Characterizing steady states of genome-scale metabolic networks in continuous cell cultures

We present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. More importantly, we derive a number of consequences from the model that are independent of parameter values. First, that the ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties invariant across perfusion systems. This conclusion is robust even in the presence of multi-stability, which is explained in our model by the negative feedback loop on cell growth due to toxic byproduct accumulation. Moreover, a complex landscape of steady states in continuous cell culture emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced. Thus, in order to actually reflect the expected behavior in perfusion, performance benchmarks of cell-lines and culture media should be carried out in a chemostat

MOMO - multi-objective metabolic mixed integer optimization : application to yeast strain engineering

BACKGROUND: In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering. RESULTS: Production of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. CONCLUSIONS: The multi-objective programming framework we developed, called MOMO, is open-source and uses POLYSCIP (Available at http://polyscip.zib.de/). as underlying multi-objective solver. MOMO is available at http://momo-sysbio.gforge.inria.fr

In silico Derivation of a Reduced Kinetic Model for Stationary or Oscillating Glycolysis in Escherichia coli Bacterium

Modelling bacteria glycolysis is a classical subject but still of high interest. Glycolysis, together with the phosphotransferase (PTS)-system for glucose transport into the cell, the pentose-phosphate pathway (PPP), and tricarboxylic acid cycle (TCA) characterize the central carbon metabolism. Such a model usually serves as the foundation for developing modular simulation platforms used for consistent analysis of the control / regulation of target metabolite synthesis. The present study is focused on analyzing the advantage and limitations of using a simplified but versatile ‘core’ model of mTRM) of glycolysis when incomplete experimental information is available. Exemplification is made for a reduced glycolysis model from literature for Escherichia coli cells, by performing a few modifications (17 identifiable parameters) to increase its agreement with simulated data generated by using an extended model (127 parameters) over a large operating domain of an experimental bioreactor. With the expense of ca. 8–13 % increase in the relative model error vs. extended simulation models, derivation of reduced kinetic structures to describe some parts of the core metabolism is worth the associated identification effort, due to the considerable reduction in model parameterization (e.g. 17 parameters in mTRM vs. 127 in the extendedChassM model of Chassagnole et al.), while preserving a fair adequacy over a wide experimental domain generated in-silico by using the valuable extended ChassM. The reduced model flexibility is tested by reproducing stationary or oscillatory glycolysis conditions. The reduced mTRM model includes enough information to reproduce not only the cell energy-related potential through the total A(MDT)P level, but also the role played by ATP/ADP ratio as a glycolysis driving force. The model can also reproduce the oscillatory behaviour occurrence conditions, as well as situations when homeostatic conditions are not fulfilled

Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains

Metabolic engineering is highly demanded currently for the production of various useful compounds such as succinate and lactate that are very useful in food, pharmaceutical, fossil fuels, and energy industries. Gene or reaction deletion known as knockout is one of the strategies used in in silico metabolic engineering to change the metabolism of the chosen microbial cells to obtain the desired phenotypes. However, the size and complexity of the metabolic network are a challenge in determining the near-optimal set of genes to be knocked out in the metabolism due to the presence of competing pathway that interrupts the high production of desired metabolite, leading to low production rate and growth rate of the required microorganisms. In addition, the inefficiency of existing algorithms in reconstructing high growth rate and production rate becomes one of the issues to be solved. Therefore, this research proposes Dynamic Flux Variability Analysis (DFVA) algorithm to identify the best knockout reaction combination to improve the production of desired metabolites in microorganisms. Based on the experimental results, DFVA shows an improvement of growth rate of succinate and lactate by 12.06% and 47.16% respectively in E. coli and by 4.62% and 47.98% respectively in S. Cerevisae. Suggested reactions to be knocked out to improve the production of succinate and lactate have been identified and validated through the biological database

Creation and Application of Various Tools for the Reconstruction, Curation, and Analysis of Genome-Scale Models of Metabolism

