Derivation of a biomass proxy for dynamic analysis of whole genome metabolic models

A whole genome metabolic model (GEM) is essentially a reconstruction of a network of enzyme-enabled chemical reactions representing the metabolism of an organism, based on information present in its genome. Such models have been designed so that flux balance analysis (FBA) can be applied in order to analyse metabolism under steady state. For this purpose, a biomassfunctionisaddedtothesemodelsasanoverallindicatorofthemodel’s viability. Our objective is to develop dynamic models based on these FBA models in order to observe new and complex behaviours, including transient behaviour. There is however a major challenge in that the biomass function does not operate under dynamic simulation. An appropriate biomass function would enable the estimation under dynamic simulation of the growth of both wildtype and genetically modified bacteria under different, possibly dynamically changing growth conditions. Using data analytics techniques, we have developed a dynamic biomass function which acts as a faithful proxy for the FBA equivalent for a reduced GEM for E. coli. This involved consolidating data for reaction rates and metabolite concentrations generated under dynamic simulation with gold standard target data for biomass obtained by steady state analysis using FBA. It also led to a number of interesting insights regarding biomass fluxes for pairs of conditions. These findings were reproduced in our dynamic proxy function

Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools

Stoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell.Published versio

Relationship between fitness and heterogeneity in exponentially growing microbial populations

Despite major environmental and genetic differences, microbial metabolic networks are known to generate consistent physiological outcomes across vastly different organisms. This remarkable robustness suggests that, at least in bacteria, metabolic activity may be guided by universal principles. The constrained optimization of evolutionarily motivated objective functions, such as the growth rate, has emerged as the key theoretical assumption for the study of bacterial metabolism. While conceptually and practically useful in many situations, the idea that certain functions are optimized is hard to validate in data. Moreover, it is not always clear how optimality can be reconciled with the high degree of single-cell variability observed in experiments within microbial populations. To shed light on these issues, we develop an inverse modeling framework that connects the fitness of a population of cells (represented by the mean single-cell growth rate) to the underlying metabolic variability through the maximum entropy inference of the distribution of metabolic phenotypes from data. While no clear objective function emerges, we find that, as the medium gets richer, the fitness and inferred variability for Escherichia coli populations follow and slowly approach the theoretically optimal bound defined by minimal reduction of variability at given fitness. These results suggest that bacterial metabolism may be crucially shaped by a population-level trade-off between growth and heterogeneity

Fundamental Principles in Bacterial Physiology - History, Recent progress, and the Future with Focus on Cell Size Control: A Review

Bacterial physiology is a branch of biology that aims to understand overarching principles of cellular reproduction. Many important issues in bacterial physiology are inherently quantitative, and major contributors to the field have often brought together tools and ways of thinking from multiple disciplines. This article presents a comprehensive overview of major ideas and approaches developed since the early 20th century for anyone who is interested in the fundamental problems in bacterial physiology. This article is divided into two parts. In the first part (Sections 1 to 3), we review the first `golden era' of bacterial physiology from the 1940s to early 1970s and provide a complete list of major references from that period. In the second part (Sections 4 to 7), we explain how the pioneering work from the first golden era has influenced various rediscoveries of general quantitative principles and significant further development in modern bacterial physiology. Specifically, Section 4 presents the history and current progress of the `adder' principle of cell size homeostasis. Section 5 discusses the implications of coarse-graining the cellular protein composition, and how the coarse-grained proteome `sectors' re-balance under different growth conditions. Section 6 focuses on physiological invariants, and explains how they are the key to understanding the coordination between growth and the cell cycle underlying cell size control in steady-state growth. Section 7 overviews how the temporal organization of all the internal processes enables balanced growth. In the final Section 8, we conclude by discussing the remaining challenges for the future in the field.Comment: Published in Reports on Progress in Physics. (https://doi.org/10.1088/1361-6633/aaa628) 96 pages, 48 figures, 7 boxes, 715 reference

