Frank Johannes Bruggeman

• PhD
• Chair at Vrije Universiteit Amsterdam

Cell-to-cell variability in the molecular composition of isogenic, steady-state growing cells arises spontaneously from the inherent stochasticity of intracellular biochemical reactions and cell growth. Here, we present a general decomposition of the total variance in the copy number per cell of a particular molecule. It quantifies the individual c...

Specific product formation rates and cellular growth rates are important maximization targets in biotechnology and microbial evolution. Maximization of a specific rate (i.e. a rate expressed per unit biomass amount) requires the expression of particular metabolic pathways at optimal enzyme concentrations. In contrast to the prediction of maximal pr...

Introduction Cells use multilayered regulatory systems to respond adequately to changing environments or perturbations. Failure in regulation underlies cellular malfunctioning, loss of fitness, or disease. How molecular components dynamically interact to give rise to robust and adaptive responses is not well understood. Here, we studied how the mod...

Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity i...

A central focus in studies of microbial communities is the elucidation of the relationships between genotype, phenotype, and dynamic community structure. Here, we present a new computational method called community flux balance analysis (cFBA) to study the metabolic behavior of microbial communities. cFBA integrates the comprehensive metabolic capa...

Systems biologists have been working out the contours of a general theory of microbial physiology for the last two decades, guided by experimental data. At its foundations lie basic principles from evolutionary biology, enzyme biochemistry, cellular metabolism, cellular composition, and steady-state cell growth. The theory makes predictions about f...

Living cells can express different metabolic pathways that support growth. The criteria that determine which pathways are selected in which environment remain unclear. One recurrent selection is overflow metabolism: the simultaneous usage of an ATP-efficient and -inefficient pathway, shown for example in Escherichia coli, Saccharomyces cerevisiae a...

In this paper we try to describe all possible molecular states (phenotypes) for a cell that fabricates itself at a constant rate, given its enzyme kinetics and the stoichiometry of all reactions. For this, we must understand the process of cellular growth: steady-state self-fabrication requires a cell to synthesize all of its components, including...

Background A central theme in (micro)biology is understanding the molecular basis of fitness i.e. which strategies are successful under which conditions; how do organisms implement such strategies at the molecular level; and which constraints shape the trade-offs between alternative strategies. Highly standardized microbial laboratory evolution exp...

The cAMP-PKA signalling cascade in budding yeast regulates adaptation to changing environments. Many questions remain about the function of cAMP dynamics, largely because no robust method for in vivo cAMP measurements exists for yeast. Here we developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We show that this biosensor can be...

An open problem in biology is to understand when particular adaptation strategies of microorganisms are selected during evolution. They range from random, bet-hedging strategies to deterministic, responsive strategies, relying on signalling circuits. We present an evolutionary model that integrates basic statistical physics of molecular circuits wi...

In each environment, living cells can express different metabolic pathways that support growth. The criteria that determine which pathways are selected remain unclear. One recurrent selection is overflow metabolism: the seemingly wasteful, simultaneous usage of an efficient and an inefficient pathway, shown for example in E. coli, S. cerevisiae and...

Single, isogenic cells can differ in their survival and adaptation capacity. This phenotypic diversity is generally due to stochastic molecular events. Since mother cells on average pass half of their molecular content on to their daughters, the states of progeny cells strongly correlate with that of mother cells (Lamarckian inheritance). Why a par...

A major aim of biology is to predict phenotype from genotype. Here we ask if we can describe all possible molecular states (phenotypes) for a cell that fabricates itself at a constant rate, given its enzyme kinetics and the stoichiometry of all reactions (the genotype). For this, we must understand the autocatalytic process of cellular growth which...

The physical biology of G protein-coupled receptor (GPCR) signalling can be inferred from imaging of single molecules and single living cells. In this opinion paper, we highlight recent developments in technologies to study GPCR signalling in vitro and in cyto. We start from mobility and localisation characteristics of single receptors in membranes...

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rat...

