COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling.

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Taking the pulse of bioprocesses: at-line and in-line monitoring of mammalian cell cultures.

Real-time and near real-time monitoring of cell culture processes are critical to the evolving process analytical technology (PAT) paradigm for upstream bioprocessing. The responses measured from these analytical instruments can enable rapid feedback to perturbations that can otherwise lead to batch failures. Historically, real-time monitoring of bioreactor processes has been relegated to parameters such as pH, dissolved oxygen, and temperature. Other analytical results, such as cell growth and metabolites, are provided through manual daily sampling. In order to reduce sample error and increase throughput, real-time and near real-time instruments have been developed. Here we discuss recent advances in these technologies. This article aims to focus on other developing at-line and in-line technologies that enable monitoring of bioreactor processes, including dielectric spectroscopy, NIR, off-gas spectrometry, integrated at-line HPLC, and nanofluidic devices for monitoring cell growth and health, metabolites, titer, and product quality.

Phytoremediation of agriculture runoff by filamentous algae poly-culture for biomethane production, and nutrient recovery for secondary cultivation of lipid generating microalgae.

An integrated system was implemented for water phytoremediation and biofuel production through sequential cultivation of filamentous algae followed by cultivation of lipid-producing microalgae Chlorella sorokiniana. Natural poly-culture of filamentous algae was grown in agricultural stormwater using the Algal Turf Scrubber®, harvested and subjected for lipid extraction and/or methane production using anaerobic digestion (AD). While filamentous algae lipid content was too low for feasible biodiesel production (<2%), both whole biomass and lipid-extracted algal residues (LEA) yielded ∼0.2LmethanepergVS at loading rates up to 5gVS/L-day. Importantly, essential macro-nutrients and trace elements captured from stormwater were released into the AD effluent as soluble nutrients and were successfully tested as fertilizer replacement for cultivation of lipid-accumulating C. sorokiniana in a subsequent stage. Accordingly, filamentous algae poly-culture was exploited for waste nutrient capturing and biofuel feedstock generation. These nutrients were recovered and reused as a concentrated supplement for potentially high-value microalgae.