Biology subjects

Gangopadhyay, A.

Publications and source records attributed to Gangopadhyay, A..

5 recordsLinked to original sources

bioRxiv2023

A functional perspective on the conditional covariance comparison problem in dementia analysis

Although there are many methods available in the literature to compare the covariance structures of two populations, few are suitable for clinical application due to the inability to account for covariate(s) that affect the dependence structure of the variables being investigated. A common method is to adjust the effect of the covariates via a linear model and work with the resulting residuals. However, removing the effects of the covariates could potentially eliminate valuable information from the analysis. We propose a functional nonparametric covariance matrix estimator to account for any given value in the covariate(s), which allows a comparison of the functional covariance structures of the multivariate data. This comparison is facilitated via a test statistic involving the first eigenvalue of the combined form of covariance matrices of the two groups. Three different approaches, namely, the parametric Tracy-Widom, the semi-parametric Forkmans test, and the nonparametric Permutation method, are used to compute the approximate p-values of the test statistic. We have conducted extensive simulation studies to determine the type I error and power of the proposed hypothesis testing methods and developed practical recommendations for implementing this novel approach. Finally, we apply our methods to the Alzheimers Disease Neuroimaging Initiative (ADNI) study to compare cerebrospinal fluid (CSF) biomarkers between dementia and non-dementia cohorts, which offers a fascinating insight into the differences between covariance structures of biomarkers amyloid {beta}(1-42) (A{beta}42), total tau (tau), and phosphorylated tau (ptau) for given values of age, sex, and years of education.

neuroscience↗

bioRxiv2022

Analyzing the covariance structure of plasma signaling proteins in relation to the diagnosis of dementia

Numerous studies have shown that individuals with dementia have exhibited activation of inflammatory pathways in their brains. Typically, these studies use traditional and well-established regression methods for data analysis. In this paper, a new approach is introduced that utilizes the analysis of the covariance structure using methods related to the principal component analysis (PCA) theory. Eleven biomarkers related to neuroinflammation were used to determine the association with the onset of dementia. Various demographic covariates were adjusted to account for possible confounding effects of the covariance structure. Three hypothesis testing methods were considered to discern differences between partial covariance matrices for comparing power and Type I errors through simulation studies. Application of hypothesis testing methods using data from Framingham Heart Study (FHS) found significant differences in covariance matrices between the non-dementia and dementia groups.

neuroscience↗

bioRxiv2021

Ab initio modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1)

Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates cellular transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena, and plays a crucial role in maintaining normal cellular physiology, dysregulation of which has been reported towards cancerous transformation of the cells. However, despite its key role in cellular physiology, the complete structure of human TTF1 has not been elucidated to date, either experimentally or computationally. Hence, understanding the structure of human TTF1 becomes highly important for studying its functions and interactions with other cellular factors. Therefore, the aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the PDB, the complete structure of human TTF1 was constructed using ab initio modelling. The structural stability was determined using molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The representative structure of human TTF1 was obtained by trajectory clustering, and the central residues were determined by centrality analyses of the residue interaction network of TTF1. Two residue clusters, in the oligomerisation domain and C-terminal domain, were determined to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of human TTF1, and the results obtained herein will provide structural insights for future research in cancer biology and related studies. Author SummaryThe transcription termination factor 1 (TTF1) is an essential multifunctional mammalian protein which plays important role in regulating important cellular process like transcription, replication, DNA damage repair, chromatin remodelling etc. and its dysregulation leads to various cancers. Despite its being such an important factor, the complete structure of human TTF1 has not been determined to date, either using experimental techniques or computationally. Therefore, the aim of this study was to construct the complete structure of human TTF1 using computational modelling. In this study the complete structure of human TTF1 was constructed by ab initio modelling using iTasser. The stability of this model was determined by 200 ns molecular dynamics (MD) simulations. The representative conformation of human TTF1 was further determined by clustering the simulation trajectory and the residues that are central to the stability of this structure were identified. The results demonstrate the presence of two residue clusters in human TTF1, one in the oligomerisation domain and other in the C-terminal domain, which were found to be crucial for the structural stability of this protein. Hence, the results of this study will aid future studies in this field towards engineering this important protein for further biochemistry and cell biology research.

bioinformatics↗

bioRxiv2019

A Spiking Neuron and Population Model based on the Growth Transform Dynamical System

In neuromorphic engineering, neural populations are generally modeled in a bottom-up manner, where individual neuron models are connected through synapses to form large-scale spiking networks. Alternatively, a top-down approach treats the process of spike generation and neural representation of excitation in the context of minimizing some measure of network energy. However, these approaches usually define the energy functional in terms of some statistical measure of spiking activity (ex. firing rates), which does not allow independent control and optimization of neurodynamical parameters. In this paper, we introduce a new spiking neuron and population model where the dynamical and spiking responses of neurons can be derived directly from a network objective or energy functional of continuous-valued neural variables like the membrane potential. The key advantage of the model is that it allows for independent control over three neuro-dynamical properties: (a) control over the steady-state population dynamics that encodes the minimum of an exact network energy functional; (b) control over the shape of the action potentials generated by individual neurons in the network without affecting the network minimum; and (c) control over spiking statistics and transient population dynamics without affecting the network minimum or the shape of action potentials. At the core of the proposed model are different variants of Growth Transform dynamical systems that produce stable and interpretable population dynamics, irrespective of the network size and the type of neuronal connectivity (inhibitory or excitatory). In this paper, we present several examples where the proposed model has been configured to produce different types of single-neuron dynamics as well as population dynamics. In one such example, the network is shown to adapt such that it encodes the steady-state solution using a reduced number of spikes upon convergence to the optimal solution. In this paper we use this network to construct a spiking associative memory that uses fewer spikes compared to conventional architectures, while maintaining high recall accuracy at high memory loads.

neuroscience↗

bioRxiv2018

A Coupled Network of Growth Transform Neurons for Spike-Encoded Auditory Feature Extraction

This paper builds upon our previously reported growth transform based optimization framework to present a novel spiking neuron model and demonstrate its application for spike-based auditory signal processing. Unlike conventional neuromorphic approaches, the proposed Growth Transform (GT) neuron model is tightly coupled to a system objective function, which results in network dynamics that are always stable and interpretable; and the process of spike generation and population dynamics is the result of minimizing an energy functional. We then extend the model to include axonal propagation delays in a manner that the optimized solution of the system or network objective function remains unaffected. The paper characterizes the model for different types of stimuli, and explores how changing different aspects of the cost function can reproduce known single neuron dynamics. We then investigate the properties of a coupled GT neural network that can generate spike-encoded auditory features corresponding to the output of a gammatone filterbank. We show that the discriminatory information is not only encoded in the traditional spike-rates and interspike-interval statistics, but is also encoded in the subthreshold response of GT neurons for inputs that are not strong enough to elicit spikes. We also demonstrate that while different forms of coupling between the neurons could produce compact and energy-efficient representation of the auditory features, the classification performance for a speaker recognition task remains invariant across different types of coupling. Thus, we believe that the proposed GT neuron model provides a flexible neuromorphic framework to systematically design large-scale spiking neural networks with stable and interpretable dynamics.

neuroscience↗