Kinetic model of overflow metabolism. The Matlab-code used for modeling overflow metabolism is attached in a compressed folder as a supplement. In the compressed folder, we have also added a text-file with instructions. (ZIP)

Supplementary text containing general derivations and modelled examples. In this text we present our results in full mathematical detail. The harder mathematical steps are illustrated with figures. (PDF)

Core model of L. lactis switch. In this short text we analyse a core model of overflow metabolism in L. lactis using the extremum principle. Code for running the model is also provided. (PDF)

Data analysis coconsumption experiment. All raw data and the Matlab-code used for data analysis can be found in the compressed folder attached to the supplements. (ZIP)

Kinetic model of L. lactis. The Matlab-code used for the kinetic model of L. lactis is attached in a compressed folder as a supplement. In the compressed folder, we have also added a text-file with instructions. (ZIP)

Core model of overflow metabolism. In this short text we analyse a core model of overflow metabolism using the extremum principle. Code for running the model is also provided. (PDF)

Co-consumption of substrates. In this short text we first explain how we found EFMs that simultaneously take up several carbon sources in a genome-scale model. We then present the method and results of our own experiments on co-consumption. (PDF)

Finding coconsumption EFMs. The Python and Matlab-code used for finding co-consuming EFMs are attached in a compressed folder as a supplement. In the compressed folder, we have also added a text-file with instructions. (ZIP)

Estimated uptake rates co-consumption experiments. Shown are the estimated uptake rates (mean and standard deviation) of different carbon sources (normalized for initial concentration) on the different growth media. The letters that indicate the conditions denote the available carbon sources in the medium: S = Succinate, L = maLtose, M = Mannose, X...

Growth rates co-consumption experiments. Estimated growth rates from separate biological replicates. (TXT)

Substrate concentrations co-consumption experiments. For all different growth media, we include an excell-sheet. Shown are the measured concentrations of carbon sources (normalized for initial concentration), with the corresponding Optical Density (OD). The letters that indicate the conditions denote the available carbon sources in the medium: S =...

The growth rate of single bacterial cells is continuously disturbed by random fluctuations in biosynthesis rates and by deterministic cell-cycle events, such as division, genome duplication, and septum formation. It is not understood whether, and how, bacteria reject these disturbances. Here we quantified growth and constitutive protein expression...

One of the marvels of biology is the phenotypic plasticity of microorganisms. It allows them to maintain high growth rates across conditions. Studies suggest that cells can express metabolic enzymes at tuned concentrations through adjustment of gene expression. The associated transcription factors are often regulated by intracellular metabolites. H...

Extreme robustness: A simulation for the same pathway as in Fig 3 in the main text, but now with minimal initial conditions: At the start, enzymes are completely absent, and all internal metabolites except the sensor are absent. The pathway is still steered to the optimal specific flux steady state. A: the pathway; B: metabolite concentrations over...

With initial conditions for the sensor concentration such that they actually predict an optimal flow from end to beginning rather than the reverse, the predicted optimum needs to ‘straighten out’, and move through a singular point: Thermodynamic equilibrium. Although the requirements for sensor control are not upheld in this point, the predicted op...

King-Altman patterns of an ordered M − K mechanism. The left one shows the master pattern, the middle and right figure show two alternative patterns that yield the constant term in the denominator of f. (TIF)

An example of qORAC steering for an changing internal parameter, showing lack of convergence to the optimum. A: The pathway, which is identical to that in Fig 5 in the main text—only the choice of sensors (in blue) is different. Sensor x3 is swapped with x4. B/C: The dynamics of metabolites (B) and predicted K3 values (C) do start to change. Howeve...

Supporting information text in which we prove that the optimisation problem (8) has a unique solution for a large class of reaction kinetics. We also give a detailed explanation which and how many sensor metabolites may be used in qORAC. We prove that many pathways with qORAC control only have one steady state, the actual optimum. We also give addi...

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rat...

In single cells, all molecules fluctuate in concentration because synthesis and degradation events occur asynchronously in a probabilistic manner. Since molecules generally influence multiple reactions, concentration fluctuations can propagate through the entire molecular circuit of a cell. This causes single isogenic cells to vary in their phenoty...

Yeast glycolysis has been the focus of research for decades, yet a number of dynamical aspects of yeast glycolysis remain poorly understood at present. If nutrients are scarce, yeast will provide its catabolic and energetic needs with other pathways, but the enzymes catalysing upper glycolytic fluxes are still expressed. We conjecture that this ove...

