Use and limitations of modular metabolic control analysis in medicine and biotechnology

Constraint-based metabolic control analysis for rational strain engineering

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

Deciphering the regulation of metabolism with dynamic optimization: an overview of recent advances

Understanding optimality principles shaping the evolution of regulatory networks controlling metabolism is crucial for deriving a holistic picture of how metabolism is integrated into key cellular processes such as growth, adaptation and pathogenicity. While in the past the focus of research in pathway regulation was mainly based on stationary states, more recently dynamic optimization has proved to be an ideal tool to decipher regulatory strategies for metabolic pathways in response to environmental cues. In this short review, we summarize recent advances in the elucidation of optimal regulatory strategies and identification of optimal control points in metabolic pathways. We discuss biological implications of the discovered optimality principles on genome organization and provide examples how the derived knowledge can be used to identify new treatment strategies against pathogens. Furthermore, we briefly discuss the variety of approaches for solving dynamic optimization problems and emphasize whole-cell resource allocation models as an important emerging area of research that will allow us to study the regulation of metabolism on the whole-cell level.

Robust identification of metabolic control for microbial l-methionine production following an easy-to-use puristic approach

The identification of promising metabolic engineering targets is a key issue in metabolic control analysis (MCA). Conventional approaches make intensive use of model-based studies, such as exploiting post-pulse metabolic dynamics after proper perturbation of the microbial system. Here, we present an easy-to-use, purely data-driven approach, defining pool efflux capacities (PEC) for identifying reactions that exert the highest flux control in linear pathways. Comparisons with linlog-based MCA and data-driven substrate elasticities (DDSE) showed that similar key control steps were identified using PEC. Using the example of l-methionine production with recombinant Escherichia coli, PEC consistently and robustly identified main flux controls using perturbation data after a non-labeled ¹²C-l-serine stimulus. Furthermore, the application of full-labeled ¹³C-l-serine stimuli yielded additional insights into stimulus propagation to l-methionine. PEC analysis performed on the ¹³C data set revealed the same targets as the ¹²C data set. Notably, the typical drawback of metabolome analysis, namely, the omnipresent leakage of metabolites, was excluded using the ¹³C PEC approach.

Designing RNA-based genetic control systems for efficient production from engineered metabolic pathways

Engineered metabolic pathways can be augmented with dynamic regulatory controllers to increase production titers by minimizing toxicity and helping cells maintain homeostasis. We investigated the potential for dynamic RNA-based genetic control systems to increase production through simulation analysis of an engineered p-aminostyrene (p-AS) pathway in E. coli. To map the entire design space, we formulated 729 unique mechanistic models corresponding to all of the possible control topologies and mechanistic implementations in the system under study. Two thousand sampled simulations were performed for each of the 729 system designs to relate the potential effects of dynamic control to increases in p-AS production (total of 3 × 10(6) simulations). Our analysis indicates that dynamic control strategies employing aptazyme-regulated expression devices (aREDs) can yield >10-fold improvements over static control. We uncovered generalizable trends in successful control architectures and found that highly performing RNA-based control systems are experimentally tractable. Analyzing the metabolic control state space to predict optimal genetic control strategies promises to enhance the design of metabolic pathways.

A modular modulation method for achieving increases in metabolite production

Increasing the production of overproducing strains represents a great challenge. Here, we develop a modular modulation method to determine the key steps for genetic manipulation to increase metabolite production. The method consists of three steps: (i) modularization of the metabolic network into two modules connected by linking metabolites, (ii) change in the activity of the modules using auxiliary rates producing or consuming the linking metabolites in appropriate proportions and (iii) determination of the key modules and steps to increase production. The mathematical formulation of the method in matrix form shows that it may be applied to metabolic networks of any structure and size, with reactions showing any kind of rate laws. The results are valid for any type of conservation relationships in the metabolite concentrations or interactions between modules. The activity of the module may, in principle, be changed by any large factor. The method may be applied recursively or combined with other methods devised to perform fine searches in smaller regions. In practice, it is implemented by integrating to the producer strain heterologous reactions or synthetic pathways producing or consuming the linking metabolites. The new procedure may contribute to develop metabolic engineering into a more systematic practice.

