Over the past decade, a growing community of researchers has emerged around the use of COnstraint-Based Reconstruction and Analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a significant update of this in silico ToolBox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include: (1) network gap filling, (2) 13C analysis, (3) metabolic engineering, (4) omics-guided analysis, and (5) visualization. As with the first version, the COBRA Toolbox reads and writes Systems Biology Markup Language formatted models. In version 2.0, we improved performance, usability, and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the Toolbox and validate results. This Toolbox lowers the barrier of entry to use powerful COBRA methods.

Biological systems are inherently hierarchal and multiscale in time and space. A major challenge of systems biology is to describe biological systems as a computational model, which can be used to derive novel hypothesis and drive experiments leading to new knowledge. The constraint-based reconstruction and analysis approach has been successfully applied to metabolism and to the macromolecular synthesis machinery assembly. Here, we present the first integrated stoichiometric multiscale model of metabolism and macromolecular synthesis for Escherichia coli K12 MG1655, which describes the sequence-specific synthesis and function of almost 2000 gene products at molecular detail. We added linear constraints, which couple enzyme synthesis and catalysis reactions. Comparison with experimental data showed improvement of growth phenotype prediction with the multiscale model over E. coli’s metabolic model alone. Many of the genes covered by this integrated model are well conserved across enterobacters and other, less related bacteria. We addressed the question of whether the bias in synonymous codon usage could affect the growth phenotype and environmental niches that an organism can occupy. We created two classes of in silico strains, one with more biased codon usage and one with more equilibrated codon usage than the wildtype. The reduced growth phenotype in biased strains was caused by tRNA supply shortage, indicating that expansion of tRNA gene content or tRNA codon recognition allow E. coli to respond to changes in codon usage bias. Our analysis suggests that in order to maximize growth and to adapt to new environmental niches, codon usage and tRNA content must co-evolve. These results provide further evidence for the mutation-selection-drift balance theory of codon usage bias. This integrated multiscale reconstruction successfully demonstrates that the constraint-based modeling approach is well suited to whole-cell modeling endeavors.

Human neural stem/progenitor cells (hNSPCs) are good candidates for treating central nervous system (CNS) trauma since they secrete beneficial trophic factors and differentiate into mature CNS cells; however, many cells die after transplantation. This cell death can be ameliorated by inclusion of a biomaterial scaffold, making identification of optimal scaffolds for hNSPCs a critical research focus. We investigated the properties of fibrin-based scaffolds and their effects on hNSPCs and found that fibrin generated from salmon fibrinogen and thrombin stimulates greater hNSPC proliferation than mammalian fibrin. Fibrin scaffolds degrade over the course of a few days in vivo, so we sought to develop a novel scaffold that would retain the beneficial properties of fibrin but degrade more slowly to provide longer support for hNSPCs. We found combination scaffolds of salmon fibrin with interpenetrating networks (IPNs) of hyaluronic acid (HA) with and without laminin polymerize more effectively than fibrin alone and generate compliant hydrogels matching the physical properties of brain tissue. Furthermore, combination scaffolds support hNSPC proliferation and differentiation while significantly attenuating the cell-mediated degradation seen with fibrin alone. HNSPCs express two fibrinogen-binding integrins, αVβ1 and α5β1, and several laminin binding integrins (α7β1, α6β1, α3β1) that can mediate interaction with the scaffold. Lastly, to test the ability of scaffolds to support vascularization, we analyzed human cord blood-derived endothelial cells alone and in co-culture with hNSPCs and found enhanced vessel formation and complexity in co-cultures within combination scaffolds. Overall, combination scaffolds of fibrin, HA, and laminin are excellent biomaterials for hNSPCs.

A biomaterial that inhibits the host immune response by displaying an endogenously expressed immunomodulatory molecule, CD200. Immobilization of CD200 onto biomaterial surfaces effectively suppresses macrophage activation and reduces inflammatory response to subcutaneously implanted materials.

No abstract

Collagen is the most abundant protein in extracellular matrices and is commonly used as a tissue engineering scaffold. However, collagen and other biopolymers from native sources can exhibit limitations when tuning mechanical and biological properties. Cysteines do not naturally occur within the triple-helical region of any native collagen. We utilized a novel modular synthesis strategy to fabricate variants of recombinant human collagen that contained 2, 4, or 8 non-native cysteines at precisely defined locations within each biopolymer. This bottom-up approach introduced capabilities using sulfhydryl chemistry to form hydrogels and immobilize bioactive factors. Collagen variants retained their triple-helical structure and supported cellular adhesion. Hydrogels were characterized using rheology, and the storage moduli were comparable to fibrillar collagen gels at similar concentrations. Furthermore, the introduced cysteines functioned as anchoring sites, with TGF-β1-conjugated collagens promoting myofibroblast differentiation. This approach demonstrates the feasibility to produce customdesigned collagens with chemical functionality not available from native sources.

Adhesion to the microenvironment profoundly affects stem cell functions, including proliferation and differentiation, and understanding the interaction of stem cells with the microenvironment is important for controlling their behavior. In this study, we investigated the effects of the integrin binding epitopes GFOGER and IKVAV (natively present in collagen I and laminin, respectively) on human neural stem/progenitor cells (hNSPCs). To test the specificity of these epitopes, GFOGER or IKVAV were placed within the context of recombinant triple-helical collagen III engineered to be devoid of native integrin binding sites. HNSPCs adhered to collagen that presented GFOGER as the sole integrin-binding site, but not to IKVAV-containing collagen. For the GFOGER-containing collagens, antibodies against the β1 integrin subunit prevented cellular adhesion, antibodies against the α1 subunit reduced cell adhesion, and antibodies against α2 or α3 subunits had no significant effect. These results indicate that hNSPCs primarily interact with GFOGER through the α1β1 integrin heterodimer. These GFOGER-presenting collagen variants also supported differentiation of hNSPCs into neurons and astrocytes. Our findings show, for the first time, that hNSPCs can bind to the GFOGER sequence, and they provide motivation to develop hydrogels formed from recombinant collagen variants as a cell delivery scaffold. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1363-1372, 2018.

Over the past decade, a growing community of researchers has emerged around the use of COnstraint-Based Reconstruction and Analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a significant update of this in silico ToolBox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include: (1) network gap filling, (2) 13 C analysis, (3) metabolic engineering, (4) omics-guided analysis, and (5) visualization. As with the first version, the COBRA Toolbox reads and writes Systems Biology Markup Language formatted models. In version 2.0, we improved performance, usability, and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the Toolbox and validate results. This Toolbox lowers the barrier of entry to use powerful COBRA methods.