Network-based approach for stress-responsive genes identification in plants:

The seminar addresses the use of computational networks to identify stress-responsive genes in plants—a key challenge for food security and sustainability. Through the Institute for Omics Sciences Research, the study focuses on crops such as rice and sugarcane, which are essential to Colombian agriculture and vulnerable to adverse conditions like drought and salinity. To understand how plants respond to these stressors, the research analyzes gene expression using transcriptomic and phenotypic data, comparing patterns under normal and stress conditions. The methodology is based on network analysis, allowing the identification of gene groups that interact with each other, rather than evaluating genes in isolation. Statistical techniques such as Pearson correlation and Lasso regression are used to construct networks that reflect gene interactions and detect functional modules relevant to stress response. This approach provides a more integrated and structured view of the biological mechanisms that enable plants to adapt to hostile environments. Case study results computationally validated that the selected genes play biologically significant roles in stress response. This research represents progress in identifying key genes that could enhance crop resilience, thereby contributing to improved agricultural productivity and food security.