Dokyun Na

Systems metabolic engineering of microorganisms for natural and non-natural chemicals

Growing concerns over limited fossil resources and associated environmental problems are motivating the development of sustainable processes for the production of chemicals, fuels and materials from renewable resources. Metabolic engineering is a key enabling technology for transforming microorganisms into efficient cell factories for these compounds. Systems metabolic engineering, which incorporates the concepts and techniques of systems biology, synthetic biology and evolutionary engineering at the systems level, offers a conceptual and technological framework to speed the creation of new metabolic enzymes and pathways or the modification of existing pathways for the optimal production of desired products. Here we discuss the general strategies of systems metabolic engineering and examples of its application and offer insights as to when and how each of the different strategies should be used. Finally, we highlight the limitations and challenges to be overcome for the systems metabolic engineering of microorganisms at more advanced levels.

Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs

Small regulatory RNAs (sRNAs) regulate gene expression in bacteria. We designed synthetic sRNAs to identify and modulate the expression of target genes for metabolic engineering in Escherichia coli. Using synthetic sRNAs for the combinatorial knockdown of four candidate genes in 14 different strains, we isolated an engineered E. coli strain (tyrR- and csrA-repressed S17-1) capable of producing 2 g per liter of tyrosine. Using a library of 130 synthetic sRNAs, we also identified chromosomal gene targets that enabled substantial increases in cadaverine production. Repression of murE led to a 55% increase in cadaverine production compared to the reported engineered strain (XQ56 harboring the plasmid p15CadA). The design principles and the engineering strategy using synthetic sRNAs reported here are generalizable to other bacteria and applicable in developing superior producer strains. The ability to fine-tune target genes with designed sRNAs provides substantial advantages over gene-knockout strategies and other large-scale target identification strategies owing to its easy implementation, ability to modulate chromosomal gene expression without modifying those genes and because it does not require construction of strain libraries.

Construction and optimization of synthetic pathways in metabolic engineering

Metabolic engineering has enabled us to develop strains suitable for their use as microbial factories of chemicals and materials from renewable sources. It has recently become more powerful with the advanced in synthetic biology, which is allowing us to create novel and fine-controlled metabolic and regulatory circuits maximizing metabolic fluxes to the desired products in the strain being developed. This enables us to engineer host microorganisms to enhance their innate metabolic capabilities or to gain new capabilities in the production of target compounds. Here we review recently constructed synthetic pathways that have been successfully applied for producing non-innate chemicals and also discuss recent approaches developed to increase the efficiency of synthetic pathways for achieving higher productivities of desired bioproducts.

RBSDesigner: software for designing synthetic ribosome binding sites that yields a desired level of protein expression

MOTIVATION RBSDesigner predicts the translation efficiency of existing mRNA sequences and designs synthetic ribosome binding sites (RBSs) for a given coding sequence (CDS) to yield a desired level of protein expression. The program implements the mathematical model for translation initiation described in Na et al. (Mathematical modeling of translation initiation for the estimation of its efficiency to computationally design mRNA sequences with a desired expression level in prokaryotes. BMC Syst. Biol., 4, 71). The program additionally incorporates the effect on translation efficiency of the spacer length between a Shine-Dalgarno (SD) sequence and an AUG codon, which is crucial for the incorporation of fMet-tRNA into the ribosome. RBSDesigner provides a graphical user interface (GUI) for the convenient design of synthetic RBSs. AVAILABILITY RBSDesigner is written in Python and Microsoft Visual Basic 6.0 and is publicly available as precompiled stand-alone software on the web (http://rbs.kaist.ac.kr). CONTACT dhlee@kaist.ac.kr

Design and use of synthetic regulatory small RNAs to control gene expression in Escherichia coli

Gene knockout experiments are often essential in functional genomics and metabolic engineering studies. However, repeated multiple gene knockout experiments are laborious, time consuming and sometimes impossible to perform for those genes that are essential for cell function. Small regulatory RNAs (sRNAs) are short noncoding RNAs in prokaryotes that can finely control the expression of target genes in trans at the post-transcriptional level. Here we describe the protocol for synthetic sRNA-based gene expression control, including sRNA design principles. Customized synthetic sRNAs consist of a scaffold and a target-binding sequence, and they can be created by simply replacing an existing target-binding sequence with one that is complementary to the target mRNA to be repressed, while retaining the scaffold. Our plasmid-based synthetic sRNA system does not require chromosomal modifications, and it enables one to perform high-throughput studies on the effects of knockdowns on host cell physiology, and it further allows the simultaneous screening of target genes in different Escherichia coli strains for applications in metabolic engineering and synthetic biology. Once an sRNA scaffold-harboring plasmid is constructed, customized synthetic sRNAs can be made within 3–4 d; after this time, the synthetic sRNAs can be applied to the desired experiments.

Mathematical modeling of translation initiation for the estimation of its efficiency to computationally design mRNA sequences with desired expression levels in prokaryotes

