Computational systems biology complements experimental biology in unique ways that are hoped to reveal insights and a depth of understanding not achievable without systems approaches. A major challenge of systems biology continues to be the determination of parameter values for mathematical models. While some models can be analyzed in symbolic form, these are few and far between, and the lack of parameter values is a true obstacle for most computational analyses of realistic biological phenomena. As a consequence, computational modelers tend to take on a problem only if there is a relatively solid database for parameter estimation. Interestingly, biologists very often have very detailed mental models of the phenomenon they are investigating and are not really interested in absolutely precise numerical results, as long as they can test relevant, semi-quantitative hypotheses. However, neither they nor their modeling colleagues have the means of translating the mental models into numerical mathematical structures that would allow advanced diagnosis and testing. I will speculate in this presentation on a possible way to bridge the cleft between mental and numerical models, using modern methods from Biochemcial Systems Theory. The envisioned technique is tentatively called "concept map modeling" and seems quite reasonable, but I do not have proof yet that it will actually work in real-world applications.