Systems approaches in molecular and cell biology: Making sense out of data; providing meaning to models

Two very different research strategies and mathematical modeling cultures will be outlined: Bottom-up theory-driven modeling and top-down data-driven modeling. The former encapsulates prior knowledge and hypotheses, while the latter extracts relevant co-variation patterns within and between large data tables. The former relies on the scientist defining non-linear differential equations to model the dynamics of a process; the latter automatically finds and displays dominant latent structures by multivariate eigen-structure approximation. The two approaches have different strengths and weaknesses. Based on our experiences, we therefore here suggest a possible way for combining these approaches.