Models for Cell-Free Synthetic Biology: Make Prototyping Easier, Better, and Faster

Cell-free TX-TL is an increasingly mature and useful platform for prototyping, testing, and engineering biological parts and systems. However, to fully accomplish the promises of synthetic biology, mathematical models are required to facilitate the design and predict the behavior of biological components in cell-free extracts. We review here the latest models accounting for transcription, translation, competition, and depletion of resources as well as genome scale models for lysate-based cell-free TX-TL systems, including their current limitations. These models will have to find ways to account for batch-to-batch variability before being quantitatively predictive in cell-free lysate-based platforms

Noise-induced oscillatory shuttling of NF-{\kappa}B in a two compartment IKK-NF-{\kappa}B-I{\kappa}B-A20 signaling model

NF-{\kappa}B is a pleiotropic protein whose nucleo-cytoplasmic trafficking is tightly regulated by multiple negative feedback loops embedded in the NF-{\kappa}B signaling network and contributes to diverse gene expression profiles important in immune cell differentiation, cell apoptosis, and innate immunity. The intracellular signaling processes and their control mechanisms, however, are susceptible to both extrinsic and intrinsic noise. In this article, we present numerical evidence for a universal dynamic behavior of NF-{\kappa}B, namely oscillatory nucleo-cytoplasmic shuttling, due to the fundamentally stochastic nature of the NF-{\kappa}B signaling network. We simulated the effect of extrinsic noise with a deterministic ODE model, using a statistical ensemble approach, generating many copies of the signaling network with different kinetic rates sampled from a biologically feasible parameter space. We modeled the effect of intrinsic noise by simulating the same networks stochastically using the Gillespie algorithm. The results demonstrate that extrinsic noise diversifies the shuttling patterns of NF-{\kappa}B response, whereas intrinsic noise induces oscillatory behavior in many of the otherwise non-oscillatory patterns. We identify two key model parameters which significantly affect the NF-{\kappa}B dynamic response and deduce a two-dimensional phase-diagram of the NF-{\kappa}B response as a function of these parameters. We conclude that if single-cell experiments are performed, a rich variety of NF-{\kappa}B response will be observed, even if population-level experiments, which average response over large numbers of cells, do not evidence oscillatory behavior.Comment: 49 pages, 12 figure

A retrosynthetic biology approach to metabolic pathway design for therapeutic production

<p>Abstract</p> <p>Background</p> <p>Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an <it>in situ </it>drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.</p> <p>Results</p> <p>In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.</p> <p>Conclusions</p> <p>We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.</p

Integrated predictive genome-scale models to improve the metabolic re-engineering efficiency

One of the most common applications of metabolic circuits is to produce a desired chemical in a chassis organism, such as the Escherichia coli (E. coli), by importing heterologous genes encoding for the enzymes that participate in the biosynthetic pathway. Recently, an automated pipeline named RetroPath was developed to synthesise embedded metabolic circuits [1]. These circuits are to be embedded in E. coli for a wide range of applications such as regulating biomass productions, sensing specifc molecules, processing specific molecules, and releasing specific molecules. In this paper, we improve the efficiency of RetroPath via quadratic programming

neo4jsbml: import systems biology markup language data into the graph database Neo4j

Systems Biology Markup Language (SBML) has emerged as a standard for representing biological models, facilitating model sharing and interoperability. It stores many types of data and complex relationships, complicating data management and analysis. Traditional database management systems struggle to effectively capture these complex networks of interactions within biological systems. Graph-oriented databases perform well in managing interactions between different entities. We present neo4jsbml, a new solution that bridges the gap between the Systems Biology Markup Language data and the Neo4j database, for storing, querying and analyzing data. The Systems Biology Markup Language organizes biological entities in a hierarchical structure, reflecting their interdependencies. The inherent graphical structure represents these hierarchical relationships, offering a natural and efficient means of navigating and exploring the model’s components. Neo4j is an excellent solution for handling this type of data. By representing entities as nodes and their relationships as edges, Cypher, Neo4j’s query language, efficiently traverses this type of graph representing complex biological networks. We have developed neo4jsbml, a Python library for importing Systems Biology Markup Language data into a Neo4j database using a user-defined schema. By leveraging Neo4j’s graphical database technology, exploration of complex biological networks becomes intuitive and information retrieval efficient. Neo4jsbml is a tool designed to import Systems Biology Markup Language data into a Neo4j database. Only the desired data is loaded into the Neo4j database. neo4jsbml is user-friendly and can become a useful new companion for visualizing and analyzing metabolic models through the Neo4j graphical database. neo4jsbml is open source software and available at https://github.com/brsynth/neo4jsbml

3-D Structural Modeling of Humic Acids through Experimental Characterization, Computer Assisted Structure Elucidation and Atomistic Simulations. 1. Chelsea Soil Humic Acid

This paper describes an integrated experimental and computational framework for developing 3-D structural models for humic acids (HAs). This approach combines experimental characterization, computer assisted structure elucidation (CASE), and atomistic simulations to generate all 3-D structural models or a representative sample of these models consistent with the analytical data and bulk thermodynamic/structural properties of HAs. To illustrate this methodology, structural data derived from elemental analysis, diffuse reflectance FT-IR spectroscopy, 1-D/2-D ^1H and ^(13)C solution NMR spectroscopy, and electrospray ionization quadrupole time-of-flight mass spectrometry (ESI QqTOF MS) are employed as input to the CASE program SIGNATURE to generate all 3-D structural models for Chelsea soil humic acid (HA). These models are subsequently used as starting 3-D structures to carry out constant temperature-constant pressure molecular dynamics simulations to estimate their bulk densities and Hildebrand solubility parameters. Surprisingly, only a few model isomers are found to exhibit molecular compositions and bulk thermodynamic properties consistent with the experimental data. The simulated ^(13)C NMR spectrum of an equimolar mixture of these model isomers compares favorably with the measured spectrum of Chelsea soil HA

The GoldenBricks assembly: A standardized one-shot cloning technique for complete cassette assembly

BBF RFC 92 proposes a new standard assembly method for the Parts Registry. The method makes one-shot cloning of a complete eukaryotic or prokaryotic cassette possible in one day while keeping compatibility with the BBF RFC 10 BioBrick assembly standard

Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling

The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to "achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life." While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling." This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to ellucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO2 are important terms in the global environmental response to anthropogenic atmospheric inputs of CO2 and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63164/1/153623102321112746.pd