Project properties
Title Redox cofactors: why and how many do we need?
Group Systems and Synthetic Biology
Project type thesis
Credits 36
Supervisor(s) Prof. Maria Suarez Diez (SSB), Prof. Ruud Weusthuis (BPE)
Examiner(s) Prof. Maria Suarez Diez (SSB), Prof. Ruud Weusthuis (BPE
Contact info robert1.smith@wur.nl
Begin date 2025/02/03
End date
Description In anaerobic metabolism three redox cofactors are used: ferredoxin, NAD and NADP. This is the outcome of an evolutionary process, and it probably means that this is the ideal situation, giving the ability to grow fast and outcompete strains with alternative strategies. We would like to know why.
Let us consider a network in which 10 redox couples are present. They transfer electrons to each other, from low to high redox potential. If all redox couples can transfer electrons the redox couples with higher potential (that is: every redox couple acts as a redox cofactor) we would need 9+8+7+6+5+4+3+2+1=45 specific enzymes that transfer electrons. If we make 3 of them redox cofactors the 7 remaining redox couples should be able to send electrons to and receive electrons from 1 redox cofactor (two in total). In that case we would need 6x2 + 2x1 = 14 specific enzymes. If we also consider electron transfer between the cofactors, the number of enzymes required is only 16. If we scale up to a more realistic number of redox couples of 200, the first case would require 20100 enzymes and the second only 402 enzymes. All these enzymes have to find a place in a cell. In the first case the enzymes are 20100/402=50 times more diluted, resulting in a 50 times lower growth rate. This illustrates the advantages of specialized redox couples that act as redox cofactors.
The question is why in nature we find ferredoxin, NAD and NADP that have a certain redox potential?
We think energy conservation might be very relevant here. Energy can be conserved if the redox potential difference between the redox couples is just high enough to contribute to the formation of a proton motive force or substrate phosphorylation. If the difference in redox potential is too large or too small energy will be dissipated into heat. The electron flux between the redox cofactors is much higher than in the other reactions. So it makes sense to use these steps to conserve energy. The redox potential difference between the redox cofactors should therefore by optimized to realize energy conservation. Higher energy conservation will lead to higher growth yield.

We want to model the evolution of the formation of redox cofactors based on these principles. What would such a model look like? Our initial idea is to develop a model containing 200 redox couples with a randomly assigned redox potential between -400 mV and 0 mV (about right for anaerobic conditions). We start with the situation in which all redox couples can act as a redox cofactor and calculate growth rate and growth yield. Then we follow an optimization process to reduce the number of connections while maintaining (or improving) rate and yield. We would like to know how many of the final outcomes of such optimizations suggest three redox cofactors.
This simple model can be further expanded, for instance by including bifurcation reactions, aerobic growth conditions, or other non-redox reactions in the model. After all, life is more complicated than described above.
This MSc thesis topic will help us understand how life works and will give us new clues how we can improve microbial cell factories. We are looking for an MSc student interested in this topic and with the ability to write code
The student working in this project will:
- Develop a suitable modelling framework to represent rates, yield and redox cofactors based on the described ideas.
- Deploy optimization algorithms to minimize the number of required redox cofactors.
- Evaluate the output of the optimization algorithms and compare it with evolutionary trajectories of microbes.







Used skills Metabolic modelling, optimization routines, analysis or redox cofactors, thermodynamics.
Requirements Programming (python) and modelling metabolism (as explained in “MEIM: Metabolic engineering of industrial microorganisms BPE34306”.