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

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.

Reconstruction of biochemical networks in microorganisms

Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

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.

Isotopomer-based metabolomic analysis by NMR and mass spectrometry

Nuclear magnetic resonance (NMR) and mass spectrometry (MS) together are synergistic in their ability to profile comprehensively the metabolome of cells and tissues. In addition to identification and quantification of metabolites, changes in metabolic pathways and fluxes in response to external perturbations can be reliably determined by using stable isotope tracer methodologies. NMR and MS together are able to define both positional isotopomer distribution in product metabolites that derive from a given stable isotope-labeled precursor molecule and the degree of enrichment at each site with good precision. Together with modeling tools, this information provides a rich functional biochemical readout of cellular activity and how it responds to external influences. In this chapter, we describe NMR- and MS-based methodologies for isotopomer analysis in metabolomics and its applications for different biological systems.

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.

Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

Background

Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding.

Results

Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented.

Conclusion

Multiplex time series data can be used for the construction of the network of cellular processes and the calibration of the associated physicochemical parameters. We have demonstrated these concepts in the context of gene regulation understood through the analysis of gene expression microarray time series data. Casting the approach in a probabilistic framework has allowed us to address the uncertainties in gene expression microarray data. Our approach was found to be robust to error in the gene expression microarray data and mistakes in a proposed TRN.

Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project

Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.

A proposed role for all-trans retinal in regulation of rhodopsin regeneration in human rods

In order to account for the multi-phasic dynamics of photopigment regeneration in human rods, we developed a new model of the retinoid cycle. We first examined the relative roles of the classical and channeling mechanisms of metarhodopsin decay in establishing these dynamics. We showed that neither of these mechanisms alone, nor a linear combination of the two, can adequately account for the dynamics of rhodopsin regeneration at all bleach levels. Our new model adds novel inhibitory interactions in the cycle of regeneration of rhodopsin that are consistent with the 3D structure of rhodopsin. Our analyses show that the dynamics of human rod photopigment regeneration can be accounted for by end-product regulation of the channeling mechanism where all-trans retinal (tral) inhibits the binding of 11-cis retinal to the opsin.tral complex.

Experimental and Computational Analysis of Translation Products in Apomyoglobin Expression

This work focuses on the experimental analysis of the time-course of protein expression in a cell-free system, in conjunction with the development of a computational model, denoted as progressive chain buildup (PCB), able to simulate translation kinetics and product formation as a function of starting reactant concentrations. Translation of the gene encoding the apomyoglobin (apoMb) model protein was monitored in an Escherichia coli cell-free system under different experimental conditions. Experimentally observed protein expression yields, product accumulation time-course and expression completion times match with the predictions by the PCB model. This algorithm regards elementary single-residue elongations as apparent second-order events and it accounts for aminoacyl-tRNA regeneration during translation. We have used this computational approach to model full-length protein expression and to explore the kinetic behavior of incomplete chains generated during protein biosynthesis. Most of the observed incomplete chains are non-obligatory dead-end species, in that their formation is not mandatory for full-length protein expression, and that they are unable to convert to the expected final translation product. These truncated polypeptides do not arise from post-translational degradation of full-length protein, but from a distinct subpopulation of chains which expresses intrinsically more slowly than the population leading to full-length product. The PCB model is a valuable tool to predict full-length and incomplete chain populations and formulate experimentally testable hypotheses on their origin. PCB simulations are applicable to E.coli cell-free expression systems (both in batch and dialysis mode) under the control of T7 RNA polymerase and to other environments where transcription and translation can be regarded as kinetically decoupled.

A framework for whole-cell mathematical modeling

The default framework for modeling biochemical processes is that of a constant-volume reactor operating under steady-state conditions. This is satisfactory for many applications, but not for modeling growth and division of cells. In this study, a whole-cell modeling framework is developed that assumes expanding volumes and a cell-division cycle. A spherical newborn cell is designed to grow in volume during the growth phase of the cycle. After 80% of the cycle period, the cell begins to divide by constricting about its equator, ultimately affording two spherical cells with total volume equal to twice that of the original. The cell is partitioned into two regions or volumes, namely the cytoplasm (Vcyt) and membrane (Vmem), with molecular components present in each. Both volumes change during the cell cycle; Vcyt changes in response to osmotic pressure changes as nutrients enter the cell from the environment, while Vmem changes in response to this osmotic pressure effect such that membrane thickness remains invariant. The two volumes change at different rates; in most cases, this imposes periodic or oscillatory behavior on all components within the cell. Since the framework itself rather than a particular set of reactions and components is responsible for this behavior, it should be possible to model various biochemical processes within it, affording stable periodic solutions without requiring that the biochemical process itself generates oscillations as an inherent feature. Given that these processes naturally occur in growing and dividing cells, it is reasonable to conclude that the dynamics of component concentrations will be more realistic than when modeled within constant-volume and/or steady-state frameworks. This approach is illustrated using a symbolic whole cell model.

Simulated complex dynamics of glycolysis in the protozoan parasite Trypanosoma brucei

Glycolysis in Trypanosoma brucei was modeled using a reaction transport simulator and tested for possible complex dynamics. The glycolytic model is multi-compartmentalized and accounts for the exchange of metabolites between the glycosomes, cytosol, mitochondrion and the host medium. The model is used to examine the effects of a range of culture medium concentrations of oxygen on the glycolysis of T. brucei. Our results are in good agreement with steady-state experiments. We also find that under aerobic conditions, increasing the activity of glycerol-3-phosphate dehydrogenase induces complex dynamics in the system. We report the presence of three distinct types of these dynamics. Varying the oxygen concentration in the medium can induce the transition between these dynamics.