Archive for the ‘Computational Biology’ Category

In a paper published in the journal Plant Physiology, scientists have demonstrated the creation of computer model of photosynthesis. A photosynthesis model developed by Farquhar et al., 1980, holds great significance in photosynthesis research (Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149: 78–90). The model is important in that it links biochemical properties of photosynthesis with in vivo photosynthetic rates. The model of Farquhar et al., 1980, is steady-state biochemical model of photosynthesis, and photosynthesis in nature is rarely at steady state. It is influenced by several environmental factors like light, temperature, oxygen, carbon dioxide and many more biotic and abiotic stresses. The concentration of carbon dioxide has changed greatly over the past 100 years. Considering these factors, dynamic models of photosynthesis have been created (Laisk A, Walker DA (1986) Control of phosphate turnover as a rate-limiting factor and possible cause of oscillations in photosynthesis: a mathematical model. Proc R Soc Lond B Biol Sci 227: 281–302; Laisk A, Walker DA (1989) A mathematical model of electron transport. Thermodynamics necessity for photosystem II regulation: ‘Light stroma’. Proc R Soc Lond B Biol Sci 237: 417–444). These models contribute to further enhance our understanding of control of many photosynthetic properties.

In this remarkable study, a model of photosynthesis has been created on the basis of how resources are partitioned between enzymes of carbon metabolism. The study asks, “Could photosynthetic rate be increased by altered partitioning of resources among the enzymes of carbon metabolism?” In creating the model, the authors addressed this question by using an “evolutionary” algorithm. The algorithm searched for many alternative partitioning strategies (resources partitioned differentially between carbon metabolism enzyme), and the ones leading to increased photosynthetic rate were chosen to build the model. The model looked for changes in concentration of each metabolite. The enzyme activities, the relative abundance of each of the proteins involved in photosynthesis, and initial metabolite concentrations were extracted from the published literature and the whole data fed into the algorithm. The researchers programmed the model to randomly alter levels of individual enzymes in the photosynthetic process. Thus every step of photosynthesis was simulated. The model after several tweaks was able to predict the outcome of experiments conducted in real leaves. The model studied the process for several generations. And after 1500 generations, photosynthesis was increased substantially. To quote from Physorg story:

Using “evolutionary algorithms,” which mimic evolution by selecting for desirable traits, the model hunted for enzymes that – if increased – would enhance plant productivity. If higher concentrations of an enzyme relative to others improved photosynthetic efficiency, the model used the results of that experiment as a parent for the next generation of tests.

The study has far-reaching implications for increasing productivity. The model would help in determining the most suitable growth conditions that would help maximize productivity, and finally, Could the model be extended to the production of better trasngenic plants?

Read the full article here.



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