225 research outputs found
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
Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms
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
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Shotgun protein sequencing.
A novel experimental and computational technique based on multiple enzymatic digestion of a protein or protein mixture that reconstructs protein sequences from sequences of overlapping peptides is described in this SAND report. This approach, analogous to shotgun sequencing of DNA, is to be used to sequence alternative spliced proteins, to identify post-translational modifications, and to sequence genetically engineered proteins
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
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Enumerating molecules.
This report is a comprehensive review of the field of molecular enumeration from early isomer counting theories to evolutionary algorithms that design molecules in silico. The core of the review is a detail account on how molecules are counted, enumerated, and sampled. The practical applications of molecular enumeration are also reviewed for chemical information, structure elucidation, molecular design, and combinatorial library design purposes. This review is to appear as a chapter in Reviews in Computational Chemistry volume 21 edited by Kenny B. Lipkowitz
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Developing algorithms for predicting protein-protein interactions of homology modeled proteins.
The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms
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
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