garcia.bib

@Article{	  garcia2019,
  abstract	= {Diversity of cellular metabolism can be harnessed to
		  produce a large space of molecules. However, development of
		  optimal strains with high product titers, rates, and yields
		  required for industrial production is laborious and
		  expensive. To accelerate the strain engineering process, we
		  have recently introduced a modular cell design concept that
		  enables rapid generation of optimal production strains by
		  systematically assembling a modular cell with an
		  exchangeable production module(s) to produce target
		  molecules efficiently. In this study, we formulated the
		  modular cell design concept as a general multiobjective
		  optimization problem with flexible design objectives
		  derived from mass balance. We developed algorithms and an
		  associated software package, named ModCell2, to implement
		  the design. We demonstrated that ModCell2 can
		  systematically identify genetic modifications to design
		  modular cells that can couple with a variety of production
		  modules and exhibit a minimal tradeoff among modularity,
		  performance, and robustness. Analysis of the modular cell
		  designs revealed both intuitive and complex metabolic
		  architectures enabling modular production of these
		  molecules. We envision ModCell2 provides a powerful tool to
		  guide modular cell engineering and sheds light on modular
		  design principles of biological systems.},
  author	= {Garcia, Sergio and Trinh, Cong T},
  doi		= {10.1016/j.ymben.2018.09.003},
  issn		= {10967184},
  journal	= {Metabolic Engineering},
  keywords	= {Modular cell,Modular cell engineering,Modular
		  design,Modularity,Multiobjective evolutionary
		  algorithms,Multiobjective optimization,Production modules},
  title		= {{Multiobjective strain design: A framework for modular
		  cell engineering}},
  volume	= {51},
  year		= {2019}
}

@Article{	  garcia2019b,
  title		= "Modular design: Implementing proven engineering principles
		  in biotechnology",
  journal	= "Biotechnology Advances",
  year		= "2019",
  issn		= "0734-9750",
  doi		= "https://doi.org/10.1016/j.biotechadv.2019.06.002",
  url		= "http://www.sciencedirect.com/science/article/pii/S0734975019300928",
  author	= "Sergio Garcia and Cong T. Trinh",
  keywords	= "Modular design, Modularity, Modular cell, Modular cell
		  engineering, ModCell, Systems biology, Metabolic
		  engineering, Synthetic biology, Robustness, Evolvability,
		  Networks, Pareto optimality, Industrialization of biology,
		  Microbial biocatalysis",
  abstract	= "Modular design is at the foundation of contemporary
		  engineering, enabling rapid, efficient, and reproducible
		  construction and maintenance of complex systems across
		  applications. Remarkably, modularity has recently been
		  discovered as a governing principle in natural biological
		  systems from genes to proteins to complex networks within a
		  cell and organism communities. The convergent knowledge of
		  natural and engineered modular systems provides a key to
		  drive modern biotechnology to address emergent challenges
		  associated with health, food, energy, and the environment.
		  Here, we first present the theory and application of
		  modular design in traditional engineering fields. We then
		  discuss the significance and impact of modular
		  architectures on systems biology and biotechnology. Next,
		  we focus on the very recent theoretical and experimental
		  advances in modular cell engineering that seeks to enable
		  rapid and systematic development of microbial catalysts
		  capable of efficiently synthesizing a large space of useful
		  chemicals. We conclude with an outlook towards theoretical
		  and practical opportunities for a more systematic and
		  effective application of modular cell engineering in
		  biotechnology."
}

@Article{	  garcia2019c,
  author	= {Garcia, Sergio and Trinh, Cong T.},
  title		= {Comparison of Multi-Objective Evolutionary Algorithms to
		  Solve the Modular Cell Design Problem for Novel
		  Biocatalysis},
  journal	= {Processes},
  volume	= {7},
  year		= {2019},
  number	= {6},
  article-number= {361},
  url		= {https://www.mdpi.com/2227-9717/7/6/361},
  issn		= {2227-9717},
  abstract	= {A large space of chemicals with broad industrial and
		  consumer applications could be synthesized by engineered
		  microbial biocatalysts. However, the current strain
		  optimization process is prohibitively laborious and costly
		  to produce one target chemical and often requires new
		  engineering efforts to produce new molecules. To tackle
		  this challenge, modular cell design based on a chassis
		  strain that can be combined with different product
		  synthesis pathway modules has recently been proposed. This
		  approach seeks to minimize unexpected failure and avoid
		  task repetition, leading to a more robust and faster strain
		  engineering process. In our previous study, we
		  mathematically formulated the modular cell design problem
		  based on the multi-objective optimization framework. In
		  this study, we evaluated a library of state-of-the-art
		  multi-objective evolutionary algorithms (MOEAs) to identify
		  the most effective method to solve the modular cell design
		  problem. Using the best MOEA, we found better solutions for
		  modular cells compatible with many product synthesis
		  modules. Furthermore, the best performing algorithm could
		  provide better and more diverse design options that might
		  help increase the likelihood of successful experimental
		  implementation. We identified key parameter configurations
		  to overcome the difficulty associated with multi-objective
		  optimization problems with many competing design
		  objectives. Interestingly, we found that MOEA performance
		  with a real application problem, e.g., the modular strain
		  design problem, does not always correlate with artificial
		  benchmarks. Overall, MOEAs provide powerful tools to solve
		  the modular cell design problem for novel biocatalysis.},
  doi		= {10.3390/pr7060361}
}