Neutral Analysis Exposes Some Un-Answered Questions About Ceritinib

Automated images analysis of CLSM images and point-pattern analysis were applied to show material-induced switches from bacterial adhesion to colony growth on biomaterials. By two- or three-dimensional modeling, e.g., using cellular automata or agent-based models, the adhesion of pathogens and/or epithelial cells of the host on implant surfaces can be simulated. For the case of bacteria, see Siegismund et al. (2014b). This helps us to understand the onset of disease in the case where the pathogens win this ��race for the surface�� (Subbiahdoss et al., 2009) and the avoidance of disease in the case where the host cells win. This will also help to devise novel therapeutic strategies, as an Ceritinib appropriate surface structure of the implants can diminish adhesion by pathogens. A model for the thermal adaptation of Candida based on a differential equation system was proposed by Leach et al. (2012). That model appropriately describes the defense-evasion pair ��fever��heat shock response.�� Moreover, other modeling techniques have been used to study Candida infections, such as Bayesian modeling (Shankar et al., 2015) and dynamic interactive infectious networks (Chen and Wu, 2014). Several defense and evasion mechanism have been described by mathematical modeling. For example, the action of degradative enzymes can be simulated by kinetic models of metabolic networks (Heinrich and Schuster, 1996). Kinetic models of tryptophan metabolism (Stavrum et al., 2013) and of multi-drug resistance pumps have been published (Westerhoff et al., 2000). A large body of literature on the modeling of biofilm formation is available (Audretsch et al., 2013), though mostly on bacterial rather than fungal biofilms, for a review see Horn and Lackner (2014). Moreover, a gene regulatory network was inferred (Tierney et al., 2012). All of those modeling techniques could in principle be applied to investigate C. albicans' interactions with the host. In the present chapter, we outline the modeling methods based on game theory and agent-based models in more detail. 3.1. Game theory Metaphorically, the struggle between pathogens and the human immune system can be considered as a game in which each player attempts to win (Renaud and De Meeus, 1991; Hummert et al., 2014). This metaphor is quite useful because it allows one to understand that struggle as an extended optimization process. The extension is that the two counterparts (players) cannot always reach the optimal state because they may hinder each other in reaching it. Thus, suboptimal states can result (Hofbauer and Sigmund, 1998). A considerable number of game-theoretical models of bacterial and viral infections have been proposed, for a review see Hummert et al. (2014), while fungal infections are the subject of such studies to a lesser extent so far. To our knowledge, Hummert et al.