The birth of complexity research derives from the logical progression of advancement in the scientific field afforded by reductionist theory. We present in silico models of two complex physiological processes, wound healing and coagulation/fibrinolysis based on two common tools in the study of complex physiology: ordinary differential equations (ODE) and Agent Based Modeling (ABM). The strengths of these two approaches are well-suited in the analysis of clinical paradigms such as wound healing and coagulation. The complex interactions that characterize acute wound healing have stymied the development of effective therapeutic modalities. The use of computational models holds the promise to improve our basic approach to understanding the process. We have modified an existing ordinary differential equation model by 1) evolving from a systemic model to a local model, 2) the incorporation of fibroblast activity, and3) including the effects of tissue oxygenation. Possible therapeutic targets, such as fibroblast death rate and rate of fibroblast recruitment have been identified by computational analysis. This model is a step toward constructing an integrative systems biology model of human wound healing. The coagulation and fibrinolytic systems are complex, inter-connected biological systems with major physiological roles. We present an Agent Based Modeling and Simulation (ABMS) approach to these complex interactions. This ABMS method successfully reproduces the initiation, propagation, and termination of blood clot formation and its lysis in vitro due to the activation of either the intrinsic or extrinsic pathways. Furthermore, the ABMS was able to simulate the pharmacological effects of two clinically used anticoagulants, warfarin and heparin, as well as the physiological effects of enzyme deficiency/dysfunction, i.e., hemophilia and antithrombin III-heparin binding impairment, on the coagulation system. The results of the model compare favorably with in vitro experimental data under both physiologic and pathophysiologic conditions. Our computational systems biology approach integrates reductionist experimental data into a cohesive model that allows rapid evaluation of the effects of multiple variables. Our ODE and AMBS models offer the ability to generate non-linear responses based on known relationships among variables and in silico modeling of mechanistic biological rules on computer software, respectively. Simulations of normal and disease states as well as effects of therapeutic intervention demonstrate the potential uses of computer simulation. Specifically, models may be applied to hypothesis generation and biological advances, discovery of new diagnostic and therapeutic options, platforms to test novel therapies, and opportunities to predict adverse events during drug development. The ultimate aim of such models is creation of bedside simulators that allow personalized, individual medicine; however, a myriad of opportunities for scientific advancement are opened through in silico experimentation.