BackgroundWithin the emerging field of synthetic biology, engineering paradigms have recently been used to design biological systems with novel functionalities. One of the essential challenges hampering the construction of such systems is the need to precisely optimize protein expression levels for robust operation. However, it is difficult to design mRNA sequences for expression at targeted protein levels, since even a few nucleotide modifications around the start codon may alter translational efficiency and dramatically (up to 250-fold) change protein expression. Previous studies have used ad hoc approaches (e.g., random mutagenesis) to obtain the desired translational efficiencies for mRNA sequences. Hence, the development of a mathematical methodology capable of estimating translational efficiency would greatly facilitate the future design of mRNA sequences aimed at yielding desired protein expression levels.ResultsWe herein propose a mathematical model that focuses on translation initiation, which is the rate-limiting step in translation. The model uses mRNA-folding dynamics and ribosome-binding dynamics to estimate translational efficiencies solely from mRNA sequence information. We confirmed the feasibility of our model using previously reported expression data on the MS2 coat protein. For further confirmation, we used our model to design 22 luxR mRNA sequences predicted to have diverse translation efficiencies ranging from 10-5 to 1. The expression levels of these sequences were measured in Escherichia coli and found to be highly correlated (R2= 0.87) with their estimated translational efficiencies. Moreover, we used our computational method to successfully transform a low-expressing DsRed2 mRNA sequence into a high-expressing mRNA sequence by maximizing its translational efficiency through the modification of only eight nucleotides upstream of the start codon.ConclusionsWe herein describe a mathematical model that uses mRNA sequence information to estimate translational efficiency. This model could be used to design best-fit mRNA sequences having a desired protein expression level, thereby facilitating protein over-production in biotechnology or the protein expression-level optimization necessary for the construction of robust networks in synthetic biology.

PLPD: reliable protein localization prediction from imbalanced and overlapped datasets

Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10 localizations in a cell), it is a multi-label dataset (a protein may occur in several different subcellular locations), and it is too imbalanced (the number of proteins in each localization is remarkably different). Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackles effectively these characteristics at the same time. Thus, a new computational method for protein localization is eventually needed for more reliable outcomes. To address the issue, we present a protein localization predictor based on D-SVDD (PLPD) for the prediction of protein localization, which can find the likelihood of a specific localization of a protein more easily and more correctly. Moreover, we introduce three measurements for the more precise evaluation of a protein localization predictor. As the results of various datasets which are made from the experiments of Huh et al. (2003), the proposed PLPD method represents a different approach that might play a complimentary role to the existing methods, such as Nearest Neighbor method and discriminate covariant method. Finally, after finding a good boundary for each localization using the 5184 classified proteins as training data, we predicted 138 proteins whose subcellular localizations could not be clearly observed by the experiments of Huh et al. (2003).

On the Importance of Polar Interactions for Complexes Containing Intrinsically Disordered Proteins

There is a growing recognition for the importance of proteins with large intrinsically disordered (ID) segments in cell signaling and regulation. ID segments in these proteins often harbor regions that mediate molecular recognition. Coupled folding and binding of the recognition regions has been proposed to confer high specificity to interactions involving ID segments. However, researchers recently questioned the origin of the interaction specificity of ID proteins because of the overrepresentation of hydrophobic residues in their interaction interfaces. Here, we focused on the role of polar and charged residues in interactions mediated by ID segments. Making use of the extended nature of most ID segments when in complex with globular proteins, we first identified large numbers of complexes between globular proteins and ID segments by using radius-of-gyration-based selection criteria. Consistent with previous studies, we found the interfaces of these complexes to be enriched in hydrophobic residues, and that these residues contribute significantly to the stability of the interaction interface. However, our analyses also show that polar interactions play a larger role in these complexes than in structured protein complexes. Computational alanine scanning and salt-bridge analysis indicate that interfaces in ID complexes are highly complementary with respect to electrostatics, more so than interfaces of globular proteins. Follow-up calculations of the electrostatic contributions to the free energy of binding uncovered significantly stronger Coulombic interactions in complexes harbouring ID segments than in structured protein complexes. However, they are counter-balanced by even higher polar-desolvation penalties. We propose that polar interactions are a key contributing factor to the observed high specificity of ID segment-mediated interactions.

Fuzzy continuous petri net-based approach for modeling immune systems

The immune system has unique defense mechanisms such as innate, humoral and cellular immunity. These mechanisms are closely related to prevent pathogens from spreading in the host and to clear them effectively. To get a comprehensive understanding of the immune system, it is necessary to integrate the knowledge through modeling. Many immune models have been developed based on differential equations and cellular automata. One of the most difficult problem in modeling the immune system is to find or estimate appropriate kinetic parameters. However, it is relatively easy to get qualitative or linguistic knowledge. To incorporate such knowledge, we present a novel approach, fuzzy continuous Petri nets. A fuzzy continuous Petri net has capability of fuzzy inference by adding new types of places and transitions to continuous Petri nets. The new types of places and transitions are called fuzzy places and fuzzy transitions, which act as kinetic parameters and fuzzy inference systems between input places and output places. The approach is applied to model helper T cell differentiation, which is a critical event in determining the direction of the immune response.

NeuroGeM, a knowledgebase of genetic modifiers in neurodegenerative diseases

BackgroundNeurodegenerative diseases (NDs) are characterized by the progressive loss of neurons in the human brain. Although the majority of NDs are sporadic, evidence is accumulating that they have a strong genetic component. Therefore, significant efforts have been made in recent years to not only identify disease-causing genes but also genes that modify the severity of NDs, so-called genetic modifiers. To date there exists no compendium that lists and cross-links genetic modifiers of different NDs.DescriptionIn order to address this need, we present NeuroGeM, the first comprehensive knowledgebase providing integrated information on genetic modifiers of nine different NDs in the model organisms D. melanogaster, C. elegans, and S. cerevisiae. NeuroGeM cross-links curated genetic modifier information from the different NDs and provides details on experimental conditions used for modifier identification, functional annotations, links to homologous proteins and color-coded protein-protein interaction networks to visualize modifier interactions. We demonstrate how this database can be used to generate new understanding through meta-analysis. For instance, we reveal that the Drosophila genes DnaJ-1, thread, Atx2, and mub are generic modifiers that affect multiple if not all NDs.ConclusionAs the first compendium of genetic modifiers, NeuroGeM will assist experimental and computational scientists in their search for the pathophysiological mechanisms underlying NDs. http://chibi.ubc.ca/neurogem.