Microbes may maximize the number of daughter cells per time or per amount of nutrients consumed. These two strategies correspond, respectively, to the use of enzyme-efficient or substrate-efficient metabolic pathways. In reality, fast growth is often associated with wasteful, yield-inefficient metabolism, and a general thermodynamic trade-off betwe...

Supplementary text containing Figures 1–30, Tables 1–10, and a list of supplementary data files available on GitHub. (PDF)

Protein expression in a single cell depends on its global physiological state. Moreover, genetically-identical cells exhibit variability (noise) in protein expression, arising from the stochastic nature of biochemical processes, cell growth and division. While it is well understood how cellular growth rate influences mean protein expression, little...

The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth i...

Natural selection has shaped the strategies for survival and growth of microorganisms. The success of microorganisms depends not only on slow evolutionary tuning but also on the ability to adapt to unpredictable changes in their environment. In principle, adaptive strategies range from purely deterministic mechanisms to those that exploit the rando...

An important challenge in microbial ecology is to infer metabolic-exchange fluxes between growing microbial species from community-level data, concerning species abundances and metabolite concentrations. Here we apply a model-based approach to integrate such experimental data and thereby infer metabolic-exchange fluxes. We designed a synthetic anae...

Parameters used for simulation of the co-culture. (PDF)

(PDF)

The dFBA simulations, with the draft genome-scale metabolic model of W. succinogenes, also agree mostly with the experimental data for the four different cultivation conditions. The metabolite profile plots contain error bars, but the other subplots not. The biomass ratios are based on the gene-copy data and the carbon balance consisted of the meas...

Draft genome-scale metabolic model of W. succinogenes. The reactions and the corresponding reversibility and bounds are listed in ?reactions?. The metabolites-id and the corresponding names are listed in ?metabolites?. All reactions and their corresponding substrates and products are listed in ?network_react?, where in ?network_metab? all reactions...

A coarse-grained metabolic model of W. succinogenes was detailed enough to fit the experimental data. All metabolites, except for H+ and H2O, are element-balanced in the metabolic model. (PDF)

Product and substrate yields did not change during different growth rates. The extracted specific uptake and production rates (mmol/(gDW?h)) are plotted against the simulated growth rate ? (h-1). All lines are lineair what suggests that the specific uptake and production rates of both species are scaled with the growth. The N2 condition is not plot...

Inference of the metabolic interactions using a draft genome-scale metabolic model of W. succinogenes showed only differences in the NO2- production and NO3- consumption per utilized H2. In A the calculated flux-values for C. acetobutylicum are normalized for the glucose uptake. B shows the calculated flux-values for W. succinogenes and are normali...

Many evolutionarily successful bacteria attain high growth rates across growth-permissive conditions. They express metabolic networks that synthesise all cellular components at a high rate. Metabolic reaction rates are bounded by the concentration of the catalysing enzymes and cells have finite resources available for enzyme synthesis. Therefore, b...

The growth rate and physiology of photoautotrophic bacteria are dependent on the incident light color and intensity. Here we report a widely applicable and straightforward method for light-limited batch cultivation of phototrophic bacteria at different, yet constant, growth rates. We illustrate its usage with Synechocystis sp. PCC6803, a model cyan...

Non-dividing Saccharomyces cerevisiae cultures are highly relevant for fundamental and applied studies. However, cultivation conditions in which non-dividing cells retain substantial metabolic activity are lacking. Unlike stationary-phase (SP) batch cultures, the current experimental paradigm for non-dividing yeast cultures, cultivation under extre...

Microbial communities are ubiquitously found in Nature and have direct implications for the environment, human health and biotechnology. The species composition and overall function of microbial communities are largely shaped by metabolic interactions such as competition for resources and cross-feeding. Although considerable scientific progress has...

Protein expression is shaped by evolutionary processes that tune microbial fitness. The limited biosynthetic capacity of a cell constrains protein expression and forces the cell to carefully manage its protein economy. In a chemostat, the physiology of the cell feeds back on the growth conditions, hindering intuitive understanding of how changes in...

Mechanistic models and explanations are becoming increasingly important in molecular biology as it is slowly maneuvering itself to studying the properties of the molecular networks that we find inside cells in addition to studying the properties of single molecules and genes. The explanation of “phenomena” generated by systems of interacting molecu...

