Computation Of Microbial Ecosystems in Time and Space (COMETS): An open source collaborative platform for modeling ecosystems metabolism

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are also emerging as a valuable avenue for predicting, understanding and designing microbial communities. COMETS (Computation Of Microbial Ecosystems in Time and Space) was initially developed as an extension of dynamic flux balance analysis, which incorporates cellular and molecular diffusion, enabling simulations of multiple microbial species in spatially structured environments. Here we describe how to best use and apply the most recent version of this platform, COMETS 2, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, as well as several new biological simulation modules, including evolutionary dynamics and extracellular enzyme activity. COMETS 2 provides user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, and comprehensive documentation and tutorials, facilitating the use of COMETS for researchers at all levels of expertise with metabolic simulations. This protocol provides a detailed guideline for installing, testing and applying COMETS 2 to different scenarios, with broad applicability to microbial communities across biomes and scales.Comment: 146 pages, 12 figures, 2 supplementary figures, 3 supplementary video

Constrained Allocation Flux Balance Analysis

New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an "ensemble averaging" procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws.Comment: 21 pages, 6 figures (main) + 33 pages, various figures and tables (supporting); for the supplementary MatLab code, see http://tinyurl.com/h763es

Overview of mathematical approaches used to model bacterial chemotaxis II: bacterial populations

We review the application of mathematical modeling to understanding the behavior of populations of chemotactic bacteria. The application of continuum mathematical models, in particular generalized Keller–Segel models, is discussed along with attempts to incorporate the microscale (individual) behavior on the macroscale, modeling the interaction between different species of bacteria, the interaction of bacteria with their environment, and methods used to obtain experimentally verified parameter values. We allude briefly to the role of modeling pattern formation in understanding collective behavior within bacterial populations. Various aspects of each model are discussed and areas for possible future research are postulated

A novel approach to dynamic flux balance analysis that accounts for the dynamic transfer of information by internal metabolites

Understanding the dynamics of information feedback amongst components of complex biological systems is crucial to the success of engineering desirable metabolic phenotypes. Flux Balance Analysis (FBA) is a structural metabolic modelling procedure that allows for local topological constraints to be related to steady-state global behaviors of metabolic systems. A vast majority of biological systems of interest, such as microbial communities, however do not exist under steady-state conditions. Therefore, extending FBA methods to the dynamical setting has been a major challenge to metabolic modelling. In dynamic FBA (dFBA), the representation of feedback dynamics is made possible by combining the methods of FBA with those of Ordinary Differential Equations (ODE). Although numerous dFBA models have been constructed to date, very little effort has gone into the theoretical analysis of how static FBA models and dynamic ODE models should be combined in dFBA. To develop a better understanding of the mathematical structure of dFBA, we investigate the properties of FBA. In order to predict time-derivatives of population growth, every dFBA model must make the assumption that the underlying metabolic network modeled via FBA optimizes a phenotypic function of growth rate. We show however, that under certain circumstances, this requirement introduces a rigid correspondence between growth rate, and a related quantity, the growth yield. The consequence of this is that the dFBA models become rigid in its predictions, effectively becoming a near-static representation of metabolism. In this thesis, we show that this tight correspondence between yield and rate may be broken by combining two inversely related approaches to formulating the FBA problem

Genome-scale metabolic modeling of cyanbacteria: network structure, interactions, reconstruction and dynamics