Systems biology uses mathematics tools, modeling, and analysis for holistic understanding and design of biological systems, allowing the investigation of metabolism and the generation of actionable hypotheses based on model analyses. Detailed here are several systems biology tools for model reconstruction, curation, analysis, and application through synthetic biology. The first, OptFill, is a holistic (whole model) and conservative (minimizing change) tool to aid in genome-scale model (GSM) reconstructions by filling metabolic gaps caused by lack of system knowledge. This is accomplished through Mixed Integer Linear Programming (MILP), one step of which may also be independently used as an additional curation tool. OptFill is applied to a GSM reconstruction of the melanized fungus Exophiala dermatitidis, which underwent various analyses investigating pigmentogenesis and similarity to human melanogenesis. Analysis suggest that carotenoids serve a currently unknown function in E. dermatitidis and that E. dermatitidis could serve as a model of human melanocytes for biomedical applications. Next, a new approach to dynamic Flux Balance Analysis (dFBA) is detailed, the Optimization- and Runge-Kutta- based Approach (ORKA). The ORKA is applied to the model plant Arabidopsis thaliana to show its ability to recreate in vivo observations. The analyzed model is more detailed than previous models, encompassing a larger time scale, modeling more tissues, and with higher accuracy. Finally, a pair of tools, the Eukaryotic Genetic Circuit Design (EuGeneCiD) and Modeling (EuGeneCiM) tools, is introduced which can aid in the design and modeling of synthetic biology applications hypothesized using systems biology. These tools bring a computational approach to synthetic biology, and are applied to Arabidopsis thaliana to design thousands of potential two-input genetic circuits which satisfy 27 different input and logic gate combinations. EuGeneCiM is further used to model a repressilator circuit. Efforts are ongoing to disseminate these tools to maximize their impact on the field of systems biology. Future research will include further investigation of E. dermatitidis through modeling and expanding my expertise to kinetic models of metabolism. Advisor: Rajib Sah

More than a feeling: A unified view of stress measurement for population science.

Stress can influence health throughout the lifespan, yet there is little agreement about what types and aspects of stress matter most for human health and disease. This is in part because "stress" is not a monolithic concept but rather, an emergent process that involves interactions between individual and environmental factors, historical and current events, allostatic states, and psychological and physiological reactivity. Many of these processes alone have been labeled as "stress." Stress science would be further advanced if researchers adopted a common conceptual model that incorporates epidemiological, affective, and psychophysiological perspectives, with more precise language for describing stress measures. We articulate an integrative working model, highlighting how stressor exposures across the life course influence habitual responding and stress reactivity, and how health behaviors interact with stress. We offer a Stress Typology articulating timescales for stress measurement - acute, event-based, daily, and chronic - and more precise language for dimensions of stress measurement

Development of cell factories for the efficient production of mannosylglycerate, a thermolyte with great potential in biotechnology

Mannosylglycerate (MG) is a compatible solute implicated in the response to osmotic or heat stresses in many marine microorganisms adapted to hot environments. MG shows a remarkable ability to protect model proteins, especially against heat denaturation; however, high production costs prevented the industrial exploitation of these features. This thesis has two main objectives: i) to assess the efficacy of MG as protein stabilizer in the intracellular milieu; and ii) to develop a bio-based process for production of MG at competitive cost. The first goal was achieved by using a yeast model of Parkinson’s disease in which an aggregation-prone protein, eGFP-tagged α-synuclein, was expressed along with the biosynthetic activities that catalyze the formation of MG from GDP-mannose and 3-phosphoglycerate. There was a reduction of 3.3-fold in the number of cells containing fluorescent foci of α-synuclein, in comparison with a control strain without MG. It was also proven that inhibition of aggregation was due to direct MG-protein effects, i.e., MG acted in vivo as a chemical chaperone. This opened a way for drug development against diseases related with protein misfolding. Towards the second objective, genes PMI40 and PSA1 of the GDP-mannose pathway were over-expressed in the industrial microorganism, Saccharomyces cerevisiae, to redirect metabolic flux towards that MG precursor. This strategy led to 2.2-fold increase in MG production (15.86 mgMG.gDW-1) for cells cultivated in controlled batch mode. Further improvement was achieved by cultivation in chemostat mode at a dilution rate of 0.15 h-1; a constant productivity of 1.79 mgMG.gDW-1h-1 was reached. Next, a holist approach was undertaken by using in silico tools to identify engineering strategies that would lead to efficient channeling of substrates to MG production. The proposed strains were constructed and characterized in batch fermentation and continuous mode and led to an improved MG production of 25.3 mgMG.gDW-1 and 3.4 mgMG.L-1h-1, respectively