TY - JOUR
T1 - Reverse engineering of biochemical equations from time-course data by means of genetic programming
AU - Sugimoto, Masahiro
AU - Kikuchi, Shinichi
AU - Tomita, Masaru
N1 - Funding Information:
We thank Bin Hu of the Institute for Advanced Biosciences, Keio University, for editing this manuscript. This work was supported by a grant from the Ministry of Education, Culture, Sports, Science and Technology, a grant-in-aid from the 21st Century Center of Excellence (COE) program of Keio University, Understanding and control of life's function via systems biology, a grant from the New Energy and Industrial Technology Development and Organization (NEDO) of the Ministry of Economy, Trade and Industry of Japan (Development of a Technological Infrastructure for Industrial Bioprocess Project), a grant from Leading Project for Biosimulation, Ministry of Education, Culture, Sports, Science and Technology, and a grant from the Japan Science and Technology Agency (JST).
PY - 2005/5
Y1 - 2005/5
N2 - Increased research aimed at simulating biological systems requires sophisticated parameter estimation methods. All current approaches, including genetic algorithms, need pre-existing equations to be functional. A generalized approach to predict not only parameters but also biochemical equations from only observable time-course information must be developed and a computational method to generate arbitrary equations without knowledge of biochemical reaction mechanisms must be developed. We present a technique to predict an equation using genetic programming. Our technique can search topology and numerical parameters of mathematical expression simultaneously. To improve the search ability of numeric constants, we added numeric mutation to the conventional procedure. As case studies, we predicted two equations of enzyme-catalyzed reactions regarding adenylate kinase and phosphofructokinase. Our numerical experimental results showed that our approach could obtain correct topology and parameters that were close to the originals. The mean errors between given and simulation-predicted time-courses were 1.6 × 10-5% and 2.0 × 10-3%, respectively. Our equation prediction approach can be applied to identify metabolic reactions from observable time-courses.
AB - Increased research aimed at simulating biological systems requires sophisticated parameter estimation methods. All current approaches, including genetic algorithms, need pre-existing equations to be functional. A generalized approach to predict not only parameters but also biochemical equations from only observable time-course information must be developed and a computational method to generate arbitrary equations without knowledge of biochemical reaction mechanisms must be developed. We present a technique to predict an equation using genetic programming. Our technique can search topology and numerical parameters of mathematical expression simultaneously. To improve the search ability of numeric constants, we added numeric mutation to the conventional procedure. As case studies, we predicted two equations of enzyme-catalyzed reactions regarding adenylate kinase and phosphofructokinase. Our numerical experimental results showed that our approach could obtain correct topology and parameters that were close to the originals. The mean errors between given and simulation-predicted time-courses were 1.6 × 10-5% and 2.0 × 10-3%, respectively. Our equation prediction approach can be applied to identify metabolic reactions from observable time-courses.
KW - Biochemical equation
KW - Genetic programming
KW - Reverse engineering
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U2 - 10.1016/j.biosystems.2004.11.003
DO - 10.1016/j.biosystems.2004.11.003
M3 - Article
C2 - 15823414
AN - SCOPUS:17044395470
SN - 0303-2647
VL - 80
SP - 155
EP - 164
JO - Currents in modern biology
JF - Currents in modern biology
IS - 2
ER -