Simulated complex dynamics of glycolysis in the protozoan parasite Trypanosoma brucei

The kinetic properties of hexokinases in African trypanosomes of the subgenus Trypanozoon match the blood glucose levels of mammal hosts

We hypothesize that the hexokinases of trypanosomes of the subgenus Trypanozoon match the blood glucose levels of hosts. We studied the kinetic properties of purified hexokinase in T. equiperdum (specific activity=302U/mg), and compare with other members of Trypanozoon. With ATP (K_m=104.7μM) as phosphate donor, hexokinase catalyzes the phosphorylation of glucose (K_m=24.9μM) and mannose (K_m=8.8μM). With respect to glucose, mannose and inorganic pyrophosphate respectively are a competitive, and a mixed inhibitor of hexokinase. With respect to ATP, both are mixed inhibitors of this enzyme. In T. equiperdum, hexokinase shows a high affinity for glucose. Pleomorphism-transformation of trypanosomes from a multiplicative to a non-multiplicative form-results in a self-limited growth stabilizing glucose consumption. It delays the death of the host, thus prolonging its exposure to tsetse flies. When glucose levels descend, top-down regulation allows trypanosomes to survive through the expression of alternative metabolic pathways. It accelerates the death of the host, but helps trypanosome density to increase enough to ensure transmission without tsetse flies. Pleomorphism, and a hexokinase with a high affinity for glucose, are two main adaptive traits of T. b. brucei. The latter trait, and a strong top-down regulation, are two main adaptive traits of T. equiperdum. For trypanosomes living in glucose-rich blood, a hexokinase with a high affinity for glucose would unnecessarily harm hosts. This may explain why the human parasites, T. b. gambiense and T. b. rhodesiense, possess hexokinases with a low affinity for glucose.

Purine metabolite and energy charge analysis of Trypanosoma brucei cells in different growth phases using an optimized ion-pair RP-HPLC/UV for the quantification of adenine and guanine pools

Human African Trypanosomiasis (HAT) is caused by the protozoan parasite Trypanosoma brucei. Although trypanosomes are well-studied model organisms, only little is known about their adenine and guanine nucleotide pools. Besides being building blocks of RNA and DNA, these nucleotides are also important modulators of diverse biochemical cellular processes. Adenine nucleotides also play an important role in the regulation of metabolic energy. The energetic state of cells is evaluated by the energy charge which gives information about how much energy is available in form of high energy phosphate bonds of adenine nucleotides. A sensitive and reproducible ion-pair RP-HPLC/UV method was developed and optimized, allowing the quantification of guanine and adenine nucleosides/nucleotides in T. brucei. With this method, the purine levels and their respective ratios were investigated in trypanosomes during logarithmic, stationary and senescent growth phases. Results of this study showed that all adenine and guanine purines under investigation were in the low mM range. The energy charge was found to decrease from logarithmic to static and to senescent phase whereas AMP/ATP, ADP/ATP and GDP/GTP ratios increased in the same order. In addition, the AMP/ATP ratio varied as the square of the ADP/ATP ratio, indicating AMP to be the key energy sensor molecule in trypanosomes.

Electrophysiological-metabolic modeling of microbes: applications in fuel cells and environment analysis

A formalism for simulating coupled metabolic and electrophysiological processes is presented. The resulting chemical kinetic and electrophysiological equations are solved numerically to create a cell simulator. Metabolic features of this simulator were adapted from Karyote, a multi-compartment biochemical cell modeling simulator. We present the mathematical formalism and its computational implementation as an integrated electrophysiological-metabolic model. Applications to Geobacter sulfurreducens in the environment and in a fuel cell are discussed.

Simulating microbial systems: addressing model uncertainty/incompleteness via multiscale and entropy methods

Most systems of interest in the natural and engineering sciences are multiscale in character. Typically available models are incomplete or uncertain. Thus, a probabilistic approach is required. We present a deductive multiscale approach to address such problems, focusing on virus and cell systems to demonstrate the ideas. There is usually an underlying physical model, all factors in which (e.g., particle masses, charges, and force constants) are known. For example, the underlying model can be cast in terms of a collection of N-atoms evolving via Newton's equations. When the number of atoms is 10(6) or more, these physical models cannot be simulated directly. However, one may only be interested in a coarse-grained description, e.g., in terms of molecular populations or overall system size, shape, position, and orientation. The premise of this chapter is that the coarse-grained equations should be derived from the underlying model so that a deductive calibration-free methodology is achieved. We consider a reduction in resolution from a description for the state of N-atoms to one in terms of coarse-grained variables. This implies a degree of uncertainty in the underlying microstates. We present a methodology for modeling microbial systems that integrates equations for coarse-grained variables with a probabilistic description of the underlying fine-scale ones. The implementation of our strategy as a general computational platform (SimEntropics™) for microbial modeling and prospects for developments and applications are discussed.

