Md. Jahoor Alam

The enhancement of stability of p53 in MTBP induced p53-MDM2 regulatory network.

We have modeled an MTBP-MDM2-p53 regulatory network by integrating p53-MDM2 autoregulatory model (Proctor and Gray, 2008) with the effect of a cellular protein MTBP (MDM2 binding protein) which is allowed to bind with MDM2 (Brady et al., 2005). We study this model to investigate the activation of p53 and MDM2 steady state levels induced by MTBP protein under different stress conditions. Our simulation results in three approaches namely deterministic, Chemical Langevin equation and stochastic simulation of Master equation show a clear transition from damped limit cycle oscillation to fixed point oscillation during a certain time period with constant stress condition in the cell. This transition is the signature of transition of p53 and MDM2 levels from activated state to stabilized steady state levels. We present various phase diagrams to show the transition between unstable and stable states of p53 and MDM2 concentration levels and also their possible relations among critical value of the parameters at which the respective protein level reach stable steady states. In the stochastic approach, the dynamics of the proteins become noise induced process depending on the system size. We found that this noise enhances the stability of the p53 steady state level.

The Dynamics of Stress p53-Mdm2 Network Regulated by p300 and HDAC1

We construct a stress p53-Mdm2-p300-HDAC1 regulatory network that is activated and stabilised by two regulatory proteins, p300 and HDAC1. Different activation levels of observed due to these regulators during stress condition have been investigated using a deterministic as well as a stochastic approach to understand how the cell responds during stress conditions. We found that these regulators help in adjusting p53 to different conditions as identified by various oscillatory states, namely fixed point oscillations, damped oscillations and sustain oscillations. On assessing the impact of p300 on p53-Mdm2 network we identified three states: first stabilised or normal condition where the impact of p300 is negligible, second an interim region where p53 is activated due to interaction between p53 and p300, and finally the third regime where excess of p300 leads to cell stress condition. Similarly evaluation of HDAC1 on our model led to identification of the above three distinct states. Also we observe that noise in stochastic cellular system helps to reach each oscillatory state quicker than those in deterministic case. The constructed model validated different experimental findings qualitatively.

Intercellular synchronization of diffusively coupled Ca(2+) oscillators.

We examine the synchrony in the dynamics of localized [Ca2 + ]i oscillations among a group of cells exhibiting such complex Ca2 +  oscillations, connected in the form of long chain, via diffusing coupling where cytosolic Ca2 +  and inositol 1,4,5-triphosphate are coupling molecules. Based on our numerical results, we could able to identify three regimes, namely desynchronized, transition and synchronized regimes in the (T − ke) (time period-coupling constant) and (A − ke) (amplitude-coupling constant) spaces which are supported by phase plots (Δϕ verses time) and recurrence plots, respectively. We further show the increase of synchronization among the cells as the number of coupling molecules increases in the (T − ke) and (A − ke) spaces.

Stochastic synchronization of interacting pathways in testosterone model

We examine the possibilities of various coupling mechanisms among a group of identical stochastic oscillators via Chemical Langevin formalism where each oscillator is modeled by stochastic model of testosterone (T) releasing pathway. Our results show that the rate of synchrony among the coupled oscillators depends on various parameters namely fluctuating factor, coupling constants [symbol; see text], and interestingly on system size. The results show that synchronization is achieved much faster in classical deterministic system rather than stochastic system. Then we do large scale simulation of such coupled pathways using stochastic simulation algorithm and the detection of synchrony is measured by various order parameters such as synchronization manifolds, phase plots etc and found that the proper synchrony of the oscillators is maintained in different coupling mechanisms and support our theoretical claims. We also found that the coupling constant follows power law behavior with the systems size (V) by [symbol; see text] ~ AV(-γ), where γ=1 and A is a constant. We also examine the phase transition like behavior in all coupling mechanisms that we have considered for simulation. The behavior of the system is also investigated at thermodynamic limit; where V → ∞, molecular population, N → ∞ but N/V → finite, to see the role of noise in information processing and found the destructive role in the rate of synchronization.

Measurement of phase synchrony of coupled segmentation clocks

The temporal behavior of segmentation clock oscillations shows phase synchrony via mean field like coupling of delta protein restricting to nearest neighbors only, in a configuration of cells arranged in a regular three dimensional array. We found the increase of amplitudes of oscillating dynamical variables of the cells as the activation rate of delta-notch signaling is increased, however, the frequencies of oscillations are decreased correspondingly. Our results show the phase transition from desynchronized to synchronized behavior by identifying three regimes, namely, desynchronized, transition and synchronized regimes supported by various qualitative and quantitative measurements.