High-resolution hyperfine spectroscopy of excited states using electromagnetically-induced transparency

We use the phenomenon of electromagnetically-induced transparency in a three-level atomic system for hyperfine spectroscopy of upper states that are not directly coupled to the ground state. The three levels form a ladder system: the probe laser couples the ground state to the lower excited state, while the control laser couples the two upper states. As the frequency of the control laser is scanned, the probe absorption shows transparency peaks whenever the control laser is resonant with a hyperfine level of the upper state. As an illustration of the technique, we measure hyperfine structure in the $7S_{1/2}$ states of $^{85}$Rb and $^{87}$Rb, and obtain an improvement of more than an order of magnitude over previous values.Comment: 7 pages, 6 figure

Role of population transfer under strong probe conditions in electromagnetically induced transparency

We analyze theoretically the phenomenon of electromagnetically induced transparency (EIT) under conditions where the probe laser is not in the usual weak limit. We consider the effects in both three-level and four-level systems, which are either closed or open (due to losses to an external metastable level). We find that the EIT dip almost disappears in a closed three-level system but survives in an open system. In four-level systems, there is a narrow enhanced-absorption peak (EITA) at line center, which has applications as an optical clock. The peak converts to an EIT dip in a closed system, but again survives in an open system.Comment: 4 pages, 4 figure, accepted in Optics Communication

Network controllability solutions for computational drug repurposing using genetic algorithms

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdos-Renyi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches

Network analytics for drug repurposing in COVID-19

To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein–protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.</p

Target Controllability of Cancer Networks

Advances in the field of complex networks theory and network biology pave a new way to define human health through the study of networks of proteins, genes, metabolites, modules across cell signaling pathways, and clinical data.Combinations of large scale biological datasets and concepts from network theory, and systems biology produce new insights into the complex dynamic processes involved in human diseases such as cancer. To develop novel datadriven computational tools for discovering the insights of human diseases and for a new approach to multi-drug therapies for personalized therapeutics, it needs combinations of the high-quality set of human interactome networks, disease-specific expression data, and powerful network controllability algorithmics. Therefore, we address the issue of this thesis with the focus to integrate network biology and network controllability approach, to gain useful insight in the finding of the complex mechanism of cancer networks and open the door for a novel drug target approach called multi-drug therapeutics. The first part of the thesis presents the network biology approaches to study the interactome of the biological systems and decode the wiring diagram of the cellular information processing systems. It reveals a variety of high-level intramolecular relationships including protein-protein interaction networks (PPI), protein compound interactions, gene regulatory interactions, and metabolic pathways. These interactions play a key role in the development of diseases and various types of cancers. One characteristic of such networks is that a small number of nodes in the networks are highly connected. Another characteristic is that a group of physically and functionally interconnected molecules driving to achieve a common biological process, have a modular structure. Further, through a minimum number of target nodes a full (partial) controllability of these intracellular network can be achieved. The second part of the thesis presents the network controllability approach and some of the algorithms used in our case studies on different types of cancer PPI signaling networks. Recently, network control theory has been increasingly used in engineering and mathematics which also opens the way to investigate control principals for complex biological interaction networks through a minimum set of input (driver) nodes. According to control theory, a dynamical system may be steard such that its output is driven towards some desired final states (e.g target cancer essential proteins in PPI networks) via suitably-picked inputs (e.g. manipulating a set of driver proteins). Therefore, it is necessary to understand the dynamics of these complex networks, and their evolution rules (i.e., expressed as a system of linear equations) which govern the systems dynamics over time. This doctoral thesis provides the target control theory approach fine tuned for the analysis of specific cancer signaling transduction PPI networks. The control approach presented here can be an impressive framework for effective development of multi drug-target therapeutics. We, therefore, expect that our approach can open a new way towards effective and efficient therapeutics target and a key resource towards personalized medicine in cancer