02098nas a2200181   4500008004100000022001400041245008800055210006900143260010300212300001100315490000800326520137100334100003001705700002101735700002101756700002301777856011601800       2016                        eng d  a0021-925800aIdentification of unintuitive features of sumoylation through mathematical modeling0 aIdentification of unintuitive features of sumoylation through ma  a9650 ROCKVILLE PIKE, BETHESDA, MD 20814-3996 USAbAMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INCcAPR  a9458-+0 v2913 a<p>Sumoylation is a multistep, multienzymatic post-translational modification in which a small ubiquitin-like modifier protein (SUMO) is attached to the target. We present the first mathematical model for sumoylation including enzyme mechanism details such as autosumoylation of E2 and multifunctional nature of SENP. Simulations and analysis reveal three nonobvious properties for the long term response, modeled as an open system: (i) the steady state sumoylation level is robust to variation in several enzyme properties; (ii) even when autosumoylation of E2 results in equal or higher activity, the target sumoylation levels are lower; and (iii) there is an optimal SENP concentration at which steady state target sumoylation level is maximum. These results are qualitatively different for a short term response modeled as a closed system, where e.g. sumoylation always decreases with increasing SENP levels. Simulations with multiple targets suggest that the available SUMO is limiting, indicating a possible explanation for the experimentally observed low fractional sumoylation. We predict qualitative differences in system responses at short post-translational and longer transcriptional time scales. We thus use this mechanism-based model to explain system properties and generate testable hypotheses for existence and mechanism of unexpected responses.</p>1 aPuntambekar, Shraddha, S.1 aNyayanit, Dimpal1 aSaxena, Priyanka1 aGadgil, Chetan, J.  uhttp://library.ncl.res.in/content/identification-unintuitive-features-sumoylation-through-mathematical-modeling02124nas a2200265   4500008004100000022001400041245009000055210006900145260000800214300000800222490000700230520122000237653002401457653002201481653001901503653003301522653004301555653002501598653002401623653002501647100002201672700003001694700002301724856011101747       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.  uhttp://library.ncl.res.in/content/method-identification-sensitive-nodes-boolean-models-biological-networks