Johnson, Kathleen

Levin, Michael

Manicka, Santosh

Murrugarra, David

English

ISU ReD: Research and eData

Presentation for the Symposium on BEER November 2022The nonlinearity of regulation in biological networksSantosh Manicka1, Kathleen Johnson2, Michael Levin1, David Murrugarra3,*1Tufts University, Medford, MA 021552Eastern Kentucky University, Richmond, KY 404753University of Kentucky, Lexington, KY 40506murrugarra@uky.eduThe extent to which the components of a biological system are (non)linearly regulated determines howamenable they are to therapy and control. To better understand this property termed ‘regulatory nonlinear-ity’, we analyzed a suite of 137 published Boolean network models, containing a variety of complex nonlinearinteractions, using a probabilistic generalization of Boolean logic that George Boole himself had proposed.Leveraging the continuous-nature of this formulation, we used Taylor decomposition to approximate themodels with various levels of nonlinearity. A comparison of the resulting series of approximations of thebiological models with appropriate random ensembles revealed that biological regulation tends to be lessnonlinear than expected. A further categorical analysis of the biological models revealed that the nonlinear-ity of cancer and disease networks could not only be sometimes higher than expected but are also relativelymore variable. We show that this variation is caused by differences in the apportioning of information amongthe various orders of nonlinearity. Taken together, our results suggest, but do not imply, that biological reg-ulation may have evolved to be more linear on average, and certain systems such as cancer may have, on theother hand, evolved to be more nonlinear.

The nonlinearity of regulation in biological networks

https://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1624&context=beer

The nonlinearity of regulation in biological networks

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ISU ReD: Research and eData