Microbial growth modeling and analysis

Frontiers in Microbiology

The metabolic shift between respiration and fermentation at high glucose concentration is a widespread phenomenon in microbial world, and it is relevant for the biotechnological exploitation of microbial cell factories, affecting the achievement of high-cell-densities in bioreactors. Starting from a model already developed for the yeast Saccharomyces cerevisiae, based on the System Dynamics approach, a general process-based model for two prokaryotic species of biotechnological interest, such as Escherichia coli and Bacillus subtilis, is proposed. The model is based on the main assumption that glycolytic intermediates act as central catabolic hub regulating the shift between respiratory and fermentative pathways. Furthermore, the description of a mixed fermentation with secondary by-products, characteristic of bacterial metabolism, is explicitly considered. The model also represents the inhibitory effect on growth and metabolism of self-produced toxic compounds relevant in assessing the late phases of high-cell density culture. Model simulations reproduced data from experiments reported in the literature with different strains of non-recombinant and recombinant E. coli and B. subtilis cultured in both batch and fed-batch reactors.The proposed model, based on simple biological assumptions, is able to describe the main dynamics of two microbial species of relevant biotechnological interest. It demonstrates that a reductionist System Dynamics approach to formulate simplified macro-kinetic models can provide a robust representation of cell growth and accumulation in the medium of fermentation by-products.

Computational and Structural Biotechnology Journal, 2012

Journal of Biotechnology, 2006

This paper attempts to review in how far thermodynamic analysis can be used to understand and predict the performance of microorganisms with respect to growth and bioproduct synthesis. In the first part, a simple thermodynamic model of microbial growth is developed which explains the relationship between the driving force for growth in terms of Gibbs energy dissipation and biomass yield. From the currently available literature, it appears that the Gibbs energy dissipation per C-mol of biomass grown, which represents the driving force for chemotrophic growth, may bave been adapted by evolutionary processes to strike a reasonable compromise between metabolic rate and growth efficiency. Based on empirical correlations of the C-molar Gibbs energy dissipation, the wide variety ofbiomass yields observed in nature can be explained and roughly predicted. This type of analysis may be highly useful in environmental applications, where such wide variations occur. It is however not able to predict biomass yields in very complex systems such as mammalian cells nor is it able to predict or to assess bioproduct or recombinant protein yields. For this purpose, a much more sophisticated treatment that accounts for individual metabolic pathways separately is required. Based on glycolysis as a test example, it is shown in the last part that simple thermodynamic analysis leads to erroneous conclusions even in well-known, simple cases. Potential sources for errors have been analyzed and can be used to identify the most important needs for future research.

2015

High-throughput experimental techniques and bioinformatics tools make it possible to obtain reconstructions of the metabolism of microbial species. Combined with mathematical frameworks such as flux balance analysis, which assumes that nutrients are used so as to maximize growth, these reconstructions enable us to predict microbial growth. Although such predictions are generally accurate, these approaches do not give insights on how different nutrients are used to produce growth, and thus are difficult to generalize to new media or to different organisms. Here, we propose a systems-level phenomenological model of metabolism inspired by the virial expansion. Our model predicts biomass production given the nutrient uptakes and a reduced set of parameters, which can be easily determined experimentally. To validate our model, we test it against in silico simulations and experimental measurements of growth, and find good agreement. From a biological point of view, our model uncovers the ...

This paper attempts to review in how far thermodynamic analysis can be used to understand and predict the performance of microorganisms with respect to growth and bioproduct synthesis. In the first part, a simple thermodynamic model of microbial growth is developed which explains the relationship between the driving force for growth in terms of Gibbs energy dissipation and biomass yield. From the currently available literature, it appears that the Gibbs energy dissipation per C-mol of biomass grown, which represents the driving force for chemotrophic growth, may have been adapted by evolutionary processes to strike a reasonable compromise between metabolic rate and growth efficiency. Based on empirical correlations of the C-molar Gibbs energy dissipation, the wide variety of biomass yields observed in nature can be explained and roughly predicted. This type of analysis may be highly useful in environmental applications, where such wide variations occur. It is however not able to predict biomass yields in very complex systems such as mammalian cells nor is it able to predict or to assess bioproduct or recombinant protein yields. For this purpose, a much more sophisticated treatment that accounts for individual metabolic pathways separately is required. Based on glycolysis as a test example, it is shown in the last part that simple thermodynamic analysis leads to erroneous conclusions even in well-known, simple cases. Potential sources for errors have been analyzed and can be used to identify the most important needs for future research.

