02125nas a2200265   4500008004100000022001400041245009000055210006900145260000800214300000800222490000700230520122000237653002401457653002201481653001901503653003301522653004301555653002501598653002401623653002501647100002201672700003001694700002301724856011201747       2018                        eng d  a1751-884900aMethod for identification of sensitive nodes in boolean models of biological networks0 aMethod for identification of sensitive nodes in boolean models o  cFEB  a1-60 v123 a<p>Biological systems are often represented as Boolean networks and analysed to identify sensitive nodes which on perturbation disproportionately change a predefined output. There exist different kinds of perturbation methods: perturbation of function, perturbation of state and perturbation in update scheme. Nodes may have defects in interpretation of the inputs from other nodes and calculation of the node output. To simulate these defects and systematically assess their effect on the system output, two new function perturbations, referred to as not of function' and function of not', are introduced. In the former, the inputs are assumed to be correctly interpreted but the output of the update rule is perturbed; and in the latter, each input is perturbed but the correct update rule is applied. These and previously used perturbation methods were applied to two existing Boolean models, namely the human melanogenesis signalling network and the fly segment polarity network. Through mathematical simulations, it was found that these methods successfully identified nodes earlier found to be sensitive using other methods, and were also able to identify sensitive nodes which were previously unreported.</p>10abiological networks10aBoolean functions10aBoolean models10afly segment polarity network10ahuman melanogenesis signalling network10aperturbation methods10aPerturbation theory10aphysiological models1 aDnyane, Pooja, A.1 aPuntambekar, Shraddha, S.1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/method-identification-sensitive-nodes-boolean-models-biological-networks