Complexity-Aware Simple Modeling

Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach -- complexity-aware simple modeling -- that can bridge the gap between the small- and large-scale approaches.\n\nHighlightsO_LISimple or small-scale models allow deduction of fundamental principles of biological systems\nC_LIO_LIDetailed or large-scale models can be quantitatively accurate but difficult to analyze\nC_LIO_LIComplexity-aware simple models can extract principles that are robust to the presence of unknown complex interactions\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC=\"FIGDIR/small/248419_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@1cc6c8borg.highwire.dtl.DTLVardef@1ea1eb3org.highwire.dtl.DTLVardef@185a0b4org.highwire.dtl.DTLVardef@1f9c6b9_HPS_FORMAT_FIGEXP M_FIG Graphical abstract\n\nC_FIG