Brian J. Schmidt

GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data

MOTIVATION Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed. RESULTS GIM(3)E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM(3)E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM(3)E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. AVAILABILITY GIM(3)E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/ CONTACTS brianjamesschmidt@gmail.com

Mechanistic systems modeling to guide drug discovery and development.

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.

Metabolic systems analysis to advance algal biotechnology

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine‐tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome‐scale model‐driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Network analysis can direct microbial development efforts towards successful strategies and enable quantitative fine‐tuning of the network for optimal product yields while maintaining the robustness of the production microbe. Metabolic modeling yields insights into microbial function, guides experiments by generating testable hypotheses, and enables the refinement of knowledge on the specific organism. While the application of such analytical approaches to algal systems is limited to date, metabolic network analysis can improve understanding of algal metabolic systems and play an important role in expediting the adoption of new biofuel technologies.

Salmonella modulates metabolism during growth under conditions that induce expression of virulence genes

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome-scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome-scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake.

Adhesion and ultrasound-induced delivery from monodisperse microbubbles in a parallel plate flow cell.

Microbubbles, ultrasound contrast agents currently in development as ultrasonically activated drug delivery vehicles, were studied using a novel flow cell design. The flow cell combined ultrasound compatibility, a planar optical configuration, and a Cartesian orientation of buoyant, shear, and acoustic forces. The set-up enabled measurements of buoyant rise and adhesive sensitivity to shear forces for individual biotinylated, monodisperse, polymer-shelled microbubbles near a NeutrAvidin-coated polystyrene substrate. Analysis of the velocity history demonstrated that adhesion depended on the buoyant rise to the surface before attachment to the substrate: only when the distance parallel to the substrate in the flow direction was between 10 and 20 microm from the stopping position could specific molecular recognition events occur. Low intensity ultrasound caused strong two-dimensional mobility leading to reversible clustering of microbubbles, even though they interacted strongly with the substrate through biotin-NeutrAvidin bonds. At higher acoustic pressure, local gas release took place. With sufficient acoustic intensity, the agents demonstrate potential as large payload carriers for biomolecularly targeted therapeutic delivery. However, difficulties may limit the range of targeting applications: large sizes may render microbubbles susceptible to detachment at the shearing forces present in many regions of the vasculature and secondary radiation forces may reduce targeting effectiveness.

Catch Strip Assay for the Relative Assessment of Two-Dimensional Protein Association Kinetics

Accurate interpretation of recruitment rate measurements of microscale particles, such as cells and microbeads, to biofunctional surfaces is difficult because factors such as uneven ligand distributions, particle collisions, variable particle fluxes, and molecular-scale surface separation distances obfuscate the ability to link the observed particle behavior with the governing nanoscale biophysics. We report the development of a hydrodynamically conditioned micropattern catch strip assay to measure microparticle recruitment kinetics. The assay exploited patterning within microfluidic channels and the mechanostability of selectin bonds to create reaction geometries that confined a microbead flux to within 200 nm of the surface under flow conditions. Systematic control of capillary action enabled the creation of homogeneous or gradient ligand distributions. The method enabled the measurement of particle recruitment rates (keff, s-1) that were primarily determined by the interaction of the biomolecular pair being investigated. The method is therefore well suited for relative measurements of delivery vehicle and cellular recruitment potential as governed by surface-bound molecules.

Nano-motion Dynamics are Determined by Surface-Tethered Selectin Mechanokinetics and Bond Formation

