Algorithms for Reconstructing and Reasoning about Chemical Reaction Networks

Recent advances in systems biology have uncovered detailed mechanisms of biological processes such as the cell cycle, circadian rhythms, and signaling pathways. These mechanisms are modeled by chemical reaction networks (CRNs) which are typically simulated by converting to ordinary differential equations (ODEs), so that the goal is to closely reproduce the observed quantitative and qualitative behaviors of the modeled process. This thesis proposes two algorithmic problems related to the construction and comprehension of CRN models. The first problem focuses on reconstructing CRNs from given time series. Given multivariate time course data obtained by perturbing a given CRN, how can we systematically deduce the interconnections between the species of the network? We demonstrate how this problem can be modeled as, first, one of uncovering conditional independence relationships using buffering experiments and, second, of determining the properties of the individual chemical reactions. Experimental results demonstrate the effectiveness of our approach on both synthetic and real CRNs. The second problem this work focuses on is to aid in network comprehension, i.e., to understand the motifs underlying complex dynamical behaviors of CRNs. Specifically, we focus on bistability---an important dynamical property of a CRN---and propose algorithms to identify the core structures responsible for conferring bistability. The approach we take is to systematically infer the instability causing structures (ICSs) of a CRN and use machine learning techniques to relate properties of the CRN to the presence of such ICSs. This work has the potential to aid in not just network comprehension but also model simplification, by helping reduce the complexity of known bistable systems.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2013

Author dc:creator

• Cho, Yong Ju

Chair dc:contributor.committeechair

• Ramakrishnan, Naren

Committee members dc:contributor.committeemember

• Vullikanti, Anil Kumar S.
• Murali, T. M.
• Bevan, David R.
• Cao, Yang

• Chemical reaction networks
• bistability
• data mining
• time series modeling

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:270

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/19244