Microbial growth can be characterized by a limited set of macroscopic parameters such as growth rate, biomass yield and substrate affinity. Different culturing protocols for laboratory evolution have been developed to select mutant strains that have one specific macroscopic growth parameter improved. Some of those mutant strains display tradeoffs b...

The signal-transduction network of a mammalian cell integrates internal and external cues in order to initiate adaptive responses. Amongst the cell-surface receptors are the G protein-coupled receptors (GPCRs) that have remarkable signal-integrating capabilities. Binding of extracellular signals stabilises intracellular-domain conformations that se...

Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism,...

Microorganisms rely on binding-protein assisted, active transport systems to scavenge for scarce nutrients. Several advantages of using binding proteins in such uptake systems have been proposed. However, a systematic, rigorous and quantitative analysis of the function of binding proteins is lacking. By combining knowledge of selection pressure and...

High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important insights into the metabolic capacities of a cell. How...

Author Summary Transcription initiation is an important process that contributes to determining mRNA and eventually protein levels. In multicellular organisms transcription activity of a single gene is regulated by many different signals. This leads to multiple transcription factors binding to the same promoter. Here we study fundamental aspects of...

Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give r...

Maximisation of growth rate is an important fitness strategy for bacteria. Bacteria can achieve this by expressing proteins at optimal concentrations, such that resources are not wasted. This is exemplified for Escherichia coli by the increase of its ribosomal protein-fraction with growth rate which precisely matches the increased protein synthesis...

After more than a century of research on glycolysis, we have detailed descriptions of its molecular organization, but despite this wealth of knowledge, linking the enzyme properties to metabolic pathway behavior remains challenging. These challenges arise from multi-layered regulation and the context and time dependence of component functions. Howe...

Transcriptional stochasticity can be measured by counting the number of mRNA molecules per cell. Cell-to-cell variability is best captured in terms of concentrations rather than molecule counts, because reaction rates depend on concentrations. We combined single-molecule mRNA counting with single-cell volume measurements to quantify the statistics...

Changes in transcription factor levels, epigenetic status, splicing kinetics and mRNA degradation can each contribute to changes in the mRNA dynamics of a gene. We present a novel method to identify which of these processes is changed in cells in response to external signals or as a result of a diseased state. The method employs a mathematical mode...

The activity of a single gene is influenced by the composition of the chromatin in which it is embedded. Nucleosome turnover, conformational dynamics, and covalent histone modifications each induce changes in the structure of chromatin and its affinity for regulatory proteins. The dynamics of histone modifications and the persistence of modificatio...

Individual cells respond very differently to changes in environmental conditions. Stochasticity causes cells to respond at different times, magnitudes or both. Here we disentangle and quantify these two sources of heterogeneity. We track the adaptation dynamics of single Saccharomyces cerevisiae cells exposed to a nutrient shift from methionine to...

G protein-coupled receptors (GPCRs) are a versatile, important class of cell-surface receptors. GPCRs occur in different conformations that exist in a dynamic ligand-sensitive equilibrium. These conformations vary in their affinities for intracellular signalling proteins and initiate signalling via different intracellular routes. The binding of ext...

Signal transduction by prokaryotes almost exclusively relies on two-component systems for sensing and responding to (extracellular) signals. Here, we use stochastic models of two-component systems to better understand the impact of stochasticity on the fidelity and robustness of signal transmission, the outcome of autoregulatory gene expression and...

Flux balance analysis (FBA) is one of the most often applied methods on genome-scale metabolic networks. Although FBA uniquely determines the optimal yield, the pathway that achieves this is usually not unique. The analysis of the optimal-yield flux space has been an open challenge. Flux variability analysis is only capturing some properties of the...

In the model eukaryote Saccharomyces cerevisiae, it has long been known that a functional trehalose pathway is indispensable for transitions to high glucose conditions. Upon addition of glucose, cells with a defect in trehalose 6-phosphate synthase (Tps1), the first committed step in the trehalose pathway, display what we have termed an imbalanced...

Data integration is a central activity in systems biology. The integration of genomic, transcript, protein, metabolite, flux, and computational data yields unprecedented information about the system level functioning of organisms. Often, data integration is done purely computationally, leaving the user with little insight besides statistical inform...

Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematic...

Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic plasticity and robustness, and the ability of organis...