The interaction of proteins at cellular interfaces is critical for many biological processes, from intercellular signaling to cell adhesion. For example, the selectin family of adhesion receptors plays a critical role in trafficking during inflammation and immunosurveillance. Quantitative measurements of binding rates between surface-constrained proteins elicit insight into how molecular structural details and post-translational modifications contribute to function. However, nano-scale transport effects can obfuscate measurements in experimental assays. We constructed a biophysical simulation of the motion of a rigid microsphere coated with biomolecular adhesion receptors in shearing flow undergoing thermal motion. The simulation enabled in silico investigation of the effects of kinetic force dependence, molecular deformation, grouping adhesion receptors into clusters, surface-constrained bond formation, and nano-scale vertical transport on outputs that directly map to observable motions. Simulations recreated the jerky, discrete stop-and-go motions observed in P-selectin/PSGL-1 microbead assays with physiologic ligand densities. Motion statistics tied detailed simulated motion data to experimentally reported quantities. New deductions about biomolecular function for P-selectin/PSGL-1 interactions were made. Distributing adhesive forces among P-selectin/PSGL-1 molecules closely grouped in clusters was necessary to achieve bond lifetimes observed in microbead assays. Initial, capturing bond formation effectively occurred across the entire molecular contour length. However, subsequent rebinding events were enhanced by the reduced separation distance following the initial capture. The result demonstrates that vertical transport can contribute to an enhancement in the apparent bond formation rate. A detailed analysis of in silico motions prompted the proposition of wobble autocorrelation as an indicator of two-dimensional function. Insight into two-dimensional bond formation gained from flow cell assays might therefore be important to understand processes involving extended cellular interactions, such as immunological synapse formation. A biologically informative in silico system was created with minimal, high-confidence inputs. Incorporating random effects in surface separation through thermal motion enabled new deductions of the effects of surface-constrained biomolecular function. Important molecular information is embedded in the patterns and statistics of motion.

High Resolution Optical Tracking to Identify Adhesive Events in Vitro

Glycoproteins serve as ligands on metastatic cancer cells to mediate labile adhesive interactions with the vascular endothelium. The sub-second transience of carbohydrate-protein bonding events poses challenges for quantification of adhesion by optical tracking. We report the development of a simple, shape- based algorithm with novelty in its application to high spatiotemporal resolution tracking. We report positional and morphological signals from the spatial, time, and frequency domains for rigid particles and distensible cells. Error was comparable with previous methods: the resolution-dependent positional precision was 7 nm upon minimization of mechanical noise under experimental conditions.

Selectin Mechanokinetics and Two-Dimensional Bond Formation Determine and are Reported by Nano-Motion Dynamic Patterns

Binding between surface-tethered proteins at cellular interfaces has been considered two-dimensional because of the restricted motion of the two binding partners. Two-dimensional protein interactions between cells are critical for many biological processes, such as leukocyte vascular adhesion via selectins. Experimental measurements have yielded data on the kinetics of selectin bond formation and dissociation. Additionally, computational methods have been employed to integrate molecular and cellular properties to elucidate the factors that influence the dynamics of selectin-mediated rolling. Simulation methods focused on biomolecular properties promise to yield additional novel insights into the molecular component of adhesion with the assistance of measurements from improved assays. We performed an in silico investigation on the effects of the kinetic force dependence, molecular deformation, grouping adhesion receptors into clusters, two-dimensional bond formation, and nano-scale vertical transport on outputs that directly map to observable motions. Statistics describing the motion patterns tied simulated motions to experimentally reported quantities. Distributing adhesive forces among P-selectin/PSGL-1 molecules closely grouped in clusters was necessary to achieve pause times observed in microbead assays. Notably, rebinding events were enhanced by the reduced separation distance following initial sphere capture. The result demonstrates vertical transport can contribute to an enhancement in the apparent bond formation rate. The result also suggests a new mechanism that may be important for the rebinding events characteristic of stable leukocyte rolling. When selectin receptor and ligand are restricted to small, two-dimensional interaction zones during rolling, the resultant wobble was found to be dependent on the confinement model used. Insight into two-dimensional bond formation gained from flow cell assays might also therefore be important to understand processes involving extended cellular interactions, such as immunological synapse formation.

A Micropatterned Surface for the Assessment of Relative Two-Dimensional Molecular Selectin Kinetics in Flow

Recruitment rate measurements of micro-scale particles, such as cells or microbeads, to biofunctional surfaces is difficult because factors such as uneven ligand distributions, particle collisions, variable particle fluxes, and molecular-scale surface separation distances that combine to obfuscate the ability to link the observed particle behavior with the governing nanoscale biophysics. We report the development of a hydrodynamically-conditioned micropattern catch strip assay to measure microparticle and cellular recruitment kinetics. The assay exploited patterning within microfluidic channels and the mechanostability of selectin bonds to create reaction geometries that confined a microbead flux to within 200 nm of the surface under flow conditions. Systematic control of capillary action enabled the creation of homogenous or gradient ligand distributions. The method enabled the measurement of particle recruitment rates (keff , s−1 ) that were primarily determined by the interaction of the biomolecular pair being investigated. The method may be well suited for analysis of reaction rates between surface-bound molecules in the presence of convective flow patterns.Copyright