IN SILICO ANALYSIS FOR THE PRODUCTION OF HIGHER CARBON ALCOHOLS USING SACCHAROMYCES CEREVISIAE

Technology for the production of alternative fuels is receiving increased attention owing to concerns on global energy and environmental problems. Using higher carbon alcohols as gasoline substitutes has several advantages compared to ethanol, the first generation biofuel. Higher carbon alcohols also have other applications as flavor/aroma compounds and as building blocks for several other products. Two different pathways for the production of higher carbon alcohols have been recently reported. This work looks at evaluating the different pathways for higher carbon alcohol production and identification of metabolic bottlenecks for their production using Saccharomyces cerevisiae . Quantitative characterization of the metabolic pathways of Saccharomyces cerevisiae is essential for understanding the metabolic behavior of the microorganism. Several mathematical modeling frameworks have been developed to describe and analyze the metabolic behavior of an organism. Stoichiometric modeling is one such approach which relies on mass balances over intracellular metabolites and the assumption of pseudo-steady-state conditions to determine intracellular metabolic fluxes. The development of stoichiometric models (metabolic models) and analysis of intracellular metabolic fluxes have several applications in metabolic engineering and strain improvement. The production of higher carbon alcohols (such as 1-butanol, isobutanol, isopropanol) was analyzed by introducing the pathways into the genome scale metabolic model of Saccharomyces cerevisiae . The yield of higher carbon alcohols obtained from the fermentative and non-fermentative pathways was calculated and compared with maximum theoretical yield. The effect of different industrially relevant carbon sources on the production of higher carbon alcohols was also analyzed. Constraint based analysis was carried out on the genome scale metabolic model to obtain the intracellular metabolic flux distribution during the production of these alcohols. Detailed analysis of the metabolic flux distribution was carried out based on the shadow prices and reduced costs obtained from metabolic flux analysis. The metabolic bottlenecks for the production of higher carbon alcohols and the rate limiting steps in the metabolism were identified based on these analyses. Strategies for enhancing the yield of higher carbon alcohols will be proposed based on these analyses

Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data

Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients – basic parameters required for implementing the MIIA – are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite

Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities

An intriguing aspect in microbial communities is that pairwise interactions can be influenced by neighboring species. This creates context dependencies for microbial interactions that are based on the functional composition of the community. Context dependent interactions are ecologically important and clearly present in nature, yet firmly established theoretical methods are lacking from many modern computational investigations. Here, we propose a novel network inference method that enables predictions for interspecies interactions affected by shifts in community composition and species populations. Our approach first identifies interspecies interactions in binary communities, which is subsequently used as a basis to infer modulation in more complex multi-species communities based on the assumption that microbes minimize adjustments of pairwise interactions in response to neighbor species. We termed this rule-based inference minimal interspecies interaction adjustment (MIIA). Our critical assessment of MIIA has produced reliable predictions of shifting interspecies interactions that are dependent on the functional role of neighbor organisms. We also show how MIIA has been applied to a microbial community composed of competing soil bacteria to elucidate a new finding that – in many cases – adding fewer competitors could impose more significant impact on binary interactions. The ability to predict membership-dependent community behavior is expected to help deepen our understanding of how microbiomes are organized in nature and how they may be designed and/or controlled in the future

Real-time data-driven and multi-scale model-guided system for bioproccess digital twin platform

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Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement

<p>Abstract</p> <p>Background</p> <p><it>Pichia pastoris </it>has been recognized as an effective host for recombinant protein production. A number of studies have been reported for improving this expression system. However, its physiology and cellular metabolism still remained largely uncharacterized. Thus, it is highly desirable to establish a systems biotechnological framework, in which a comprehensive <it>in silico </it>model of <it>P. pastoris </it>can be employed together with high throughput experimental data analysis, for better understanding of the methylotrophic yeast's metabolism.</p> <p>Results</p> <p>A fully compartmentalized metabolic model of <it>P. pastoris </it>(<it>iPP</it>668), composed of 1,361 reactions and 1,177 metabolites, was reconstructed based on its genome annotation and biochemical information. The constraints-based flux analysis was then used to predict achievable growth rate which is consistent with the cellular phenotype of <it>P. pastoris </it>observed during chemostat experiments. Subsequent <it>in silico </it>analysis further explored the effect of various carbon sources on cell growth, revealing sorbitol as a promising candidate for culturing recombinant <it>P. pastoris </it>strains producing heterologous proteins. Interestingly, methanol consumption yields a high regeneration rate of reducing equivalents which is substantial for the synthesis of valuable pharmaceutical precursors. Hence, as a case study, we examined the applicability of <it>P. pastoris </it>system to whole-cell biotransformation and also identified relevant metabolic engineering targets that have been experimentally verified.</p> <p>Conclusion</p> <p>The genome-scale metabolic model characterizes the cellular physiology of <it>P. pastoris</it>, thus allowing us to gain valuable insights into the metabolism of methylotrophic yeast and devise possible strategies for strain improvement through <it>in silico </it>simulations. This computational approach, combined with synthetic biology techniques, potentially forms a basis for rational analysis and design of <it>P. pastoris </it>metabolic network to enhance humanized glycoprotein production.</p