02207nas a2200157   4500008004100000022001400041245014700055210006900202260000900271300001600280490000700296520156900303100002601872700002301898856012801921       2021                        eng d  a2040-336400aImplication of adsorption preferences of ions and surfactants on the shape control of gold nanoparticles: a microscopic, atomistic perspective0 aImplication of adsorption preferences of ions and surfactants on  cDEC   a19549-195600 v133 aShape modulation of nanoparticles is crucial for their tailored applications; however, it depends on surfactants, ions, reactants, and other additives present in the growth solution. Here we dissect the role of surfactants, their counterions (halide ions), silver ions, and gold reactant in gold nanoparticle anisotropic growth using polarizable surfaces and nanoseed molecular dynamics simulation models. Our planar surface models predict a 14%-16% increment in cetyltrimethylammonium bromide (CTAB) coverage on Au(111) and Au(100) due to the surface polarization effect. The CTAB micelle adsorbs compactly similar to that observed on non-polarizable surfaces. The cetyltrimethylammonium chloride (CTAC) micelle remains in solution leaving the polarizable gold surfaces unprotected, similar to that observed with the non-polarizable surfaces, which favors isotropic growth. The cetyltrimethylammonium iodide (CTAI) micelle adsorbs with higher surface densities than CTAB on all the surfaces. The surface polarizable penta-twinned nanoseed model predicts the total surface coverage of the cetyltrimethylammonium cation (CTA(+)), Br- and Ag+ to be around two times higher on the side as compared to the tip of the nanoseed, leading to a 2.6 times higher initial rate of adsorption of AuCl2- on the tip than on the side. Predicted CTA(+) surface densities on the tip and the side of the nanoseed are consistent with experimental results. Our simulations explain the growth mechanism of anisotropic nanoparticles and the microscopic origin of their controlled shapes.1 aMeena, Santosh, Kumar1 aMeena, Chandrakala  uhttps://library.ncl.res.in/content/implication-adsorption-preferences-ions-and-surfactants-shape-control-gold-nanoparticles02794nas a2200265   4500008004100000022001400041245009800055210006900153260000800222300000700230490000700237520190500244653001502149653002302164653003202187653002102219653003502240653002102275653002002296100002102316700001902337700002302356700002102379856012802400       2022                        eng d  a0250-599100aEmerging landscape of molecular interaction networks: opportunities, challenges and prospects0 aEmerging landscape of molecular interaction networks opportuniti  cAPR  a240 v473 a<p>
	Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug-disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.</p>
10aCentrality10adisease mechanisms10ahybrid network-based models10amachine learning10amolecular interaction networks10anetwork topology10asystems biology1 aPanditrao, Gauri1 aBhowmick, Rupa1 aMeena, Chandrakala1 aSarkar, Ram, Rup  uhttps://library.ncl.res.in/content/emerging-landscape-molecular-interaction-networks-opportunities-challenges-and-prospects02297nas a2200205   4500008004100000022001400041245005100055210005100106260000800157300001000165490000700175520169900182100002301881700002301904700002001927700001801947700002401965700001901989856008302008       2023                        eng d  a1745-247300aEmergent stability in complex network dynamics0 aEmergent stability in complex network dynamics  cJUL  a1033+0 v193 a<p>
	The stable functionality of networked systems is a hallmark of their natural ability to coordinate between their multiple interacting components. Yet, real-world networks often appear random and highly irregular, raising the question of what are the naturally emerging organizing principles of complex system stability. The answer is encoded within the system's stability matrix-the Jacobian-but is hard to retrieve, due to the scale and diversity of the relevant systems, their broad parameter space and their nonlinear interaction dynamics. Here we introduce the dynamic Jacobian ensemble, which allows us to systematically investigate the fixed-point dynamics of a range of relevant network-based models. Within this ensemble, we find that complex systems exhibit discrete stability classes. These range from asymptotically unstable (where stability is unattainable) to sensitive (where stability abides within a bounded range of system parameters). Alongside these two classes, we uncover a third asymptotically stable class in which a sufficiently large and heterogeneous network acquires a guaranteed stability, independent of its microscopic parameters and robust against external perturbation. Hence, in this ensemble, two of the most ubiquitous characteristics of real-world networks-scale and heterogeneity-emerge as natural organizing principles to ensure fixed-point stability in the face of changing environmental conditions. Despite looking highly irregular, most real-world networks exhibit natural stability to external perturbations. A study of the properties of the stability matrix of networks now sheds light on the principles underlying this emerging stability.</p>
1 aMeena, Chandrakala1 aHens, Chittaranjan1 aAcharyya, Suman1 aHaber, Simcha1 aBoccaletti, Stefano1 aBarzel, Baruch  uhttps://library.ncl.res.in/content/emergent-stability-complex-network-dynamics