In-silico scientific research is a complex task that involves the management of huge volumes of data and metadata produced during the scientific exploration life cycle, from hypothesis formulation up to its final validation. This wealth of data needs to be structured and managed in a way that readily makes sense to scientists, so that relevant knowledge may be extracted to contribute to the scientific investigation process. This paper proposes a scientific hypothesis conceptual model that allows scientists to represent the phenomenon been investigated, the hypotheses formulated in the attempt to explain it, and provides the ability to store results of experiment simulations with their corresponding provenance metadata. The proposed model supports scientific life-cycle: provenance, scientists exchange of information, experiment reproducibility, model steering and results analyses. A cardiovascular numerical simulation illustrates the applicability of the model and an initial implementation using SciDB is discussed.