02918nas a2200193   4500008004100000022001400041245006200055210006100117260008100178300000700259490000600266520225400272100002502526700001802551700001802569700001802587700002302605856009602628       2010                        eng d  a1745-615000aKinetic model of TBP auto-regulation exhibits bistability0 aKinetic model of TBP autoregulation exhibits bistability  a236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLANDbBIOMED CENTRAL LTDcAUG  a500 v53 a<p>Background: TATA Binding Protein (TBP) is required for transcription initiation by all three eukaryotic RNA polymerases. It participates in transcriptional initiation at the majority of eukaryotic gene promoters, either by direct association to the TATA box upstream of the transcription start site or by indirectly localizing to the promoter through other proteins. TBP exists in solution in a dimeric form but binds to DNA as a monomer. Here, we present the first mathematical model for auto-catalytic TBP expression and use it to study the role of dimerization in maintaining the steady state TBP level. Results: We show that the autogenous regulation of TBP results in a system that is capable of exhibiting three steady states: an unstable low TBP state, one stable state corresponding to a physiological TBP concentration, and another stable steady state corresponding to unviable cells where no TBP is expressed. Our model predicts that a basal level of TBP is required to establish the transcription of the TBP gene, and hence for cell viability. It also predicts that, for the condition corresponding to a typical mammalian cell, the high-TBP state and cell viability is sensitive to variation in DNA binding strength. We use the model to explore the effect of the dimer in buffering the response to changes in TBP levels, and show that for some physiological conditions the dimer is not important in buffering against perturbations. Conclusions: Results on the necessity of a minimum basal TBP level support the in vivo observations that TBP is maternally inherited, providing the small amount of TBP required to establish its ubiquitous expression. The model shows that the system is sensitive to variations in parameters indicating that it is vulnerable to mutations in TBP. A reduction in TBP-DNA binding constant can lead the system to a regime where the unviable state is the only steady state. Contrary to the current hypotheses, we show that under some physiological conditions the dimer is not very important in restoring the system to steady state. This model demonstrates the use of mathematical modelling to investigate system behaviour and generate hypotheses governing the dynamics of such nonlinear biological systems.</p>1 aGokhale, Sucheta, A.1 aRoshan, Reema1 aKhetan, Vivek1 aPillai, Beena1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/kinetic-model-tbp-auto-regulation-exhibits-bistability-001512nas a2200157   4500008004100000245005100041210005100092260000800143300001400151490000600165520103500171100002501206700002101231700002301252856007901275       2011                        eng d00aSystems view of the protein expression process0 aSystems view of the protein expression process  cDEC  a139–1500 v53 a<p>Many biological processes are regulated by changing the concentration and activity of proteins. The presence of a protein at a given subcellular location at a given time with a certain conformation is the result of an apparently sequential process. The rate of protein formation is influenced by chromatin state, and the rates of transcription, translation, and degradation. There is an exquisite control system where each stage of the process is controlled both by seemingly unregulated proteins as well as through feedbacks mediated by RNA and protein products. Here we review the biological facts and mathematical models for each stage of the protein production process. We conclude that advances in experimental techniques leading to a detailed description of the process have not been matched by mathematical models that represent the details of the process and facilitate analysis. Such an exercise is the first step towards development of a framework for a systems biology analysis of the protein production process.</p>1 aGokhale, Sucheta, A.1 aNyayanit, Dimpal1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/systems-view-protein-expression-process01914nas a2200157   4500008004100000022001400041245010800055210006900163260011000232300001200342490000600354520121800360100002501578700002301603856013001626       2012                        eng d  a1742-206X00aAnalysis of miRNA regulation suggests an explanation for `unexpected' increase in target protein levels0 aAnalysis of miRNA regulation suggests an explanation for unexpec  aTHOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLANDbROYAL SOC CHEMISTRYcDEC  a760-7650 v83 a<p>MicroRNA (miRNA) has been mostly associated with decrease in target protein expression levels. Recently, `unexpected' observations of increase in target protein expression attributed to microRNA regulation have been reported. We formulate a comprehensive model for regulation by miRNA that