Temporal segregation of biosynthetic processes is responsible for metabolic oscillations during the budding yeast cell cycle

Many cell biological and biochemical mechanisms controlling the fundamental process of eukaryotic cell division have been identified; however, the temporal dynamics of biosynthetic processes during the cell division cycle are still elusive. Here, we show that key biosynthetic processes are temporally segregated along the cell cycle. Using budding yeast as a model and single-cell methods to dynamically measure metabolic activity, we observe two peaks in protein synthesis, in the G1 and S/G2/M phase, whereas lipid and polysaccharide synthesis peaks only once, during the S/G2/M phase. Integrating the inferred biosynthetic rates into a thermodynamic-stoichiometric metabolic model, we find that this temporal segregation in biosynthetic processes causes flux changes in primary metabolism, with an acceleration of glucose-uptake flux in G1 and phase-shifted oscillations of oxygen and carbon dioxide exchanges. Through experimental validation of the model predictions, we demonstrate that primary metabolism oscillates with cell-cycle periodicity to satisfy the changing demands of biosynthetic processes exhibiting unexpected dynamics during the cell cycle.</p

Towards a non-equilibrium thermodynamic theory of ecosystem assembly and development

Non-equilibrium thermodynamics has had a significant historic influence on the development of theoretical ecology, even informing the very concept of an ecosystem. Much of this influence has manifested as proposed extremal principles. These principles hold that systems will tend to maximise certain thermodynamic quantities, subject to the other constraints they operate under. A particularly notable extremal principle is the maximum entropy production principle (MaxEPP); that systems maximise their rate of entropy production. However, these principles are not robustly based in physical theory, and suffer from treating complex ecosystems in an extremely coarse manner. To address this gap, this thesis derives a limited but physically justified extremal principle, as well as carrying out a detailed investigation of the impact of non-equilibrium thermodynamic constraints on the assembly of microbial communities. The extremal principle we obtain pertains to the switching between states in simple bistable systems, with switching paths that generate more entropy being favoured. Our detailed investigation into microbial communities involved developing a novel thermodynamic microbial community model, using which we found the rate of ecosystem development to be set by the availability of free-energy. Further investigation was carried out using this model, demonstrating the way that trade-offs emerging from fundamental thermodynamic constraints impact the dynamics of assembling microbial communities. Taken together our results demonstrate that theory can be developed from non-equilibrium thermodynamics, that is both ecologically relevant and physically well grounded. We find that broad extremal principles are unlikely to be obtained, absent significant advances in the field of stochastic thermodynamics, limiting their applicability to ecology. However, we find that detailed consideration of the non-equilibrium thermodynamic mechanisms that impact microbial communities can broaden our understanding of their assembly and functioning.Open Acces

A combined modelling and experimental characterisation of Chlamydomonas reinhardtii under monochromatic LED illumination

Industrial biotechnology is currently synonymous with heterotrophic processes that rely on bacterial, yeast, insect or mammalian cells to biosynthesise products of interest. Microalgae are of substantial biotechnological interest due their polyphyletic nature which grants them access to a wide array of high-value metabolites and their ability to grow under a variety of trophic strategies, including phototrophy. Despite significant process development and optimisation efforts, the full potential of these photosynthetic organisms has yet to be realised. One of the most impactful process parameters when cultivating microalgae is light. It is essential for phototrophic growth and remains highly influential on mixotrophic growth. Indoor cultivations relying on artificial light allow full control of illumination conditions. The advent of LED lights has lowered the costs and improved the flexibility of such installations. Specifically, the spectral composition of LED lights can be accurately and dynamically tailored to the needs of the culture. Spectral composition is known to exert regulatory control over the cell cycle and can affect the cell’s biochemical make up. The effects of illumination strategy on the model microalgae Chlamydomonas reinhardtii were characterised at three different levels (a) growth kinetics, (b) biochemical composition and, (c) transcriptional activity at key carbon nodes. To obtain the transcriptional data, RNA extraction protocols were compared and optimised. Additionally, a suite of candidate reference genes was validated to ensure accurate gene expression normalisation was possible in reverse transcriptase quantitative real-time polymerase chain reaction (RT-qPCR) studies. The growth kinetics and biochemical composition data obtained served as inputs for a previously published genome scale metabolic model. An algorithm was developed to approximate the default biomass composition in the model to experimental data in an effort to increase the fidelity of the simulations. The flux distributions obtained thereafter helped to describe the distinct metabolic fingerprints created under different trophic and illumination strategies