Kinetic Characterisation of Phosphofructokinase Purified from Setaria cervi: A Bovine Filarial Parasite

Phosphofructokinase (PFK), a regulatory enzyme in glycolytic pathway, has been purified to electrophoretic homogeneity from adult female Setaria cervi and partially characterized. For this enzyme, the Lineweaver-Burk's double reciprocal plots of initial rates and D-fructose-6-phosphate (F-6-P) or Mg-ATP concentrations for varying values of cosubstrate concentration gave intersecting lines indicating that K_m values for F-6-P (1.05 mM) and ATP (3 μM) were independent of each other. S. cervi PFK, when assayed at inhibitory concentration of ATP (>0.1 mM), exhibited sigmoidal behavior towards binding with F-6-P with a Hill coefficient (n) value equal to 1.8 and 1.7 at 1.0 and 0.33 mM ATP, respectively. D-fructose-1,6-diphosphate (FDP) competitively inhibited the filarial enzyme: K_i and Hill coefficient values being 0.18 μM and 2.0, respectively. Phosphoenolpyruvate (PEP) also inhibited the enzyme competitively with the K_i value equal to 0.8 mM. The Hill coefficient values (>1.5) for F-6-P (at inhibitory concentration of ATP) and FDP suggested its positive cooperative kinetics towards F-6-P and FDP, showing presence of more than one binding sites for these molecules in enzyme protein and allosteric nature of the filarial enzyme. The product inhibition studies gave us the only compatible mechanism of random addition process with a probable orientation of substrates and products on the enzyme surface.

Systems biology of microbial communities

Microbes exist naturally in a wide range of environments in communities where their interactions are significant, spanning the extremes of high acidity and high temperature environments to soil and the ocean. We present a practical discussion of three different approaches for modeling microbial communities: rate equations, individual-based modeling, and population dynamics. We illustrate the approaches with detailed examples. Each approach is best fit to different levels of system representation, and they have different needs for detailed biological input. Thus, this set of approaches is able to address the operation and function of microbial communities on a wide range of organizational levels.

In silico Geobacter sulfurreducens metabolism and its representation in reactive transport models

Microbial activity governs elemental cycling and the transformation of many anthropogenic substances in aqueous environments. Through the development of a dynamic cell model of the well-characterized, versatile, and abundant Geobacter sulfurreducens, we showed that a kinetic representation of key components of cell metabolism matched microbial growth dynamics observed in chemostat experiments under various environmental conditions and led to results similar to those from a comprehensive flux balance model. Coupling the kinetic cell model to its environment by expressing substrate uptake rates depending on intra- and extracellular substrate concentrations, two-dimensional reactive transport simulations of an aquifer were performed. They illustrated that a proper representation of growth efficiency as a function of substrate availability is a determining factor for the spatial distribution of microbial populations in a porous medium. It was shown that simplified model representations of microbial dynamics in the subsurface that only depended on extracellular conditions could be derived by properly parameterizing emerging properties of the kinetic cell model.

Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12

Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks.

Simulating cellular dynamics through a coupled transcription, translation, metabolic model

In order to predict cell behavior in response to changes in its surroundings or to modifications of its genetic code, the dynamics of a cell are modeled using equations of metabolism, transport, transcription and translation implemented in the Karyote software. Our methodology accounts for the organelles of eukaryotes and the specialized zones in prokaryotes by dividing the volume of the cell into discrete compartments. Each compartment exchanges mass with others either through membrane transport or with a time delay effect associated with molecular migration. Metabolic and macromolecular reactions take place in user-specified compartments. Coupling among processes are accounted for and multiple scale techniques allow for the computation of processes that occur on a wide range of time scales. Our model is implemented to simulate the evolution of concentrations for a user-specifiable set of molecules and reactions that participate in cellular activity. The underlying equations integrate metabolic, transcription and translation reaction networks and provide a framework for simulating whole cells given a user-specified set of reactions. A rate equation formulation is used to simulate transcription from an input DNA sequence while the resulting mRNA is used via ribosome-mediated polymerization kinetics to accomplish translation. Feedback associated with the creation of species necessary for metabolism by the mRNA and protein synthesis modifies the rates of production of factors (e.g. nucleotides and amino acids) that affect the dynamics of transcription and translation. The concentrations of predicted proteins are compared with time series or steady state experiments. The expression and sequence of the predicted proteins are compared with experimental data via the construction of synthetic tryptic digests and associated mass spectra. We present the mathematical model showing the coupling of transcription, translation and metabolism in Karyote and illustrate some of its unique characteristics.