Algorithms for Reconstructing and Reasoning about Chemical Reaction Networks

Recent advances in systems biology have uncovered detailed mechanisms of biological pro-cesses such as the cell cycle, circadian rhythms, and signaling pathways. These mechanisms are modeled by chemical reaction networks (CRNs) which are typically simulated by con-verting 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 sys-tematically deduce the interconnections between the species of the network? We demonstrate how this problem can be modeled as, first, one of uncovering conditional independence re-lationships 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 under-stand the motifs underlying complex dynamical behaviors of CRNs. Specifically, we focus o