Computational Methods for Pathway Synthesis and Strain Optimization.
and optimization of biological cells have been central to modern biotechnology, with
applications ranging from drug discovery and development to production of commercially
significant chemicals. Purely empirical approaches can benefit greatly when paired with
computer-aided design methods that allow for design space exploration and optimization.
Such methods may signific... read moreantly contribute to reducing experimental efforts and
expediting discoveries. This thesis addresses two problems in metabolic engineering and
synthetic biology. The first problem concerns the construction of synthetic pathways to
produce a desired metabolite within a microbial cell. We present two approaches for
solving this problem. The first approach, ProbPath, identifies non-native de novo
synthesis pathways from reactions within a database by probabilistically sampling
available reactions. ProbPath is shown effective in identifying synthesis pathways when
compared to exhaustive exploration of the design space with limited path length in terms
of generating similar yield profiles. Additionally, we were able with ProbPath to
reproduce routes that were experimentally obtained for the production of several
molecules. The second approach addresses the issue when a desired target metabolite is
not present in known databases. To produce a synthesis pathway or such a metabolite, we
develop a novel methodology based on identifying structural similarities between the
target metabolite and existing metabolites within the database and developing
transformation operators that predict the transformation outcome when applied to the
target metabolite. To study this approach, we developed an algorithm, PROXIMAL, to
construct transformation operators based on the set of xenobiotic transformations
associated with human liver enzymes. We evaluated the prediction accuracy of PROXIMAL
through case studies on two environmental chemicals. Comparisons with published reports
confirm that our predictions have been experimentally validated in the literature. The
second problem addressed in this thesis concerns identifying optimal gene modifications
when tuning a microbial cell to maximize the production of a desired compound. The
novelty in our problem formulation lies in explicitly accounting for likely variations
in flux capacities due to engineering modifications. The thesis presents a computational
framework, CCOpt, which identifies an optimal set of gene modifications. CCOpt is based
on chance-constrained programming, where constraints are probabilistically met at a
user-specified confidence level. Evaluation of the approach demonstrates that CCOpt
consistently finds a solution most-frequently found when using Monte Carlo sampling, but
at a fraction of a computational cost. The CCOpt formulation is the first work to
incorporate uncertainties when computing gene modifications. Overall, the thesis
contributes to and advances the state-of-the-art in design automation tools for
metabolic engineering and synthetic biology.
Thesis (Ph.D.)--Tufts University, 2015.
Submitted to the Dept. of Computer Science.
Advisors: Soha Hassoun, and Kyongbum Lee.
Committee: Roni Khardon, Kathleen Fisher, and Keith Tyo.
Keywords: Computer science, and Bioinformatics.read less