七、 問題討論
7.2 未來展望
在已知系統結構及實驗資料下估計模型使用的參數是為了將校準 模型而讓實驗結果能再現於模型上[72]，所以參數之最佳化需要對整 個系統做。但是卻沒有演算法能在有限時間內準確地解一般性的問題。
建立代謝網路的要點在能表示出反應過程的變化，所以模型中反應物 濃度的變化是觀察模型好壞的條件之一[59]。存在於生物網路間交錯 的相互關係中，有些調節關係的觸發條建是跟會濃度梯度有關，所以 將代謝網路模型盡可能地還原出實驗結果將會有助於研究系統生物 學。
往後能將代謝網路與基因調節網路或是與信號傳遞網路整合是未 來系統生物學的趨勢，實際上也已有如此研究逐漸被發表出來[2-6]，
所以我們建出的網路模型在未來將可經整合而成更複雜的系統而讓生 物系統的模擬更為完善。
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附錄
全資料來自[44]
Reference species
ΔfGo NH(j) pKa1 ΔH(I=0) pKa2 pKaMg ΔH(I=0) pKaK ΔH(I=0) G1P2- -1756.87 11 6.09 -1.7 2.48 -12G6P2- -1763.94 11 6.11 F6P2- -1760.8 11 5.89
FDP4- -2601.4 10 6.4 5.92 2.7 G3P2- -1339.25 5 6.22 -3.1 1.63 DHAP2- -1296.26 5 5.9 1.57
GAP2- -1288.6 5 6.45 13DPG4- -2356.14 4 7.5
3PG3- -1502.54 4 6.21
2PG3- -1496.38 4 7 2.45 1.18 PEP3- -1263.65 2 6.35 2.26 1.08 PYR- -472.27 3 2.49
LAC- -516.72 5 3.67 -0.33 0.98
HPi2- -1096.1 1 6.75 3 1.65 -2.9 0.5 IMP2- Not avail. 11 6.34 -2 1.67
AMP2- -1040.45 12 6.29 -3 1.92 -7.5
ADP3- -1906.13 12 6.38 -3 3.25 -15 1
ATP4- -2768.1 12 6.48 -5 4.19 -18 1.17 -1 HPCr2- Not avail. 9 4.5 2.66 1.6 8.19 0.31
HCr0 Not avail. 8 2.63 Glycogen(n) 0 0n+1 - Glycogen(n-1) 679.1 0(n-1)+1 - NAD- 0 26 - NADH2- 22.65 27 -
H+ 0 1 -