Seminars

Discussion meeting on extreme QCD matter

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Discussion meeting on extreme QCD matter

Click here for Schedule. (This announcement is being made so that the conference info will show up on IMSc main page).

We present the results of our analytical as well as numerical investigation on transition to synchronization in Sakaguchi-Kuramoto (SK) model on complex networks. On one hand, we analytically derive self-consistent equations involving the order parameter and the group angular velocity for fully as well as partially degree-frequency correlated SK model using annealed approximation and mean field approach. We then numerically compute the critical coupling strength for the onset of synchronization from the self-consistent equations. On the other hand, for the validation of the results obtained from the self-consistent equations, we simulate the complex networks numerically for Barab\'{a}si-Albert (BA) scale-free networks and Erd\"{o}s-R\'{e}nyi (ER) random network. We also present here a mathematical framework for achieving optimal as well as perfect synchronization states in SK model on complex networks having single layer and two layers (multiplex). This is a challenging task, as the phase-frustration in Kuramoto dynamics usually inhibits synchronization. For single layer case, we also check the sensitivity of the achieved synchronization state by perturbing the constructed frequency set and by changing the network structure. Where as for the multiplex set up, we derive a multiplex synchrony alignment function (MSAF) for the purpose of achieving optimal synchronization.

Bio-complexity is a thriving research area in the 21st century, focusing mainly on organisms-environment interactions across the levels of biological organizations. However, biological levels of organizations have classically divided into two mainstream science disciplines. The first one is molecular and cell biology at which evolutionary mechanism of natural selection plays key role in shaping the organization and has remained to be a primary force interlinking the molecule/cell structures with the biological functions. The other one is ecological and environmental sciences that neglect the evolutionary effects with the reasonable assumption that ecological changes are taking place at much faster time scale than the evolutionary changes. The recognizable gap between these two has remained open until recently, mainly due to technological barriers of generating and processing a large volume of molecular data. Rapid development of genomic science methods and high-throughput molecular data-generating techniques are stimulating the bio-complexity research with great opportunities of addressing ambitious questions of determining a general underlying principle of biological regulations observed and measured across the levels. Along this line of research, in this presentation, I will introduce the general idea of limiting resource that I have started developing since 2006 and will brief the Limiting Resource-Diversity-Functioning of the Systems (R-D-FS) framework for examining the evolution and design of biological regulatory systems. Focusing mainly on adaptive mechanisms, I will present three recent, published studies illustrating how R-D-FS approach work. In the first example, I will present the direct link between molecules and ecosystems via nitrogen limitation and will show how we could get potential biochemical control over the fundamental process of ecosystem nitrogen cycle. In the second example, I will show a bi-stable adaptive mechanism that regulates a transceptor (a transporter cum sensor) molecule in plant’s root cell. In the third example, I will present allosteric regulation of an important enzyme, glutamate dehydrogenase (GDH), that fundamentally link two essential biological process of nitrogen assimilation and TCA cycle. As it will be seen in all these three examples, this research is highly interdisciplinary, encompassing tools, approaches, and ideas at the interface of biology, physics, chemistry, mathematics, and computer sciences. Then, I will talk about future direction of my research that combines both translational and basic in-silico research.

TBA