Jayanta Kumar Das

Camellia Ray, Jayanta Kumar Das, Pabitra Pal Choudhury

Boolean networks are used to model biological networks such as gene regulatory networks. Often Boolean networks show very chaotic behaviour which is sensitive to any small perturbations. In order to reduce the chaotic behaviour and to attain stability in the gene regulatory network, nested Canalizing Functions (NCFs) are best suited. NCFs and its variants have a wide range of applications in systems biology. Previously, many works were done on the application of canalizing functions, but there were fewer methods to check if any arbitrary Boolean function is canalizing or not. In this paper, by using Karnaugh Map this problem is solved and also it has been shown that when the canalizing functions of variable is given, all the canalizing functions of variable could be generated by the method of concatenation. In this paper we have uniquely identified the number of NCFs having a particular Hamming Distance (H.D) generated by each variable as starting canalizing input. Partially NCFs of 4 variables has also been studied in this paper.

Camellia Ray, Jayanta Kumar Das, Pabitra Pal Choudhury

Boolean networks are used to model biological networks such as gene regulatory networks. Often Boolean networks show very chaotic behavior which is sensitive to any small perturbations.In order to reduce the chaotic behavior and to attain stability in the gene regulatory network,nested canalizing functions(NCF)are best suited NCF and its variants have a wide range of applications in system biology. Previously many work were done on the application of canalizing functions but there were fewer methods to check if any arbitrary Boolean function is canalizing or not. In this paper, by using Karnaugh Map this problem gas been solved and also it has been shown that when the canalizing functions of n variable is given, all the canalizing functions of n+1 variable could be generated by the method of concatenation. In this paper we have uniquely identified the number of NCFs having a particular hamming distance (H.D) generated by each variable x as starting canalizing input. Partially nested canalizing functions of 4 variables have also been studied in this paper. Keywords: Karnaugh Map, Canalizing function, Nested canalizing function, Partially nested canalizing function,concatenation

Jayanta Kumar Das, Ranjeet Kumar Rout, Pabitra Pal Choudhury

Interaction graphs provide an important qualitative modeling approach for System Biology. This paper presents a novel approach for construction of interaction graph with the help of Boolean function decomposition. Each decomposition part (Consisting of 2-bits) of the Boolean functions has some important significance. In the dynamics of a biological system, each variable or node is nothing but gene or protein. Their regulation has been explored in terms of interaction graphs which are generated by Boolean functions. In this paper, different classes of Boolean functions with regards to Interaction Graph with biologically significant properties have been adumbrated.

Jayanta Kumar Das, Swarup Roy, Pietro Hiram Guzzi

The development of therapeutic targets for COVID-19 treatment is based on the understanding of the molecular mechanism of pathogenesis. The identification of genes and proteins involved in the infection mechanism is the key to shed out light into the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced the accumulation of both protein and genetic interactions. In this work we integrate these available results and we obtain an host protein-protein interaction network composed by 1432 human proteins. We calculate network centrality measures to identify key proteins. Then we perform functional enrichment of central proteins. We observed that the identified proteins are mostly associated with several crucial pathways, including cellular process, signalling transduction, neurodegenerative disease. Finally, we focused on proteins involved in causing disease in the human respiratory tract. We conclude that COVID19 is a complex disease, and we highlighted many potential therapeutic targets including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, HSPA1A, which are central and also associated with multiple diseases