Biology subjects

Nayak, P.

Publications and source records attributed to Nayak, P..

5 recordsLinked to original sources

bioRxiv2025

Optimizing Oyster Breeding with Machine Learning and BigData for Superior Quality

1.Oyster aquaculture is a vital component of global marine ecosystems and food production, yet winter mortality events threaten both ecological stability and economic viability. Traditional selective breeding methods, reliant on phenotypic traits and slow generational cycles, struggle to address these challenges efficiently. This study introduces an innovative approach integrating machine learning with multi-omics data genomics, transcriptomics, and proteomics to optimize oyster breeding for resilience and quality. By analyzing high-resolution datasets encompassing genetic markers, environmental stressors, and survival metrics, our ML models identified key SNPs linked to cold tolerance and disease resistance. Marker-assisted selection (MAS) accelerated breeding cycles, while predictive algorithms achieved 92.4% accuracy in forecasting survival and growth traits. Controlled trials demonstrated a 30% reduction in winter mortality and a 25% improvement in growth rates among ML-selected oyster lineages compared to traditional methods. Additionally, a smartphone-based diagnostic tool was developed to enable real-time monitoring of oyster health, empowering farmers to adapt feeding and environmental strategies dynamically. This research bridges the gap between conventional aquaculture and computational innovation, offering a scalable framework to enhance genetic diversity, sustainability, and yield. By replacing trial-and-error practices with data-driven precision, our approach not only mitigates immediate industry challenges but also establishes a pathway for climate-resilient aquaculture. The fusion of ML with multi-omics technologies marks a transformative shift, enabling breeders to make rapid, evidence-based decisions that harmonize ecological stewardship with commercial demands.

bioinformatics↗

bioRxiv2025

Modelling Actin-MT Crosstalk in Migrating Cells

Actin-Microtubule crosstalk regulates the polarity and morphology of migrating cells and encompasses mechanical interactions, mediated by crosslinkers, molecular motors, and cytoskeletal regulators. Recent experiments indicate that local microtubule depolymerization promotes local actomyosin retraction, whereas local microtubule polymerization promotes local actin-polymerization. Based on these observations, we develop a computational whole-cell model involving dynamic microtubules interacting mechanically and chemically with an active cell boundary. Specifically, the tips of microtubules send signals for local expansion or contraction to the active cell boundary, depending on whether they are in the growth or shrink phase. A rich, self-organized, dynamic behavior emerges, characterized by the repositioning of the microtubule-organizing center relative to the nucleus and the direction of migration. This also includes a variety of migration patterns, cell morphologies, and complex responses to obstacles in microfluidic and obstacle park environments. We demonstrate that microtubule length and cell boundary stiffness have a significant impact on these features, highlighting the need for new experimental investigations. Thus, the model provides a unified framework that explains a wide range of experimental observations and setups where actin-microtubule crosstalk plays a crucial role.

biophysics↗

bioRxiv2023

Microtubule search-and-capture model evaluates the effect of chromosomal volume conservation on spindle assembly during mitosis

Variation in the chromosome numbers can arise from the erroneous mitosis or fusion and fission of chromosomes. While the mitotic errors lead to an increase or decrease in the overall chromosomal substance in the daughter cells, fission and fusion keep this conserved. Variations in chromosome numbers are assumed to be a crucial driver of speciation. For example, the members of the muntjac species are known to have very different karyotypes with the chromosome numbers varying from 2n = 70 + 3B in the brown brocket deer to 2n = 46 in the Chinese muntjac and 2n = 6/7 in the Indian muntjac. The chromosomal content in the nucleus of these closely related mammals is roughly the same and various chromosome fusion and fission pathways have been suggested as the evolution process of these karyotypes. Similar trends can also be found in lepidoptera and yeast species which show a wide variation of chromosome numbers. The effect of chromosome number variation on the spindle assembly time and accuracy is still not properly addressed. We computationally investigate the effect of conservation of the total chromosomal substance on the spindle assembly during prometaphase. Our results suggest that chromosomal fusion pathways aid the microtubule-driven Search and Capture of the kinetochore in cells with monocentric chromosomes. We further report a comparative analysis of the site and percentage of amphitelic captures, dependence on cell shape, position of the kinetochore in respect of chromosomal volume partitioning.

biophysics↗

bioRxiv2022

Transcriptome profiling of tendon fibroblasts at the onset of embryonic muscle contraction reveals novel force-responsive genes

Mechanical forces play a critical role in tendon development and function, influencing cell behavior through mechanotransduction signaling pathways and subsequent extracellular matrix (ECM) remodeling. Here we investigate the molecular mechanisms by which tenocytes in developing zebrafish embryos respond to muscle contraction forces during the onset of swimming and cranial muscle activity. Using genome-wide bulk RNA sequencing of FAC-sorted tenocytes we identify novel tenocyte markers and genes involved in tendon mechanotransduction. Embryonic tendons show dramatic changes in expression of Matrix Remodeling Associated 5b (mxra5b), Matrilin 1 (matn1), and the transcription factor Kruppel-like factor 2a (klf2a), as muscles start to contract. Using embryos paralyzed either by loss of muscle contractility or neuromuscular stimulation we confirm that muscle contractile forces influence the spatial and temporal expression patterns of all three genes. Quantification of these gene expression changes across tenocytes at multiple tendon entheses and myotendinous junctions reveals that their responses depend on force intensity, duration and tissue stiffness. These force-dependent feedback mechanisms in tendons, particularly in the ECM, have important implications for improved treatments of tendon injuries and atrophy.

developmental biology↗

bioRxiv2020

Quantitative comparison of Drosophila behavior annotations by human observers and a machine learning algorithm

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.Significance Statement Accurate quantification of animal behaviors is fundamental to neuroscience. Here, we quantitatively assess how human choices influence the performance of automated classifiers trained by a machine-learning algorithm. We found that human decisions about the computational tracking method, the training images, and the images used for performance evaluation impact both the classifier outputs and how human observers interpret the results. These factors are sometimes overlooked but are critical, especially because animal behavior is itself inherently variable. Automated quantification of animal behavior is becoming increasingly prevalent: our results provide a model for bridging the gap between traditional human annotations and computer-based annotations. Systematic assessment of human choices is important for developing behavior classifiers that perform robustly in a variety of experimental conditions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

animal behavior and cognition↗