@phdthesis{oai:kyutech.repo.nii.ac.jp:00005325,
 author = {Hasan,  A. B. M. Shamim Ul},
 month = {2018-01-29},
 note = {1 Introduction||2 Simplification of gene expression model||3 Noise analysis among different cascades of gene regulatory networks||4 Gene expression noise can induce stochastic bimodality, even multimodality in deterministically monostable description with non-cooperative binding||5 Mathematical comparison of memory functions between mutual activation and repression networks in a stochastic environment||6 Conclusion and Future Works, Stochasticity in gene regulatory network has become increasingly distinguished in the current thinking of system biology. So it is important to know the variety of noise in gene regulatory network. Here, we constructed different types of gene regulatory networks, two-gene regulated mutual activation network of positive feedback; two-gene regulated mutual repression network of positive feedback. We have investigated the dynamical behavior of noise i.e. noise induced bistable （bimodal）, multistable （multimodal） of this gene regulatory networks in deterministic and stochastic approaches at the steady state level. Also, we have investigated the one gene with respect to another one in both deterministic, stochastic environments with non-cooperative transcription factor binding / unbinding on the promoter region by using non-symmetric kinetic parameters to predict the bimodal and multimodal gene expression.On the other hand, biological memory is a ubiquitous function that can generate a sustained response to a transient inductive stimulus. To better understand this function, we must consider the mechanisms by which different structures of genetic networks achieve memory. Here, we investigated two competitive gene regulatory network models: the regulated mutual activation network （MAN） and the regulated mutual repression network （MRN）. Stochasticity deteriorated the memory function of both the MAN and the MRN models.Theoretical analysis was performed to support the simulation results. We exemplified the stochastic potential profile of the one-variable rate equation deriving from the MAN and MRN models. In the presence of noise, a stochastic potential and the mean first-time passage （MFTP） are used to investigate bistability and memory persistency by the Fokker-Planck equation （FPE）, which is derived from the chemical Langevin equation.Mathematical comparison by simulation and theoretical analysis identified functional differences in the stochastic memory between the competitive models: specifically, the MAN provided much more robust, persistent memory than the MRN. The stochastic memory pattern of the MAN can be adjusted by changing the binding strength of the activators, whereas the MRN required highly cooperative and strong binding repressors for robust memory.Therefore, we should select the MAN or MRN for an optimal, rational design. If a robust memory is required, a mutual activation network should be selected. If the opposite state of protein synthesis is necessary, a mutual repression network must be selected, although the memory effect is fragile. This fragility may be related to the fact that suppression cascades amplify noise compared with activation cascades. A mutual activation network comprising two protein kinases, p42 MAPK and Cdc2, is suggested to require robust memory. On the other hand, a mutual repression network comprising the cI and Cro proteins would require a gene expression system opposite to that of robust memory. A Notch-Delta mutual repression network is an intelligible example to communicate between neighboring cells. An increase in Notch activity within a cell decreases Notch activity in neighboring cells, and thus Notch-Delta mutual repression provides inhomogeneous or opposite protein synthesis in homogeneous cell populations. Our results expected to have significant implications on the dynamical behavior of the genetic network in cell populations., 九州工業大学博士学位論文 学位記番号：情工博甲第330号 学位授与年月日：平成29年9月22日, 平成29年度},
 school = {九州工業大学},
 title = {Simulation and theoretical analysis for stochastic dynamics of biochemical networks},
 year = {}
}