includes both reversible mRNA-miRNA binding and selective return of RNA. We use this mathematical model incorporating multiple individual steps in the regulation process to study the simultaneous effects of these steps on the target protein level. We show that four dimensionless numbers obtained from 12 rate constants are sufficient to define the relative change in steady state target protein levels. We quantify the range of these numbers for which such pleiotropic increase in protein levels is possible, and interpret the experimental findings in the framework of our model such that the results are no longer unexpected. Finally, we show through stochastic simulation that the nature of the target protein distribution remains unchanged and the relative steady state noise levels are also completely defined by the values of these dimensionless numbers, irrespective of the individual reaction rate constants.</p>1 aGokhale, Sucheta, A.1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/analysis-mirna-regulation-suggests-explanation-unexpected-increase-target-protein-levels-001831nas a2200181   4500008004100000022001400041245009000055210006900145260011000214300001400324490000600338520108900344100002501433700002101458700002701479700002301506856012001529       2012                        eng d  a1742-206X00aSimple method for incorporating dynamic effects of intronic miRNA mediated regulation0 aSimple method for incorporating dynamic effects of intronic miRN  aTHOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLANDbROYAL SOC CHEMISTRYcAPR  a2145-21520 v83 a<p>The importance of microRNA (miRNA) in modulating gene expression at the post-transcriptional level is well known. Such regulation has been shown to influence the dynamics of several regulatory networks including the cell cycle. In this study we incorporated regulatory effects of intronic miRNA into an existing mathematical model of the cell cycle through the use of an existing `proxy' protein - the host protein. It was observed that the incorporation of intronic miRNA mediated regulation improved the performance of the model resulting in a closer match to experimental results. To test the universality of this approach we compared the effects of intronic miRNA mediated regulation and host protein mediated regulation. Further, we compared miRNA mediated and protein mediated positive and negative feedback regulations of the target protein. We found that the target protein profiles were predominantly similar. These observations show the applicability of our method for incorporating intronic miRNA mediated dynamic effects in models for regulation of gene expression.</p>1 aGokhale, Sucheta, A.1 aHariharan, Manoj1 aBrahmachari, Samir, K.1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/simple-method-incorporating-dynamic-effects-intronic-mirna-mediated-regulation-002323nas a2200157   4500008004100000022001400041245010200055210006900157260009100226300002900317490000700346520164400353100002501997700002302022856012002045       2015                        eng d  a1932-620300aRanking of reactions based on sensitivity of protein noise depends on the choice of noise measure0 aRanking of reactions based on sensitivity of protein noise depen  a1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USAbPUBLIC LIBRARY SCIENCEcDEC  aArticle Number: e01438670 v103 a<p>Gene expression is a stochastic process. Identification of the step maximally affecting noise in the protein level is an important aspect of investigation of gene product distribution. There are numerous experimental and theoretical studies that seek to identify this important step. However, these studies have used two different measures of noise, viz. coefficient of variation and Fano factor, and have compared different processes leading to contradictory observations regarding the important step. In this study, we performed systematic global and local sensitivity analysis on two models of gene expression to investigate relative contribution of reaction rate parameters to steady state noise in the protein level using both the measures of noise. We analytically and computationally showed that the ranking of parameters based on the sensitivity of the noise to variation in a given parameter is a strong function of the choice of the noise measure. If the Fano factor is used as the noise measure, translation is the important step whereas for coefficient of variation, transcription is the important step. We derived an analytical expression for local sensitivity and used it to explain the distinct contributions of each reaction parameter to the two measures of noise. We extended the analysis to a generic linear catalysis reaction system and observed that the reaction network topology was an important factor influencing the local sensitivity of the two measures of noise. Our study suggested that, for the analysis of contributions of reactions to the noise, consideration of both the measures of noise is important.</p>1 aGokhale, Sucheta, A.1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/ranking-reactions-based-sensitivity-protein-noise-depends-choice-noise-measure-1