TY - GEN
T1 - Representing and reasoning about signal networks
T2 - 2nd International IEEE Computer Society Computational Systems Bioinformatics Conference, CSB 2003
AU - Baral, C.
AU - Chancellor, K.
AU - Tran, N.
AU - Tran, N.
N1 - Publisher Copyright:
© 2003 IEEE.
PY - 2003
Y1 - 2003
N2 - We propose a formal language to represent and reason about signal transduction networks. The existing approaches such as ones based on Petri nets, and π-calculus fall short in many ways and our work suggests that an artificial intelligence (AI) based approach may be well suited for many aspects. We apply a form of action language to represent and reason about NFκB dependent signaling pathways. Our language supports several essential features of reasoning with signal transduction knowledge, such as: reasoning with partial (or incomplete) knowledge, and reasoning about triggered evolutions of the world and elaboration tolerance. Because of its growing important role in cellular functions, we select NFκB dependent signaling to be our test bed. NFκB is a central mediator of the immune response, and it can regulate stress responses, as well as cell death/survival in several cell types. While many extracellular signals may lead to the activation of NFκB, few related pathways are elucidated. We study the tasks of representation of pathways, reasoning with pathways, explaining observations, and planning to alter the outcomes; and show that all of them can be well formulated in our framework. Thus our work shows that our AI based approach is a good candidate for feasible and practical representation of and reasoning about signal networks.
AB - We propose a formal language to represent and reason about signal transduction networks. The existing approaches such as ones based on Petri nets, and π-calculus fall short in many ways and our work suggests that an artificial intelligence (AI) based approach may be well suited for many aspects. We apply a form of action language to represent and reason about NFκB dependent signaling pathways. Our language supports several essential features of reasoning with signal transduction knowledge, such as: reasoning with partial (or incomplete) knowledge, and reasoning about triggered evolutions of the world and elaboration tolerance. Because of its growing important role in cellular functions, we select NFκB dependent signaling to be our test bed. NFκB is a central mediator of the immune response, and it can regulate stress responses, as well as cell death/survival in several cell types. While many extracellular signals may lead to the activation of NFκB, few related pathways are elucidated. We study the tasks of representation of pathways, reasoning with pathways, explaining observations, and planning to alter the outcomes; and show that all of them can be well formulated in our framework. Thus our work shows that our AI based approach is a good candidate for feasible and practical representation of and reasoning about signal networks.
UR - http://www.scopus.com/inward/record.url?scp=84864267606&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864267606&partnerID=8YFLogxK
U2 - 10.1109/CSB.2003.1227427
DO - 10.1109/CSB.2003.1227427
M3 - Conference contribution
AN - SCOPUS:84864267606
T3 - Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003
SP - 623
EP - 628
BT - Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 August 2003 through 14 August 2003
ER -