Applied Microbiology

A phenomenological model of planktonic microbial cultures, reported in previous papers, suggests that the whole growth progress seems planned by the microbial population since a pre-growth latency phase, during which the population level remains at its starting level. This model is in line with recent suggestions about the behavior of complex systems, as long as it allows for the gathering of the growth trends of a number of real batch cultures in a single master plot of reduced variables, in spite of their metabolic and physiological differences. One important issue of the model concerns the origin of the time scale for the microbes that can differ from that for the observer. The present paper reports some consequences of the model in view of its potential use in predictive microbiology and proposes an extension to the steady and decay phases of the culture evolution suggesting that, consistent with the assumptions about the growth phase, the decay occurs by a scan of the cell gene...

BMC Genomics, 2016

Background: In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels (e.g. gene expression, metabolism). When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now. Results: We used multi-step constraint-based metabolic modelling to simulate the growth in a complex medium over several time steps of the Antarctic model organism Pseudoalteromonas haloplanktis TAC125. As each of these phases is characterized by a specific set of amino acids to be used as carbon and energy source our modelling framework describes the major consequences of nutrients switching at the system level. The model predicts that a deep metabolic reprogramming might be required to achieve optimal biomass production in different stages of growth (different medium composition), with at least half of the cellular metabolic network involved (more than 50% of the metabolic genes). Additionally, we show that our modelling framework is able to capture metabolic functional association and/or common regulatory features of the genes embedded in our reconstruction (e.g. the presence of common regulatory motifs). Finally, to explore the possibility of a sub-optimal biomass objective function (i.e. that cells use resources in alternative metabolic processes at the expense of optimal growth) we have implemented a MOMA-based approach (called nutritional-MOMA) and compared the outcomes with those obtained with Flux Balance Analysis (FBA). Growth simulations under this scenario revealed the deep impact of choosing among alternative objective functions on the resulting predictions of fluxes distribution. Conclusions: Here we provide a time-resolved, systems-level scheme of PhTAC125 metabolic re-wiring as a consequence of carbon source switching in a nutritionally complex medium. Our analyses suggest the presence of a potential efficient metabolic reprogramming machinery to continuously and promptly adapt to this nutritionally changing environment, consistent with adaptation to fast growth in a fairly, but probably inconstant and highly competitive, environment. Also, we show i) how functional partnership and co-regulation features can be predicted by integrating multi-step constraint-based metabolic modelling with fed-batch growth data and ii) that performing simulations under a sub-optimal objective function may lead to different flux distributions in respect to canonical FBA.

Chemical Engineering and Processing: Process Intensification, 2008

In view of the prime importance of the biomass yield in microbial growth for research, for environmental and for industrial applications some thermodynamic concepts are reviewed that were developed over the years in order to understand the large variations observed in this parameter and to predict it. Special emphasis is given to a comparison between cellular growth and an energy transducer, which uses an input of chemical or mechanical energy to drive a useful output reaction. It turns out that it is impossible to calculate energetic efficiencies and to predict biomass yield based on correlations of the latter because the very concept is plagued with internal inconsistencies. Nevertheless, the energy transducer model reflects at least qualitatively the compromise between two extreme types of metabolism that must have emerged during evolution. On one hand, one can imagine that microbial metabolism could dissipate most of the Gibbs energy contained in the substrates for generating large driving forces and thus fast metabolism, but this would result in small biomass yields. On the other hand, metabolism could maximize biomass yield but this would minimize the chemical driving forces and thus the growth rate. It appears that a certain range of values of Gibbs energy dissipation somewhere between these two extremes may have emerged during evolution. Correlations for estimating this range of Gibbs energy dissipation for specific microbial growth system have been developed. Based on a Gibbs energy balance, the wide variety of growth yields occurring in nature may be explained and roughly predicted.

Microbial Growth Kinetics opens with a critical review of the history of microbial kinetics from the 19th century to the present day. The results of original investigations into the growth of microbes in both laboratory and natural environments are summarized. The book emphasizes the analysis of complex dynamic behavior of microbial populations. Non-steady states and unbalanced growth, multiple limitation, survival under starvation, differentiation, morphological variability, colony and biofilm growth, mixed cultures and microbial population dynamics in soil are all examined. Mathematical models are proposed which give mechanistic explanation to many features of microbial growth. The book takes general kinetic principles and their ecological applications and presents them in a way specifically designed for the microbiologist. This in itself unusual, but taken with the book's fascinating historical overview and the many fresh and sometimes controversial ideas expressed, this book is a must for all advanced students of microbiology and researchers in microbial ecology and growth. Nicolai Panikov is a lecturer in the