A structured approach to the study of metabolic control principles in intact and impaired mitochondria

We devised an approach to extract control principles of cellular bioenergetics for intact and impaired mitochondria from ODE-based models and applied it to a recently established bioenergetic model of cancer cells. The approach used two methods for varying ODE model parameters to determine those model components that, either alone or in combination with other components, most decisively regulated bioenergetic state variables. We found that, while polarisation of the mitochondrial membrane potential (ΔΨ(m)) and, therefore, the protomotive force were critically determined by respiratory complex I activity in healthy mitochondria, complex III activity was dominant for ΔΨ(m) during conditions of cytochrome-c deficiency. As a further important result, cellular bioenergetics in healthy, ATP-producing mitochondria was regulated by three parameter clusters that describe (1) mitochondrial respiration, (2) ATP production and consumption and (3) coupling of ATP-production and respiration. These parameter clusters resembled metabolic blocks and their intermediaries from top-down control analyses. However, parameter clusters changed significantly when cells changed from low to high ATP levels or when mitochondria were considered to be impaired by loss of cytochrome-c. This change suggests that the assumption of static metabolic blocks by conventional top-down control analyses is not valid under these conditions. Our approach is complementary to both ODE and top-down control analysis approaches and allows a better insight into cellular bioenergetics and its pathological alterations.

Parts plus pipes: synthetic biology approaches to metabolic engineering

Synthetic biologists combine modular biological "parts" to create higher-order devices. Metabolic engineers construct biological "pipes" by optimizing the microbial conversion of basic substrates to desired compounds. Many scientists work at the intersection of these two philosophies, employing synthetic devices to enhance metabolic engineering efforts. These integrated approaches promise to do more than simply improve product yields; they can expand the array of products that are tractable to produce biologically. In this review, we explore the application of synthetic biology techniques to next-generation metabolic engineering challenges, as well as the emerging engineering principles for biological design.

Impact of gene vector design on the control of recombinant monoclonal antibody production by Chinese hamster ovary cells

In this study, we systematically compare two vector design strategies for recombinant monoclonal antibody (Mab) synthesis by Chinese hamster ovary (CHO) cells; a dual open reading frame (ORF) expression vector utilizing separate cytomegalovirus (CMV) promoters to drive heavy chain (HC) and light chain (LC) expression independently, and a single ORF vector design employing a single CMV promoter to drive HC and LC polypeptide expression joined by a foot and mouth disease virus F2A polypeptide self-cleaving linker sequence. Initial analysis of stable transfectants showed that transfectants utilizing the single ORF vector designs exhibited significantly reduced Mab production. We employed an empirical modeling strategy to quantitatively describe the cellular constraints on recombinant Mab synthesis in all stable transfectants. In all transfectants, an intracellular molar excess of LC polypeptide over HC polypeptide was observed. For CHO cells transfected with the single ORF vectors, model-predicted, and empirical intracellular intermediate levels could only be reconciled by inclusion of nascent HC polypeptide degradation. Whilst a local sensitivity analysis showed that qMab of all transfectants was primarily constrained by recombinant mRNA translation rate, our data indicated that all single ORF transfectants exhibited a reduced level of recombinant gene transcription and that Mab folding and assembly reactions generically exerted greater control over qMab. We infer that the productivity of single ORF transfectants is limited by ER processing/degradation "capacity" which sets a limit on transcriptional input. We conclude that gene vector design for oligomeric recombinant proteins should be based on an understanding of protein-specific synthetic kinetics rather than polypeptide stoichiometry.

