Biology subjects

Kohli, A.

Publications and source records attributed to Kohli, A..

3 recordsLinked to original sources

bioRxiv2024

Potential Opportunities of Modeling Bioavailability for Monoclonal Antibodies: An Overview of mAbs and the current challenges of mAb development

With a growing market size, and a large variety of applications, monoclonal antibody technology adoption and clinical usage is at an all-time high. This review article seeks to explore 10 monoclonal antibodies (mAbs) and their mechanism of action, specifically their pharmacodynamic (PD) and pharmacokinetic (PK) properties, and use a machine learning model with various parameters to assess whether the mAb has adequate bioavailability when delivered subcutaneously. This is an investigation of drug optimization and patient outcomes when transitioning from traditional IV administrations to subcutaneous injections. The machine learning model is an extension based on a paper by Han Lou and Michael Hageman, Machine Learning Attempts for Predicting Human Subcutaneous Bioavailability of Monoclonal Antibodies, where they took 10 mAbs and analyzed 45 different features. To further extend this paper, we took an additional 10 monoclonal antibodies that were delivered subcutaneously, and took into account their dosage concentration as an extension to traditional PK properties. By including additional mAbs and dosage, a more sophisticated model can be produced with high scalability to deep learning modalities.

bioengineering↗

bioRxiv2024

Multi-omics of a rice population identifies genes and genomic regions in rice that bestow low glycemic index and high protein content

To address the growing incidences of increased diabetes and to meet the daily protein requirements, we developed low glycemic index (GI) rice varieties with protein yield exceeding 14%. In the development of recombinant inbred lines using Samba Mahsuri and IR36 amylose extender as parental lines, we identified quantitative trait loci (QTLs) and genes associated with low GI, high amylose content (AC), and high protein content (PC). By integrating genetic techniques with classification models, this comprehensive approach identified candidate genes on chromosome 2 (qGI2.1/qAC2.1 spanning the region from 18.62Mb to 19.95Mb), exerting influence on low GI and high amylose. Notably, the phenotypic variant with high value was associated with the recessive allele of the starch branching enzyme 2b (sbeIIb). The genome-edited sbeIIb line confirmed low GI phenotype in milled rice grains. Further, combinations of alleles from the highly significant SNPs from the targeted associations and epistatically interacting genes showed ultra-low GI phenotypes with high amylose and high protein. Metabolomics analysis of rice with varying AC, PC, and GI revealed that the superior lines of high AC and PC, and low GI were preferentially enriched in glycolytic and amino acid metabolism, whereas the inferior lines of low AC and PC and high GI were enriched with fatty acid metabolism. The high amylose high protein RIL (HAHP_101) was enriched in essential amino acids like lysine. Such lines may be highly relevant for food product development to address diabetes and malnutrition. Significance StatementThe increasing global incidence of diabetes calls for the development of diabetic friendly healthier rice. In this study, we developed recombinant inbred rice lines with milled rice exhibiting ultra-low to low glycemic index and high protein content from the cross between Samba Mahsuri and IR36 amylose extender. We performed comprehensive genomics and metabolomics complemented with modeling analyses emphasizing the importance of OsSbeIIb along with additional candidate genes whose variations allowed us to produce target rice lines with lower glycemic index and high protein content in a high-yielding background. These lines represent an important breeding resource to address food and nutritional security.

systems biology↗

bioRxiv2023

DeepMap: A deep learning-based model with four-line code for prediction-based breeding in crops

Prediction of phenotype through genotyping data using the emerging machine or deep learning technology has been proven successful in genomic prediction. We present here a graphical processing unit (GPU) enabled DeepMap configurable deep learning-based python package for the genomic prediction of quantitative phenotype traits. We found that deep learning captures non-linear patterns more efficiently than conventional statistical methods. Furthermore, we suggest an additional module inclusion of epistasis interactions and training of the model on Graphical Processing Units (GPUs) in addition to Central Processing Unit (CPU) to enhance efficiency and increase the models performance. We developed and demonstrated the application of DeepMap using a 3K rice genome panel and 1K-Rice Custom Amplicon (1kRiCA) data for several phenotypic traits including days to 50% flowering (DTF), number of productive tillers (NPT), panicle length (PL), plant height (PH), and plot yield (PY). We have found that DeepMap outperformed the best existing state-of-the-art models by giving higher predictive correlation and low mean squared error for the datasets studied. This prediction performance was higher than other compared models in the range of 13-31%. Similarly for Dataset-2, significantly higher predictions were observed than the compared models (16-20% higher prediction ability). On Dataset-3, we have also shown the better and versatile performance of our model across crops (wheat, maize, and soybean) for yield and yield-related traits. This demonstrates the potentiality of the framework and ease of use for future research in crop improvement. The DeepMap is accessible at https://test.pypi.org/project/DeepMap-1.0/. Short SummaryDeepMap is a deep learning-based breeder-friendly python package to perform genomic prediction. It utilizes epistatic interactions for data augmentation and outperforms the existing state-of-the-art machine/deep learning models such as Bayesian LASSO, GBLUP, DeepGS, and dualCNN. DeepMap developed for rice and tested across crops such as maize, wheat, soybean etc.

genomics↗