A dynamic kinetic model captures cell-free metabolism for improved butanol production

Uncovering the interleukin-12 pharmacokinetic desensitization mechanism and its consequences with mathematical modeling

The cytokine interleukin-12 (IL-12) is a potential immunotherapy because of its ability to induce a Th1 immune response. However, success in the clinic has been limited due to a phenomenon called IL-12 desensitization - the trend where repeated exposure to IL-12 leads to reduced IL-12 concentrations (pharmacokinetics) and biological effects (pharmacodynamics). Here, we investigated IL-12 pharmacokinetic desensitization via a modeling approach to (i) validate proposed mechanisms in literature and (ii) develop a mathematical model capable of predicting IL-12 pharmacokinetic desensitization. Two potential causes of IL-12 pharmacokinetic desensitization were identified: increased clearance or reduced bioavailability of IL-12 following repeated doses. Increased IL-12 clearance was previously proposed to occur due to the upregulation of IL-12 receptor on T cells that causes increased receptor-mediated clearance in the serum. However, our model with this mechanism, the accelerated-clearance model, failed to capture trends in clinical trial data. Alternatively, our novel reduced-bioavailability model assumed that upregulation of IL-12 receptor on T cells in the lymphatic system leads to IL-12 sequestration, inhibiting the transport to the blood. This model accurately fits IL-12 pharmacokinetic data from three clinical trials, supporting its biological relevance. Using this model, we analyzed the model parameter space to illustrate that IL-12 desensitization occurs over a robust range of parameter values and to identify the conditions required for desensitization. We next simulated local, continuous IL-12 delivery and identified several methods to mitigate systemic IL-12 exposure. Ultimately, our results provide quantitative validation of our proposed mechanism and allow for accurate prediction of IL-12 pharmacokinetics over repeated doses.

Systems-Level Modeling for CRISPR-Based Metabolic Engineering

The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

Iterative design of training data to control intricate enzymatic reaction networks

Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.

Model-based optimization of cell-free enzyme cascades exemplified for the production of GDP-fucose

Cell-free production systems are increasingly used for the synthesis of industrially relevant chemicals and biopharmaceuticals. Cell-free systems often utilize cell lysates, but biocatalytic cascades based on recombinant enzymes have emerged as a promising alternative strategy. However, implementing efficient enzyme cascades is a non-trivial task and mathematical modeling and optimization has become a key tool to improve their performance. In this work, we introduce a generic framework for the model-based optimization of cell-free enzyme cascades based on a given kinetic model of the system. We first formulate and systematize seven optimization problems relevant in the context of cell-free production processes including, for example, the maximization of productivity or product yield and the minimization of overall costs. We then present an approach that accounts for parameter uncertainties, not only during model calibration and model analysis but also when performing the actual optimization. After constructing a kinetic model of the enzyme cascade, experimental data are used to generate an ensemble of kinetic parameter sets reflecting their variabilities. For every parameter set, systems optimization is then performed and the resulting solution subsequently cross-validated for all other parameterizations to identify the solution with the highest overall performance under parameter uncertainty. We exemplify our approach for the cell-free synthesis of GDP-fucose, an important sugar nucleotide with various applications. We selected and solved three optimization problems based on a constructed dynamic model and validated two of them experimentally leading to significant improvements of the process (e.g., 50% increase of titer under identical total enzyme load). Overall, our results demonstrate the potential of model-driven optimization for the rational design and improvement of cell-free production systems. The developed approach for systems optimization under parameter uncertainty could also be relevant for the metabolic design of cell factories.

Advancing synthetic biology through cell-free protein synthesis

The rapid development of synthetic biology has enabled the production of compounds with revolutionary improvements in biotechnology. DNA manipulation tools have expedited the engineering of cellular systems for this purpose. Nonetheless, the inherent constraints of cellular systems persist, imposing an upper limit on mass and energy conversion efficiencies. Cell-free protein synthesis (CFPS) has demonstrated its potential to overcome these inherent constraints and has been instrumental in the further advancement of synthetic biology. Via the removal of the cell membranes and redundant parts of cells, CFPS has provided flexibility in directly dissecting and manipulating the Central Dogma with rapid feedback. This mini-review summarizes recent achievements of the CFPS technique and its application to a wide range of synthetic biology projects, such as minimal cell assembly, metabolic engineering, and recombinant protein production for therapeutics, as well as biosensor development for in vitro diagnostics. In addition, current challenges and future perspectives in developing a generalized cell-free synthetic biology are outlined.