Clostridium thermocellum is an anaerobic, Gram-positive, thermophilic bacterium that has generated great interest due to its ability to ferment lignocellulosic biomass to ethanol. However, ethanol production is low due to the complex and poorly understood branched metabolism of C. thermocellum, and in some cases overflow metabolism as well. In this work, we developed a predictive stoichiometric metabolic model for C. thermocellum which incorporates the current state of understanding, with particular attention to cofactor specificity in the atypical glycolytic enzymes and the complex energy, redox, and fermentative pathways with the goal of aiding metabolic engineering efforts. We validated the model's capability to encompass experimentally observed phenotypes for the parent strain and derived mutants designed for significant perturbation of redox and energy pathways. Metabolic flux distributions revealed significant alterations in key metabolic branch points (e.g., phosphoenol pyruvate, pyruvate, acetyl-CoA, and cofactor nodes) in engineered strains for channeling electron and carbon fluxes for enhanced ethanol synthesis, with the best performing strain doubling ethanol yield and titer compared to the parent strain. In silico predictions of a redox-imbalanced genotype incapable of growth were confirmed in vivo, and a mutant strain was used as a platform to probe redox bottlenecks in the central metabolism that hinder efficient ethanol production. The results highlight the robustness of the redox metabolism of C. thermocellum and the necessity of streamlined electron flux from reduced ferredoxin to NAD(P)H for high ethanol production. The model was further used to design a metabolic engineering strategy to phenotypically constrain C. thermocellum to achieve high ethanol yields while requiring minimal genetic manipulations. The model can be applied to design C. thermocellum as a platform microbe for consolidated bioprocessing to produce ethanol and other reduced metabolites.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

BackgroundClostridium thermocellum is a gram-positive thermophile that can directly convert lignocellulosic material into biofuels. The metabolism of C. thermocellum contains many branches and redundancies which limit biofuel production, and typical genetic techniques are time-consuming. Further, the genome sequence of a genetically tractable strain C. thermocellum DSM 1313 has been recently sequenced and annotated. Therefore, developing a comprehensive, predictive, genome-scale metabolic model of DSM 1313 is desired for elucidating its complex phenotypes and facilitating model-guided metabolic engineering.ResultsWe constructed a genome-scale metabolic model iAT601 for DSM 1313 using the KEGG database as a scaffold and an extensive literature review and bioinformatic analysis for model refinement. Next, we used several sets of experimental data to train the model, e.g., estimation of the ATP requirement for growth-associated maintenance (13.5 mmol ATP/g DCW/h) and cellulosome synthesis (57 mmol ATP/g cellulosome/h). Using our tuned model, we investigated the effect of cellodextrin lengths on cell yields, and could predict in silico experimentally observed differences in cell yield based on which cellodextrin species is assimilated. We further employed our tuned model to analyze the experimentally observed differences in fermentation profiles (i.e., the ethanol to acetate ratio) between cellobiose- and cellulose-grown cultures and infer regulatory mechanisms to explain the phenotypic differences. Finally, we used the model to design over 250 genetic modification strategies with the potential to optimize ethanol production, 6155 for hydrogen production, and 28 for isobutanol production.ConclusionsOur developed genome-scale model iAT601 is capable of accurately predicting complex cellular phenotypes under a variety of conditions and serves as a high-quality platform for model-guided strain design and metabolic engineering to produce industrial biofuels and chemicals of interest.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-016-0607-x) contains supplementary material, which is available to authorized users.