An empirical modeling platform to evaluate the relative control discrete CHO cell synthetic processes exert over recombinant monoclonal antibody production process titer

In this study we have combined empirically derived mathematical models of intracellular Mab synthesis to quantitatively compare the degree to which individual cellular processes limit recombinant IgG(4) monoclonal antibody production by GS-CHO cells throughout a state-of-the-art industrial fed-batch culture process. Based on the calculation of a production process control coefficient for each stage of the intracellular Mab synthesis and secretion pathway, we identified the major cellular restrictions on Mab production throughout the entire culture process to be recombinant heavy chain gene transcription and heavy chain mRNA translation. Surprisingly, despite a substantial decline in the rate of cellular biomass synthesis during culture, with a concomitant decline in the calculated rate constants for energy-intensive Mab synthetic processes (Mab folding/assembly and secretion), these did not exert significant control of Mab synthesis at any stage of production. Instead, cell-specific Mab production was maintained by increased Mab gene transcription which offset the decline in cellular biosynthetic rates. Importantly, this study shows that application of this whole-process predictive modeling strategy should rationally precede and inform cell engineering approaches to increase production of a recombinant protein by a mammalian host cell--where control of productivity is inherently protein product and cell line specific.

Methanol assimilation in Methylobacterium extorquens AM1: demonstration of all enzymes and their regulation

Background

Methylobacterium extorquens AM1 is an aerobic facultative methylotrophic α-proteobacterium that can use reduced one-carbon compounds such as methanol, but also multi-carbon substrates like acetate (C₂) or succinate (C₄) as sole carbon and energy source. The organism has gained interest as future biotechnological production platform based on methanol as feedstock.

Methodology/Principal Findings

We present a comprehensive study of all postulated enzymes for the assimilation of methanol and their regulation in response to the carbon source. Formaldehyde, which is derived from methanol oxidation, is assimilated via the serine cycle, which starts with glyoxylate and forms acetyl-CoA. Acetyl-CoA is assimilated via the proposed ethylmalonyl-CoA pathway, which thereby regenerates glyoxylate. To further the understanding of the central carbon metabolism we identified and quantified all enzymes of the pathways involved in methanol assimilation. We observed a strict differential regulation of their activity level depending on whether C₁, C₂ or C₄ compounds are used. The enzymes, which are specifically required for the utilization of the individual substrates, were several-fold up-regulated and those not required were down-regulated. The enzymes of the ethylmalonyl-CoA pathway showed specific activities, which were higher than the calculated minimal values that can account for the observed growth rate. Yet, some enzymes of the serine cycle, notably its first and last enzymes serine hydroxymethyl transferase and malate thiokinase, exhibit much lower values and probably are rate limiting during methylotrophic growth. We identified the natural C₁ carrying coenzyme as tetrahydropteroyl-tetraglutamate rather than tetrahydrofolate.

Conclusion/Significance

This study provides the first complete picture of the enzymes required for methanol assimilation, the regulation of their activity levels in response to the growth substrate, and the identification of potential growth limiting steps.

Harnessing nature's toolbox: regulatory elements for synthetic biology

Synthetic biologists seek to engineer complex biological systems composed of modular elements. Achieving higher complexity in engineered biological organisms will require manipulating numerous systems of biological regulation: transcription; RNA interactions; protein signalling; and metabolic fluxes, among others. Exploiting the natural modularity at each level of biological regulation will promote the development of standardized tools for designing biological systems.

Super life--how and why 'cell selection' leads to the fastest-growing eukaryote

What is the highest possible replication rate for living organisms? The cellular growth rate is controlled by a variety of processes. Therefore, it is unclear which metabolic process or group of processes should be activated to increase growth rate. An organism that is already growing fast may already have optimized through evolution all processes that could be optimized readily, but may be confronted with a more generic limitation. Here we introduce a method called 'cell selection' to select for highest growth rate, and show how such a cellular site of 'growth control' was identified. By applying pH-auxostat cultivation to the already fast-growing yeast Kluyveromyces marxianus for a sufficiently long time, we selected a strain with a 30% increased growth rate; its cell-cycle time decreased to 52 min, much below that reported to date for any eukaryote. The increase in growth rate was accompanied by a 40% increase in cell surface at a fairly constant cell volume. We show how the increase in growth rate can be explained by a dominant (80%) limitation of growth by the group of membrane processes (a 0.7% increase of specific growth rate to a 1% increase in membrane surface area). Simultaneous activation of membrane processes may be what is required to accelerate growth of the fastest-growing form of eukaryotic life to growth rates that are even faster, and may be of potential interest for single-cell protein production in industrial 'White' biotechnology processes.

