Computational simulations for disease modeling and efficient analysis tools using large data collections have become invaluable tools for Global Health programs fighting infectious diseases. Simulations are used in many ways e.g., from predicting the effectiveness of interventions for certain diseases through Bayesian-based data-model assimilation to genomic analysis of diverse vector species in their different growth states. Even though the approaches and technologies vary, they have several common requirements on the underlying infrastructure. Simulations for infectious diseases, for example, rely on environmental data like weather, geospatial data, biodiversity and transmission complexity. Data-intensive applications need efficient distributed data management capabilities facilitating replication services or Software-as-a-Service solutions. Such solutions might follow the paradigm to transfer applications to the data instead of transferring data to where the applications are deployed. In this paper we present our work towards providing a common extensible platform to build the computational investigation environment. This platform will provide an API for developers of science gateways, which can be adapted for specific simulations, various distributed data management technologies and diverse data structures. Furthermore, it will include metadata to increase the quality and the information about the data, its provenance and its context. Such an API will ease the development of new science gateways and the core technologies for both modeling and running the models. Developers can focus on the targeted domain and are relieved from re-developing core features for the underlying infrastructure. These gateways also enable the stakeholders (scientists, policy makers, etc.) in using the sophisticated tools and/or offer a single point of entry to large data collections and data analytics tools.