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Our research integrates advanced computational and mathematical modeling with biological and agricultural applications, leveraging Artificial Intelligence to address pressing challenges in these fields.
Hyperspectral Imaging (HSI), capturing information across hundreds of spectral bands, has revolutionized data analysis in agriculture. Our research focuses on leveraging HSI for agricultural advancements in Colombia. We conduct comprehensive comparative studies on various target detection algorithms applied to hyperspectral imagery, aiming to enhance crop monitoring and management. By evaluating these algorithms in diverse agricultural scenarios, our work contributes to optimizing crop health and yield, particularly in Colombian ecosystems.
The outcome for many cancer patients has substantially improved over the last two decades with the introduction of drugs targeting kinases and other proteins in signaling pathways disrupted in tumor cells. However, almost all tumors eventually develop resistance to these drugs, leading to cancer recurrence.
Our Contribution (Nature, 2017): We developed and applied computational methods to uncover how cancer cells coordinate drug resistance through gene regulatory networks. Using the φ-mixing coefficient algorithm on single-cell data, we discovered that just 4 master regulator genes orchestrate the entire resistance program across 19 genes.
→ Read Full Details of Our Cancer Research Discovery
How exponentially growing cells maintain size homeostasis is a fundamental question. Recent single-cell studies in various organisms have uncovered new mechanisms controlling cell size, despite inherent stochastic behavior. Through statistical analysis of thousands of cell-cycle measurements, we developed a stochastic processes framework. This framework suggests possible molecular mechanisms relevant to not only size homeostasis but also key components in fundamental cell processes.
Transmission of HIV occurs via two mechanisms: the free virus pathway and cell-cell transmission, where infected cells form virological synapses with uninfected cells. HIV replication involves a positive feedback loop controlled by the viral protein Tat, potentially acting as a stochastic switch in determining cell latency. We study a mathematical model of HIV replication incorporating both transmission pathways. Our model demonstrates that the high multiplicity of infection in cell-cell transmission suppresses latent infection, providing an evolutionary benefit to the virus.
Artificial Intelligence (AI) represents a complex computational model, trained with vast amounts of data, capable of making predictions and uncovering patterns in biological and agricultural research. Our work harnesses AI to complement and enhance traditional mathematical and computational models. By integrating AI, we can analyze large datasets more efficiently, providing deeper insights and more accurate predictions. This synergy between AI and traditional modeling techniques allows us to tackle complex problems in biology and agriculture with unprecedented precision and scale. Our research demonstrates how AI can be seamlessly integrated into various studies, from improving crop yields through advanced imaging techniques to understanding disease resistance mechanisms and cellular processes at a molecular level.