2016 Fall.Includes bibliographical references.Metabolic network modeling, a field of systems biology and bioengineering, enhances the quantitative predictive understanding of cellular metabolism and thereby assists in the development of model-guided metabolic engineering strategies. Metabolic models use genome-scale network reconstructions, and combine it with mathematical methods for quantitative prediction. Metabolic system reconstructions, contain information on genes, enzymes, reactions, and metabolites, and are converted into two types of networks: (i) gene-enzyme-reaction, and (ii) reaction-metabolite. The former details the links between the genes that are known to code for metabolic enzymes, and the reaction pathways that the enzymes participate in. The latter details the chemical transformation of metabolites, step by step, into biomass and energy. The latter network is transformed into a system of equations and simulated using different methods. Prominent among these are constraint-based methods, especially Flux Balance Analysis, which utilizes linear programming tools to predict intracellular fluxes of single cells. Over the past 25 years, metabolic network modeling has had a range of applications in the fields of model-driven discovery, prediction of cellular phenotypes, analysis of biological network properties, multi-species interactions, engineering of microbes for product synthesis, and studying evolutionary processes. This thesis is concerned with the development and application of metabolic network modeling to cyanobacteria as well as E. coli. Chapter 1 is a brief survey of the past, present, and future of constraint-based modeling using flux balance analysis in systems biology. It includes discussion of (i) formulation, (ii) assumption, (iii) variety, (iv) availability, and (v) future directions in the field of constraint based modeling. Chapter 2, explores the enzyme-reaction networks of metabolic reconstructions belonging to various organisms; and finds that the distribution of the number of reactions an enzyme participates in, i.e. the enzyme-reaction distribution, is surprisingly similar. The role of this distribution in the robustness of the organism is also explored. Chapter 3, applies flux balance analysis on models of E. coli, Synechocystis sp. PCC6803, and C. reinhardtii to understand epistatic interactions between metabolic genes and pathways. We show that epistatic interactions are dependent on the environmental conditions, i.e. carbon source, carbon/oxygen ratio in E. coli, and light intensity in Synechocystis sp. PCC6803 and C. reinhardtii. Cyanobacteria are photosynthetic organisms and have great potential for metabolic engineering to produce commercially important chemicals such as biofuels, pharmaceuticals, and nutraceuticals. Chapter 4 presents our new genome scale reconstruction of the model cyanobacterium, Synechocystis sp. PCC6803, called iCJ816. This reconstruction was analyzed and compared to experimental studies, and used for predicting the capacity of the organism for (i) carbon dioxide remediation, and (ii) production of intracellular chemical species. Chapter 5 uses our new model iCJ816 for dynamic analysis under diurnal growth simulations. We discuss predictions of different optimization schemes, and present a scheme that qualitatively matches observations

The physical, environmental, and evolutionary determinants of biological architecture

Thesis (Ph. D. in Physical Biology)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 201-224).The relationship between structure and function is a longstanding and central topic in biology, evolution, and ecology. The importance of morphology is clearly visible in the diverse forms taken by innumerable organisms in order to perform a myriad of functions. Examining the great variety of morphological characteristics it would seem that the overall principle of evolution is the only way to generalize the observed diversity: given differences in environments and random biological variation a great multitude of body plans have been invented as adaptations to many dynamic habitats given specific evolutionary histories. In this thesis I will show how focusing on diverse organisms makes it possible to identify common first-order laws of evolutionary organization. More specifically I will show how these common laws derive from a connection between organism structure, physical limitations, environmental constraints, and basic metabolic, biochemical, or energetic principles. Furthermore, I will show how this top level of biological organization holds significant predictive power for regional ecology and for interpreting the general trends of evolutionary history. In Chapter 2 we begin by deriving a model for the growth of single cells and populations of cells. This model is based on the partitioning of metabolic resources and the scaling relationship between metabolism and body size. We show that the growth of diverse classes of organisms is connected by common unit energetics. However there exist striking differences in the broad trends between growth rate and body size across these different classes and we show that this is a consequence of major evolutionary transitions which adjust the partitioning of metabolic resources. We interpret major evolutionary transitions to occur in response to energetic limitations. We also find that multicellular living for unicellular organisms provides a metabolic and reproductive advantage. In Chapters 3 and 4 we further investigate these features in microbial biofilms which exhibit rich spatial patterning. Using a mathematical model and experimentation we find that the tall vertical structures produced by these biofilms have optimal geometry for resource uptake and the growth efficiency of the entire colony. Our model allows us to predict the observed changes in feature geometry given alterations to the environmental conditions that the biofilms are grown in. Furthermore, we are able to show that the morphology of these structures is dependent on single cell physiology. For example, single genetic knockouts of flagellar motility radically alter the temporal dynamics of feature spacing. Our work highlights morphology as a central property in multicellular organisms which mediates the interaction between environmental conditions and physiology. In Chapter 5 we highlight the importance of morphology in complex multicellular life where we develop a general model of tree architecture which we link to physiological success within a given environment. Although this model is general, uses only tree size as a governing parameter, and does not consider speciation we are able use local resource availability to predict broad regional patterns in plant traits such as maximum tree height. Each of these chapters highlights the importance of structure and morphology at multiple biological scales. In Chapter 6 we show how the importance of structure extends to the genetic level where the specific encoding of a gene can have implicit information and functionality beyond the basic translation of codons. We investigate the observed implicit function of dramatic and frequent changes in the mutation rate of an organism given the structure of the mutL gene. We show mathematically that altering mutation rates is an evolutionarily advantageous strategy, and we show bioinformatically that the specific genetic structure that gives rise to this trait is under positive evolutionary selection.by Christopher Andrew Poling Kempes.Ph.D.in Physical Biolog

