Dr Anyela Camargo Rodriguez

Dr Anyela Camargo Rodriguez
Computational Biologist
+44(0)1223 342463

Research interests

I am computational biologist, I use mathematical and statistical models to understand and predict the development of complex biological processes. I used this modelling approach to specifically:

  • Monitor and predict the onset of plant diseases. My papers on early disease detection reports the firsts attempts to use computer vision and AI for plant disease detection.
  • Understand and simulate the development of complex traits such as biological pathways (Senescence and Jasmonate) and plant morphology. My paper on reconstruction of Gene Regulatory Networks reports the SimGenex framework which can be used to in silico simulate both the wetlab and artificial life, from cell to organ to plant.
  • Optimise crop management to reduce carbon footprint without increasing yield gap. I use a combination mechanistic and machine learning methods to predict crop performance. My recent AquaCropR reports the R based AquaCrop model. AquaCropR is becoming more popular worldwide because is opensource and easy to use.

Research projects

KOCOLATL: A bioeconomy system to valorize cacao organic waste into valuable products; Duration: 2020-2021; Partners: NIAB (lead), BIOS, Fedecacao, Hands & Crops; Funding: Newtown Fund

Brassica Rapeseed And Vegetable Optimisation: Duration: 2017-2021; Partners: JIC (Lead), NIAB, Aberystwyth University, RRas; Funding: BB/P003095/1. 2017

Extending Knowledge Of Increased Corn Crop Productivity To Farmers: Duration: August 2017-July 2020; Partners: University of Cambridge (lead), NIAB, (PI)


Recent publications

De Vega J, Camargo A. et al. (2020). Colombia’s cyberinfrastructure for biodiversity: Building data infrastructure in emerging countries to foster socioeconomic growth. Plants, people, planet.

Méndez-Espinoza, A, Camargo A, et al. (2020). Genotypic variation in leaf and whole-plant water use efficiencies are close related in bread wheat genotypes under well-watered and water-limited conditions during grain filling. Scientific Reports.

Camargo A, et al. (2018) Functional Mapping of Quantitative Trait Loci (QTLs) Associated with Plant Performance in a Wheat MAGIC Mapping Population.  Frontiers in Plant Science

Camargo Rodriguez, A.V.; Ober, E.S. (2019). AquaCropR: Crop Growth Model for R. Agronomy 2019, 9, 378

Full publication list on Google Scholar, ResearchGate