An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems

Verfasser / Beitragende:
[Zujian Wu, Wei Pang, George Coghill]
Ort, Verlag, Jahr:
2015
Enthalten in:
Soft Computing, 19/6(2015-06-01), 1595-1610
Format:
Artikel (online)
ID: 605468583
LEADER caa a22 4500
001 605468583
003 CHVBK
005 20210128100317.0
007 cr unu---uuuuu
008 210128e20150601xx s 000 0 eng
024 7 0 |a 10.1007/s00500-014-1467-6  |2 doi 
035 |a (NATIONALLICENCE)springer-10.1007/s00500-014-1467-6 
245 0 3 |a An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems  |h [Elektronische Daten]  |c [Zujian Wu, Wei Pang, George Coghill] 
520 3 |a Computational modelling of biochemical systems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illustrating how to generate atomic components by a set of given reactants and two user pre-defined component patterns, we propose an integrative top-down and bottom-up modelling approach for stepwise qualitative exploration of interactions among reactants in biochemical systems. Evolution strategy is applied to the top-down modelling approach to compose models, and simulated annealing is employed in the bottom-up modelling approach to explore potential interactions based on models constructed from the top-down modelling process. Both the top-down and bottom-up approaches support stepwise modular addition or subtraction for the model evolution. Experimental results indicate that our modelling approach is feasible to learn the relationships among biochemical reactants qualitatively. In addition, hidden reactants of the target biochemical system can be obtained by generating complex reactants in corresponding composed models. Moreover, qualitatively learned models with inferred reactants and alternative topologies can be used for further web-lab experimental investigations by biologists of interest, which may result in a better understanding of the system. 
540 |a The Author(s), 2014 
690 7 |a Evolution strategy  |2 nationallicence 
690 7 |a Simulated annealing  |2 nationallicence 
690 7 |a Qualitative model learning  |2 nationallicence 
690 7 |a Top-down and bottom-up modelling  |2 nationallicence 
690 7 |a Systems biology  |2 nationallicence 
700 1 |a Wu  |D Zujian  |u College of Information Science and Technology, Jinan University, 510632, Guangzhou, People's Republic of China  |4 aut 
700 1 |a Pang  |D Wei  |u School of Natural and Computing Sciences, University of Aberdeen, AB24 3UE, Aberdeen, Scotland, UK  |4 aut 
700 1 |a Coghill  |D George  |u School of Natural and Computing Sciences, University of Aberdeen, AB24 3UE, Aberdeen, Scotland, UK  |4 aut 
773 0 |t Soft Computing  |d Springer Berlin Heidelberg  |g 19/6(2015-06-01), 1595-1610  |x 1432-7643  |q 19:6<1595  |1 2015  |2 19  |o 500 
856 4 0 |u https://doi.org/10.1007/s00500-014-1467-6  |q text/html  |z Onlinezugriff via DOI 
898 |a BK010053  |b XK010053  |c XK010000 
900 7 |a Metadata rights reserved  |b Springer special CC-BY-NC licence  |2 nationallicence 
908 |D 1  |a research-article  |2 jats 
949 |B NATIONALLICENCE  |F NATIONALLICENCE  |b NL-springer 
950 |B NATIONALLICENCE  |P 856  |E 40  |u https://doi.org/10.1007/s00500-014-1467-6  |q text/html  |z Onlinezugriff via DOI 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Wu  |D Zujian  |u College of Information Science and Technology, Jinan University, 510632, Guangzhou, People's Republic of China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Pang  |D Wei  |u School of Natural and Computing Sciences, University of Aberdeen, AB24 3UE, Aberdeen, Scotland, UK  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Coghill  |D George  |u School of Natural and Computing Sciences, University of Aberdeen, AB24 3UE, Aberdeen, Scotland, UK  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t Soft Computing  |d Springer Berlin Heidelberg  |g 19/6(2015-06-01), 1595-1610  |x 1432-7643  |q 19:6<1595  |1 2015  |2 19  |o 500