Our strategy is to probe a mathematical representation of the whole photosynthetic process in silico to identify which of billions of possible changes might be the most rewarding in practice to boost the yields of key food crops. Top candidates are then tested in a single cultivate of tobacco; why tobacco? Tobacco was selected for our proof-of-concept experiments, not only for its ease of genetic transformation, but also because it is an ideal model crop that is robust in the field, forms a fully closed canopy, and produces large quantities of seed, circumventing the need for numerous seed amplification generations, further accelerating the timeline to field testing. When statistical evidence of productivity increase is achieved in tobacco, then the successful manipulations are transferred to the food crops of interest: cowpea, rice, cassava, and soybean.


We simulate photosynthesis in silico from gene to canopy to discover opportunities that could improve photosynthesis and boost yield.


Next we explore each opportunity to increase photosynthetic efficiency by engineering a model crop, which enables us to make modifications with precision and speed.


Finally, we transform our target food crops with the successful modifications to boost the yield of cowpea, cassava, rice, and soybeans.