Browsing by Author "Carlson, Ross"

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The Design and Characterization of Artifical Biofilms: Microbial Catalyst Platforms Based on Photo-Autotrophic Syntrophy
(2013-03) Bleem, Alissa; Carlson, Ross; Bernstein, Hans
Biofilm cells exist in environments with much higher local cell densities than those found in liquid environments, leading to significantly elevated levels of localized metabolic by-products. Such metabolites have the potential to play a key role in heterogeneous biofilms via syntrophy, a mutually beneficial process in which one microbe utilizes the by-products of another for its own proliferation. This project examined the metabolic characteristics of a microbial consortia biofilm comprised of two organisms. These artificial communities utilized an autotrophic cyanobacteria, Synechococcus sp., as a primary producer and a heterotrophic Escherichia coli as the corresponding consumer strain. Benefits of syntrophic metabolite exchange were characterized through growth rate data, vitamin exchanges, and comparison of biomass productivity under applied conditions. The artificial binary biofilm cultures displayed an approximate 40% increase in biomass productivity and nearly a 1.5-log increase in colony forming units per biofilm over the control Synechococcus mono-cultures. Current work on this system seeks to better understand the role of oxygen production and scavenging between the Synechococcus and E. coli as well as species-dependent spatial portioning within the biofilm.
In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products
(Frontiers Media SA, 2021-03) Rangel, Albert Enrique Tafur; Rios, Wendy; Mejia, Daisy; Ojeda, Carmen; Carlson, Ross; Ramírez, Jorge Mario Gómez; Barrios, Andrés Fernando González
Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout’s (feature’s) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.
Metabolic analysis of lipid accumulation in a microalga
(2013-03) Toussaint, Jean-Paul; Carlson, Ross; Mus, Florence
As concern grows about the supply of fossil fuels, new alternative energy sources are being investigated including renewable biofuels. Microalgae represent a competitive biofuel strategy when compare with “traditional” agricultural crops. Green algae and diatoms are of considerable interest as a biodiesel source because they accumulate significant amounts of energy-rich compounds, such as triacylglycerol (TAG) that can be used to synthesize biodiesel. My research project investigates factors that control TAG accumulation in the marine diatom Phaeodactylum tricornutum using physiological and molecular approaches. The first phase of the project identified optimal growth conditions that promote TAG accumulation in P. tricornutum. It has been found that nitrogen limitation, pH stress and the addition of bicarbonate or acetate stimulate lipids accumulation in P. tricornutum cells by 5 to 10 fold as compared to controls. Fundamental physiological data including photosynthetic pigment content, protein levels and carbohydrate content have been collected and correlated to TAG synthesis. A transcriptomic analysis is currently in progress to identify and characterize essential genes involved in TAG accumulation. Information on the abundance of specific transcripts under lipids accumulation conditions will permit description of bioenergetic and metabolic processes involved in TAG accumulation and to identify associated regulatory factors. This project advances algal biofuels research by elucidating both the physiological and transcriptomic basis of TAG accumulation in the marine diatom Phaeodactylum tricornutum providing a rational basis for TAG synthesis control.