Modular metabolic control analysis of large responses

Deciphering the laws that govern metabolic responses of complex systems is essential to understand physiological functioning, pathological conditions and the outcome of experimental manipulations of intact cells. To this aim, a theoretical and experimental sensitivity analysis, called modular metabolic control analysis (MMCA), was proposed. This field was previously developed under the assumptions of infinitesimal changes and/or proportionality between parameters and rates, which are usually not fulfilled in vivo. Here we develop a general MMCA for two modules, not relying on those assumptions. Control coefficients and elasticity coefficients for large changes are defined. These are subject to constraints: summation and response theorems, and relationships that allow calculating control from elasticity coefficients. We show how to determine the coefficients from top-down experiments, measuring the rates of the isolated modules as a function of the linking intermediate (there is no need to change parameters inside the modules). The novel formalism is applied to data of two experimental studies from the literature. In one of these, 40% increase in the activity of the supply module results in less than 4% increase in flux, while infinitesimal MMCA predicts more than 30% increase in flux. In addition, it is not possible to increase the flux by manipulating the activity of demand. The impossibility of increasing the flux by changing the activity of a single module is due to an abrupt decrease of the control of the modules when their corresponding activities are increased. In these cases, the infinitesimal approach can give highly erroneous predictions.

Metabolic control analysis under uncertainty: framework development and case studies

Information about the enzyme kinetics in a metabolic network will enable understanding of the function of the network and quantitative prediction of the network responses to genetic and environmental perturbations. Despite recent advances in experimental techniques, such information is limited and existing experimental data show extensive variation and they are based on in vitro experiments. In this article, we present a computational framework based on the well-established (log)linear formalism of metabolic control analysis. The framework employs a Monte Carlo sampling procedure to simulate the uncertainty in the kinetic data and applies statistical tools for the identification of the rate-limiting steps in metabolic networks. We applied the proposed framework to a branched biosynthetic pathway and the yeast glycolysis pathway. Analysis of the results allowed us to interpret and predict the responses of metabolic networks to genetic and environmental changes, and to gain insights on how uncertainty in the kinetic mechanisms and kinetic parameters propagate into the uncertainty in predicting network responses. Some of the practical applications of the proposed approach include the identification of drug targets for metabolic diseases and the guidance for design strategies in metabolic engineering for the purposeful manipulation of the metabolism of industrial organisms.

Metabolic engineering: advances in modeling and intervention in health and disease

The field of metabolic engineering encompasses a powerful set of tools that can be divided into (a) methods to model complex metabolic pathways and (b) techniques to manipulate these pathways for a desired metabolic outcome. These tools have recently seen increased utility in the medical arena, and this paper aims to review significant accomplishments made using these approaches. The modeling of metabolic pathways has been applied to better understand disease-state physiology in a variety of cellar, subcellular, and organ systems, including the liver, heart, mitochondria, and cancerous cells. Metabolic pathway engineering has been used to generate cells with novel biochemical functions for therapeutic use, and specific examples are provided in the areas of glycosylation engineering and dopamine-replacement therapy. In order to document the potential of applying both metabolic modeling and pathway manipulation, we describe pertinent advances in the field of diabetes research. Undoubtedly, as the field of metabolic engineering matures and is applied to a wider array of problems, new advances and therapeutic strategies will follow.

Understanding the art of producing protein and nonprotein molecules in Escherichia coli

The high-level production of functional proteins in E. coli is a very extense field of research in biotechnology. A number of important aspects to be considered in the initial design of an expression system and their interplay, were clear years ago. However, in recent times, strategies that go beyond transcription, translation, stability, vector, and strain choice, have been developed; so now expression of active peptides can be viewed as a more integrated process. Coexpression of protein subunits, foldases and chaperones, protein folding, location and purification schemes, metabolic engineering of the cell's central metabolism, and in vitro refolding strategies, are some of the novelties that are now available to aid in the success of an efficient expression system for active heterologous proteins. This review presents a compilation of the basic issues that influence the success in the production of protein and nonprotein products in Escherichia coli, as well as some general strategies designed to facilitate downstream process operations and improve biosynthesis yields.