Genome-Scale Metabolic Modeling of Chitin-Degrading Microbial Systems

As a major component of fungal cell walls and exoskeletons of invertebrates, chitin is widespread in soils, constituting the second most abundant biopolymer in nature. Composed of N-acetyl-D-glucosamine chains, it serves as a vital source of nutrients, including both carbon and nitrogen, for the growth of microorganisms. A solid understanding of the microbial degradation of chitin is critical for predicting their impacts on biogeochemical cycling in soil ecosystems. Organisms that degrade biopolymers (degraders) produce energetically expensive extracellular enzymes to break down complex organic carbons into simpler labile forms that are sharable with other species, including those that do not contribute directly to the degradation process (cheaters). Therefore, it impacts not only the metabolic and growth efficiencies of the degraders but also fosters diverse interspecies interactions within microbial communities. The level of complexity in this process necessitates the use of mechanistic metabolic models. However, reconstruction of phenotype-consistent genome-scale metabolic networks is still challenging due to the frequent occurrence of false positives (model prediction of biomass production in media where actual organism cannot grow) when gapfilled using typical sequential gapfilling approaches. In this work, I developed a new iterative gapfilling method to address this issue and applied it to build metabolic networks of chitin-degrading communities and their isolates—using a consortium of Cellvibrio japonicus (degrader) and Escherichia coli (non-degrader) as a model system. This new development revealed previously unknown and interesting findings on how bioenergetic cost on chitin degradation affects degrader’s metabolism and its interactions with non-degraders. The model also provided mechanistic interpretations of the predicted changes in metabolism and interactions based on carbon and nitrogen use efficiencies. Both the methods and findings are reproducible, and may be used in other biopolymer-degrading communities

Segregated modeling and selection of populations for polyhydroxyalkanoate production by mixed microbial cultures

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Química e Bioquímic

Understanding and Engineering Metabolic Feedback Regulation of Amino Acid Metabolism in Escherichia coli

Metabolism is the core of what we consider to be a living cell. It covers all chemical reactions that are necessary to break down nutrients and convert them into energy and cellular building blocks for growth. These chemical reactions comprise a large metabolic network that is subject to tight feedback-regulation of enzyme activities or abundances. However, even in intensively studied model organisms like Escherichia coli, the knowledge about the function of feedback-regulatory mechanisms and how they interact to control metabolism is still sparse. Therefore, the first goal of this study was to understand the function and relevance of metabolic feedback regulation using amino acid metabolism in E. coli as a case study. The second goal was to use the knowledge about metabolic feedback regulation to engineer microbial cell factories for the production of amino acids like L-arginine. In Chapter 1 we constructed a panel of 7 mutants with allosterically dysregulated amino acid pathways to uncover the relevance and function of allosteric feedback inhibition in vivo, which was so far only demonstrated by theoretical studies. By combining metabolomics, proteomics and flux profiling we could show that allosteric feedback inhibition is crucial to adjust a reserve of biosynthetic enzymes. Such enzyme overabundance originates from a sensitive interaction between control of enzyme activity (allosteric feedback inhibition) and enzyme abundance (transcriptional regulation). Furthermore, we used a metabolic model and CRISPR interference experiments to show that enzyme overabundance renders cells more robust against genetic perturbations. In Chapter 2 we increased fitness of a rationally engineered arginine overproduction strain by leaving a certain level of transcriptional regulation. Therefore, we titrated the transcription factor ArgR by CRISPRi and compared this different level of transcriptional regulation with an ArgR knockout strain. Using the CRISPRi approach we elevated the growth rates of an overproduction strain by two-fold compared to the knockout strain, without impairing arginine production rates and titer. Metabolomics and proteomics experiments revealed that slow growth of the knockout strain derives from limitations in pyrimidine nucleotide metabolism and that these limitations are caused by imbalances of enzyme level at critical branching points. Thus, we demonstrated the importance of balancing enzymes in an overproduction pathway and that CRISPRi is a suitable tool for this purpose. In Chapter 3 we show how cells respond to genetic perturbation on the molecular scale. Therefore, we perturbed amino acid biosynthesis genes with CRISPRi and analyzed the transcriptional response with GFP-reporter plasmids and proteomics. These experiments revealed that cells elevate the expression of genes in a perturbed pathway to counteract a genetic perturbation (We will refer to this mechanism as transcriptional compensation). Metabolomics and flow cytometry data of the wild-type and the allosteric mutant demonstrated the benefit of enzyme overabundance in response to genetic perturbations: Cells without overabundance showed a heterogenic transcriptional compensation even to mild perturbations, whereas in wild-type cells such mild perturbations were buffered by enzyme overabundance. In Chapter 4 we consider amino acid degradation pathways as an additional regulatory mechanism for the maintenance of end-product homeostasis Nutritional downshift experiments revealed increased robustness of allosteric mutants in which the respective degradation pathway was up-regulated. By dynamic metabolite measurements we showed that E. coli channels an excess of arginine into the degradation pathway. This overflow mechanism might be the reason for the robustness of allosteric mutants under dynamic conditions