Revisiting the Non-Reductionistic Ayurvedic approach in the light of modern biophysics

Post Doctoral Research Scholar at Georgia State University, Atlanta


There is an increasing emphasis on personalized medicines by going beyond the prevalent reductionist approach in studying diseases, pathologies and whole biological systems. The attempted over-simplification often fails to capture the complex intratissue interactions and dynamic responses towards environmental stresses. The correct understanding of processes of health and disease remains lost in the wide variety of data sets contributed by the knowledge of various molecular actors accumulated over years of toiling research work. Ayurveda, which is a traditional system of medicine originated in the ancient Vedic times of India, ever since its inception has already, incorporated and focussed on primary physiological principles: Kapha, Pitta and Vaat . The knowledge and practice of Ayurveda is based on the interaction between these governing principles. The recent interdisciplinary endeavours are witnessing a major paradigm shift pointing out epigenetics as one of the guiding principles  which can help elucidate the mechanisms of these Ayurvedic principles.


Name: Dube Dheeraj Prakashchand
DOB: 08/12/1991
Designation: Post Doctoral Research Scholar at Georgia State University

I am currently serving as a Postdoctoral Researcher at Georgia State University, Atlanta. I have an abiding and a passionate interest in my area of research which can be broadly categorised as computational biophysics. I have earned my Bachelor of Technology in Mechanical engineering from IIT-BHU, Varanasi in the year 2014. Thereafter, I was enrolled in an integrated program M.Sc.+Ph.D. between the period 2014-2021 at TATA Institute of Fundamental Research, Hyderabad (TIFR-HYD). At TIFR-HYD, I completed my M.Sc. under the Physics Subject Board. I defended my Ph.D. thesis titled – “Exploring Protein Dynamics: Conformational Heterogeneity and Collective Variables” on August 4th 2021. My research interests predominantly involve Molecular Dynamics (MD) simulations which can efficiently handle and model the interactions of biological relevance. Modelling the biological systems at their molecular level details utilising the novel techniques of computer simulation I work towards deciphering the physiologically and pathologically relevant biomolecular interactions. I make use of a multiscale approach, involving all-atomistic simulations as well as coarse-grained simulations. My research techniques also require harnessing the advanced sampling techniques that can take advantage of the massively parallel computing resources by distributing multiple simulations among many processors. This helps us in discovering the minimum energy path connecting the various experimentally discovered intermediate structures. I also build markov state modelling upon the innumerable short trajectories shot from the discovered energy paths. Apart from this, I make use of unbiased MD simulations as well as enhanced sampling techniques like metadynamics, umbrella sampling and Replica exchange MD to explore the conformational space of the biomolecular system at hand. I make use of various optimization techniques and algorithms as well as machine learning techniques to analyse the MD data of large scale dynamics.