Untitled - 中国植物生理与分子生物学学会

Systems Biology of Photosynthesis ( 光 合 作 用 系 统 生 物 学 )
Xin-Guang Zhu ( 朱 新 广 )
植 物 系 统 生 物 学 研 究 组
( 中 科 院 马 普 伙 伴 计 算 生 物 学 研 究 所 , 中 科 院 上 海 植 物 生 理 生 态 研 究 所 ;zhuxinguang@picb.ac.cn;
Introduction
http://www.picb.ac.cn/~zhuxinguang)
Photosynthesis provides the food and energy for human society. With the increased demand for
both, how can we engineer a higher photosynthetic rate? Experimental approaches to identify new
ways to engineer for higher photosynthetic rates are challenging. More than one hundred proteins
are involved in photosynthesis. Even if manipulation is limited to simply altering the amount of
protein, rather than its properties, the potential permutations will run into the millions. Considering
that the multiple proteins might need to be engineered at the same time, it quickly becomes apparent
that experimentally up-regulating or down-regulating the amount of each protein and examining its
impacts on productivity is inefficient. One potential way to solve this problem is to take a systems
approach. Briefly, the systems approach is to develop mathematical models which can faithfully
simulate all the biophysical and biochemical processes involved in photosynthesis, then, to conduct
numerical experiments to identify targets to engineer for higher productivity. Here, I use a model of
photosynthetic carbon metabolism to illustrate the basic procedure to develop a systems model and
illustrate the application of evolutionary algorithm in identifying optimal nitrogen distribution for
higher photosynthetic energy conversion efficiency. In addition, I will briefly discuss other major
applications of systems models of photosynthesis.
Systems biology approach to identify new opportunities to engineer for higher productivity
I) The basic procedure to develop a systems model
To build a systems model of a metabolic process, the reactions closely involved in this process
needs to be compiled to form a reaction diagram. Then, the rate equation for each reaction involved
in the diagram is developed.
A systems model representing this metabolic process is then
constructed by developing an ordinary differential equation (ode) for each metabolite involved in the
process. Each ode describes the rate of change of the metabolite with time, formed by the sum of the
rates of formation minus the sum of the rates of consumption of this metabolite. All odes collectively
form a system of ordinary differential equations (odes), the solution of which corresponds to
different physiological and dynamic states of the metabolic process. Once a system of ordinary
differential equations has been constructed, a series of numerical experiments are required to test and
validate the model.
Normally, a systems model has to show its ability to gain steady states,
robustness against external perturbation, and ability to predict commonly observed phenomena. For
example, the model of photosynthetic carbon metabolism reached a steady state within about 200
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seconds (Zhu et al 2007). It faithfully simulated the commonly measured A-C i curve, i.e. the CO 2
uptake versus intercellular CO 2 concentration (C ) i curve, for both normal (21%) and low (2%)
oxygen levels. Furthermore, after a drastic perturbation of the intercellular O 2 concentration, the
model can quickly regain a new steady state with a realistic rate of photosynthesis. Finally, the
model faithfully simulated the phosphate limited photosynthesis under conditions of low rate of
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