter, often prove unsuccessful (Van Regenmortel 2002). So, instead of assuming that non-reductionist explanations must ignore lower level molecular details, system-oriented immunologists attempt to understand the multilevel complexity of the immune reactions they study (Rivas et al. 2017). Dynamical systems theory and a variety of network-based approaches are used in systems biology to integrate distinct levels of analysis and elucidate the complexity of the biological phenomena in question (Green et al. 2017). Accordingly, a pluralistic approach that attends to distinct dimensions and frames of reference, i.e., both molecular and higher orders of analysis, has directed recent efforts to integrate the complexity immunity (Löwy 2008: 172; see entry on  philosophy of systems and synthetic biology).  However, the program of melding the reductionist and holistic methods as complementary to each other has been slowed by both technical and conceptual hurdles (Fang and Casadevall 2011). How to model the system as a system requires the collection of information about the totality of interactions to explain causal mechanisms with multiple inputs and stochastic outcomes. No approach has achieved dominance and, consequently, the question of how massive data might be incorporated into a systems-wide comprehensive model comprises a key challenge of contemporary immunology.