Flux Balance Analysis of Mammalian Cell Systems.

Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.

How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?

Genome-scale metabolic models (GEMs) possess the power to revolutionize bioprocess and cell line engineering workflows thanks to their ability to predict and understand whole-cell metabolism in silico. Despite this potential, it is currently unclear how accurately GEMs can capture both intracellular metabolic states and extracellular phenotypes. Here, we investigate this knowledge gap to determine the reliability of current Chinese hamster ovary (CHO) cell metabolic models. We introduce a new GEM, iCHO2441, and create CHO-S and CHO-K1 specific GEMs. These are compared against iCHO1766, iCHO2048, and iCHO2291. Model predictions are assessed via comparison with experimentally measured growth rates, gene essentialities, amino acid auxotrophies, and 13 C intracellular reaction rates. Our results highlight that all CHO cell models are able to capture extracellular phenotypes and intracellular fluxes, with the updated GEM outperforming the original CHO cell GEM. Cell line-specific models were able to better capture extracellular phenotypes but failed to improve intracellular reaction rate predictions in this case. Ultimately, this work provides an updated CHO cell GEM to the community and lays a foundation for the development and assessment of next-generation flux analysis techniques, highlighting areas for model improvements.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems.

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.

Controlling Liquid Crystal Configuration and Phase Using Multiple Molecular Triggers.

Liquid crystals are able to transform a local molecular interaction into a macroscopic change of state, making them a valuable "smart" material. Here, we investigate a novel polymeric amphiphile as a candidate for molecular triggering of liquid crystal droplets in aqueous background. Using microscopy equipped with crossed polarizers and optical tweezers, we find that the monomeric amphiphile is able to trigger both a fast phase change and then a subsequent transition from nematic to isotropic. We next include sodium dodecyl sulfate (SDS), a standard surfactant, with the novel amphiphilic molecules to test phase transitioning when both were present. As seen previously, we find that the activity of SDS at the surface can result in configuration changes with hysteresis. We find that the presence of the polymeric amphiphile reverses the hysteresis previously observed during such transitions. This work demonstrates a variety of phase and configuration changes of liquid crystals that can be controlled by multiple exogenous chemical triggers.

Direct Observation of Liquid Crystal Droplet Configurational Transitions using Optical Tweezers.

Liquid crystals (LCs) are easily influenced by external interactions, particularly at interfaces. When rod-like LC molecules are confined to spherical droplets, they experience a competition between interfacial tension and elastic deformations. The configuration of LCs inside a droplet can be controlled using surfactants that influence the interfacial orientation of the LC molecules in the oil-phase of an oil in water emulsion. Here, we used the surfactant sodium dodecyl sulfate (SDS) to manipulate the orientation of 5CB molecules in a polydisperse emulsion and examined the configuration of the droplets as a function of SDS concentration. We triggered pronounced morphological transitions by altering the SDS concentration while observing an individual LC droplet held in place using an optical tweezer. We compared the experimental configuration changes to predictions from simulations. We observed a hysteresis in the SDS concentration that induced the morphological transition from radial to bipolar and back as well as a fluctuations in the configuration during the transition.