TY - JOUR
T1 - Bayesian learning of biological pathways on genomic data assimilation
AU - Yoshida, Ryo
AU - Nagasaki, Masao
AU - Yamaguchi, Rui
AU - Imoto, Seiya
AU - Miyano, Satoru
AU - Higuchi, Tomoyuki
N1 - Funding Information:
In most case, the regulatory functions f are non-linear. Then, solving (5) becomes computationally hard mainly due to the inequality constraints. Besides, when we are allowed to use totally unconstrained mode of expression for modeling, it would be hard to develop a computer program possessing the general versatility. To avoid such intractability, we employ the class of semi-convex regulatory functions, which is summarized in Figure 3, so that the optimization problem becomes easy to solve. The family includes the bilinear regulatory function used for the modeling of translation, degradation and binding of different reactants, and the indicator function which describes abrupt activation or inhibition of transcription levels of mRNAs in response to excess concentration of regulatory proteins. The Michaelis–Menten kinetic function is also supported by this class. We can take full advantage of the semi-convex regulatory functions to find the solution of (5). Here, we consider the optimization procedure as follows:
PY - 2008/11
Y1 - 2008/11
N2 - Motivation: Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision. Results: The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways.
AB - Motivation: Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision. Results: The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways.
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U2 - 10.1093/bioinformatics/btn483
DO - 10.1093/bioinformatics/btn483
M3 - Article
C2 - 18818216
AN - SCOPUS:55749101224
VL - 24
SP - 2592
EP - 2601
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 22
ER -