In this paper, we show how advanced methods of continuous optimization contribute to modeling, learning and problem solution in areas of environmental protection, medicine and development. We begin by describing gene-environment networks under various kinds of uncertainty, the underlying Chebychev approximation to do the needed parameter estimation, and how semi-infinite optimization and conic programming come into play. We investigate the structure and stability of the topological landscape comprising these networks, present various regression models and future pathways on how computational statistics could become employed. This article widely employs systems of ordinary differential equations, but also turns to the use of stochastic differential equations which allow an elegant way to include various areas of the financial world. Our presentation analyzes important interactions between biology, health, educational and financial sectors, it introduces main results obtained by the knowledge-based technologies applied, it discusses structural frontiers, ways to overcome them, and it gives an outlook.