In the future, a global analysis of differentially regulated genes, based on their ontologies, is needed to satisfyingly describe preovulatory ovarian mechanisms. In addition, a cellular localization of gene expression will contribute in the understanding of their respective roles in the preovulaory ovarian physiology. Nevertheless, the present study clearly demonstrates that distinct (i.e. steroidogenic, proteolytic, proinflammatory) but concomitant events occur in the preovulatory ovary. Together, all those events concur to achieve the same goal which is the release, at the time of ovulation, of a fully competent oocyte, ready to be fertilized. An object simulation model for modeling hypothetical disease epidemics – EpiFlex BACKGROUND: EpiFlex is a flexible, easy to use computer model for a single computer, intended to be operated by one user who need not be an expert. Its purpose is to study in-silico the epidemic behavior of a wide variety of diseases, both known and theoretical, by simulating their spread at the level of individuals contracting and infecting others. To understand the system fully, this paper must be read together in conjunction with study of the software and its results. EpiFlex is evaluated using results from modeling influenza A epidemics and comparing them with a variety of field data sources and other types of modeling. EpiFlex is an object-oriented Monte Carlo system, allocating entities to correspond to individuals, disease vectors, diseases, and the locations that hosts may inhabit. EpiFlex defines eight different contact types available for a disease. Contacts occur inside locations within the model. Populations are composed of demographic groups, each of which has a cycle of movement between locations. Within locations, superspreading is defined by skewing of contact distributions. RESULTS: EpiFlex indicates three phenomena of interest for public health: (1) R(0 )is variable, and the smaller the population, the larger the infected fraction within that population will be; (2) significant compression/synchronization between cities by a factor of roughly 2 occurs between the early incubation phase of a multi-city epidemic and the major manifestation phase; (3) if better true morbidity data were available, more asymptomatic hosts would be seen to spread disease than we currently believe is the case for influenza. These results suggest that field research to study such phenomena, while expensive, should be worthwhile. CONCLUSION: Since EpiFlex shows all stages of disease progression, detailed insight into the progress of epidemics is possible. EpiFlex shows the characteristic multimodality and apparently random variation characteristic of real world data, but does so as an emergent property of a carefully constructed model of disease dynamics and is not simply a stochastic system. EpiFlex can provide a better understanding of infectious diseases and strategies for response. The most commonly used measure in public health, R 0 , is estimated from historical data and derived from SIS/SIR type models (and descendents) for forward projection [14, 15] R 0 is the basic reproductive ratio for how many individuals each infected person is going to infect [16] R 0 is often used on its own in public health as an indicator of epidemic probability; if R 0 < 1 then an epidemic is not generally considered possible, for R 0 > 1, the larger the value, the more likely an epidemic is to occur. R 0 is a composite value describing the behavior of an infectious agent. Hence, R 0 can be decomposed classically, for example, as: p d c, where p is probability of infection occurring for a contact, d is duration of infectiousness, and c is number of contacts [17] .