Allometric scaling and metabolic ecology of microorganisms and major evolutionary transitions

My dissertation centers around investigating big-picture questions related to understanding the consequences of metabolism and energetics on the evolution, ecology, and physiology of life. The evolutionary transitions from prokaryotes to unicellular eukaryotes to multicellular organisms were accompanied by major innovations in metabolic design. In my first chapter, I show that the scaling of metabolic rate, population growth rate, and production efficiency with body size have changed across these transitions. Metabolic rate scales with body mass superlinearly in prokaryotes, linearly in protists, and sublinearly in metazoans, so Kleibers 3/4 power scaling law does not apply universally across organisms. This means that major changes in metabolic processes during the early evolution of life overcame existing physical constraints, exploited new opportunities, and imposed new constraints on organism physiology. Surface areas of physiological structures of organisms impose fundamental constraints on metabolic rate. In my second chapter, I demonstrate that organisms have a variety of options for increasing the scaling of the area of their metabolic surfaces with body sizes. I develop models and examples illustrating the role of cell membrane elaborations, mitochondria, vacuoles, vesicles, inclusions, and shape-shifting in the architectural design, evolution, and ecology of unicellular microbes. I demonstrate how these surface-area scaling adaptations have played important roles in the evolution of major biological designs of cells and the physiological ecology of organisms. In my third and final chapter, I integrate and synthesize findings from the previous two chapters with important developments in geochemistry, microbiology, and astrobiology in order to identify the fundamental physical and biological dimensions that characterize a metabolic theory of ecology of microorganisms. These dimensions are thermodynamics, chemical kinetics, physiological harshness, cell size, and levels of biological organization. I show how addressing these dimensions can inform understanding of the physical and biological factors governing the metabolic rate, growth rate, and geographic distribution of cells. I propose a unifying theory to understand how the major ecological and evolutionary transitions that led to increases in levels of organization of life, such as endosymbiosis, multicellularity, eusociality, and multi-domain complexes, influences the metabolism and growth and the metabolic scaling of these complexes

Elementary approaches to microbial growth rate maximisation

This thesis, called Elementary approaches to microbial growth rate maximisation, reports on a theoretical search for principles underlying single cell growth, in particular for microbial species that are selected for fast growth rates. First, the optimally growing cell is characterised in terms of its elementary modes. We prove an extremum principle: a cell that maximises a metabolic rate uses few Elementary Flux Modes (EFMs, the minimal pathways that support steady-state metabolism). The number of active EFMs is bounded by the number of growth-limiting constraints. Later, this extremum principle is extended in a theory that explicitly accounts for self-fabrication. For this, we had to define the elementary modes that underlie balanced self-fabrication: minimal self-supporting sets of expressed enzymes that we call Elementary Growth Modes (EGMs). It turns out that many of the results for EFMs can be extended to their more general self-fabrication analogue. Where the above extremum principles tell us that few elementary modes are used by a rate-maximising cell, it does not tell us how the cell can find them. Therefore, we also search for an elementary adaptation method. It turns out that stochastic phenotype switching with growth rate dependent switching rates provides an adaptation mechanism that is often competitive with more conventional regulatory-circuitry based mechanisms. The derived theory is applied in two ways. First, the extremum principles are used to review the mathematical fundaments of all optimisation-based explanations of overflow metabolism. Second, a computational tool is presented that enumerates Elementary Conversion Modes. These elementary modes can be computed for larger networks than EFMs and EGMs, and still provide an overview of the metabolic capabilities of an organism