Visualizing regulatory interactions in metabolic networks

<p>Abstract</p> <p>Background</p> <p>Direct visualization of data sets in the context of biochemical network drawings is one of the most appealing approaches in the field of data evaluation within systems biology. One important type of information that is very helpful in interpreting and understanding metabolic networks has been overlooked so far. Here we focus on the representation of this type of information given by the strength of regulatory interactions between metabolite pools and reaction steps.</p> <p>Results</p> <p>The visualization of such interactions in a given metabolic network is based on a novel concept defining the regulatory strength (RS) of effectors regulating certain reaction steps. It is applicable to any mechanistic reaction kinetic formula. The RS values are measures for the strength of an up- or down-regulation of a reaction step compared with the completely non-inhibited or non-activated state, respectively. One numerical RS value is associated to any effector edge contained in the network. The RS is approximately interpretable on a percentage scale where 100% means the maximal possible inhibition or activation, respectively, and 0% means the absence of a regulatory interaction. If many effectors influence a certain reaction step, the respective percentages indicate the proportion in which the different effectors contribute to the total regulation of the reaction step. The benefits of the proposed method are demonstrated with a complex example system of a dynamic <it>E. coli </it>network.</p> <p>Conclusion</p> <p>The presented visualization approach is suitable for an intuitive interpretation of simulation data of metabolic networks under dynamic as well as steady-state conditions. Huge amounts of simulation data can be analyzed in a quick and comprehensive way. An extended time-resolved graphical network presentation provides a series of information about regulatory interaction within the biological system under investigation.</p

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed

Selecting high-confidence predictions from ordinary differential equation models of biological networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2008.Includes bibliographical references (p. 139-153).Many cellular processes are governed by large and highly-complex networks of chemical interactions and are therefore difficult to intuit. Computational modeling provides a means of encapsulating information about these interactions and can serve as a platform for gaining understanding of the biology and making predictions about cellular response to perturbation. In particular, there has been considerable interest in ordinary differential equation (ODE) models, which have several attractive features: ODEs can describe molecular interactions with mechanistic detail, it is relatively straightforward to implement perturbations, and, in theory, they can predict the concentration and activity of every species as a function of time. However, both the topology and parameters in such models are subject to considerable uncertainty. We explore the ramifications of these sources of uncertainty for making accurate predictions and develop methods of selecting high confidence predictions from uncertain models. In particular, we promote a shift in emphasis from model selection to prediction selection, and use consensus among model ensembles to identify the predictions most likely to be accurate. By constructing decision trees, this consensus can also be used to partition the space of potential perturbations into regions of high and low confidence. We apply our methods to the Fas signaling pathway in apoptosis to satisfy two goals: first, to design a therapeutic cocktail to reduce cell death in the presence of high levels of stimulus, and second, to design experiments that may lead to a better understanding of the biological network.by Caitlin Anne Bever.Ph.D

Metabolic network activity characterization using mass spectrometric methods

Innovative methods were developed for metabolic network activity characterization using mass spectrometry. Metabolic flux analysis (MFA) and kinetics of metabolic networks were developed and applied to Corynebacterium glutamicum. A protocol to determine metabolic fluxes at low degree of labelling using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) by the measurement of 13C enrichment in proteinogenic amino acid hydrolyzates was described. Kinetic isotope effects played an increasing role at low degree of labeling but could be corrected. From these corrected 13C enrichments In vivo fluxes in the central metabolism were determined by numerical optimization. The GC-C-IRMS-based method involving low labeling degree of expensive tracer substrate, e.g. 1%, is therefore promising for larger laboratory and industrial pilot scale fermentations. Permeabilization of Corynebacterium glutamicum cells was investigated and optimized. Permeabilized cells are considered closer to the in-vivo situation than purified enzyme(s) for the study of kinetics. A novel strategy was developed for the determination of in-situ enzymatic network kinetics combining permeabilization and matrix-assisted laser desorption/ionization time-of—flight mass spectrometry (MALDI-TOF-MS) quantification. Quantification of small molecular mass metabolites in glycolysis and pentose-phosphate pathway using MALDI-TOF-MS with [U-13C6] glucose-6-phosphate as single internal standard was established. Signal suppression during MALDI analysis could be compensated by applying the standard addition method. Adding selected substrates and cofactors, kinetics of glycolysis and pentose-phosphate pathways were be characterized using this method.Im Rahmen dieser Arbeit wurden neue Massenspektrometrie-basierte Methoden zur Charakterisierung der Aktivität metabolischer Netzwerke entwickelt, die zur Flussanalyse in metabolischen Netzwerken sowie zur Analyse der Kinetik metabolischer Netzwerke angewendet wurden. Die Methode zur Bestimmung metabolischer Flüsse basiert auf der Messung der 13C-Anreicherung in Aminosäuren von Proteinhydrolysaten mit Hilfe von GC-C-IRMS (gas-chromatography-combustion-isotope ratio mass spectrometry). Der Vorteil dieser Methode besteht darin, dass die Bestimmung metabolischer Flüsse auch bei sehr geringen Mengen an 13C-markiertem Substrat möglich ist. Durch Messung der 13C-Anreicherung in Aminosäuren und Korrektur von Isotopen-Effekten sowie durch Anpassung der korrigierten Daten mit Hilfe numerischer Optimierungen, konnten in vivo Flussverteilungen im Zentralstoffwechsel bestimmt werden. Da bei der GC-C-IRMS basierten Methode nur sehr geringe Mengen an relativ teurem 13C markiertem Substrat benötigt werden (~1%), ist diese Methodik insbesondere für die Anwendung in größerem Maßstab geeignet. Des Weiteren wurden Techniken zur Permeabiliserung von Corynebacterium glutamicum untersucht und optimiert. Generell sind permeabilisierte Zellen zur Bestimmung von in vivo Enzymkinetiken besser geeignet als isolierte und gereinigte Enzyme. Zur in situ Bestimmung von Kinetiken in enzymatischen Netzwerken wurde in dieser Arbeit eine Methode entwickelt, bei der die Enzymaktivität in permeabilisierten Zellen bestimmt wird und des Weiteren eine MALDI-TOF-MS-basierte Quantifizierung von intrazellulären Metaboliten erfolgt. Die Quantifizierung von Metaboliten der Glykolyse und des Pentosephosphat-Wegs mittels MALDI-TOF-MS erfolgte mit Hilfe von [U-13C6] Glukose-6-Phosphat als internem Standard. Die bei der MALDI-Messung auftretenden Signal-Unterdrückungen konnten durch Zugabe des Standards korrigiert werden. Durch Messung der entsprechenden Metabolite sowie durch Bestimmung von intrazellulären Enyzmaktivitäten mit Hilfe geeigneter Substrate und Kofaktoren, konnten die Kinetiken von Glykolyse sowie des Pentose-Phosphatweg erfolgreich charakterisiert werden

Experiment design for systems biology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 219-233).Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. Despite the growing interest in these models, a number of challenges frustrate the construction of high-quality models. First, the chemical reactions that control biochemical processes are only partially known, and multiple, mechanistically distinct models often fit all of the available data and known chemistry. We address this by providing methods for designing dynamic stimuli that can distinguish among models with different reaction mechanisms in stimulus-response experiments. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. Inspired by these computational results, we tested the idea that pulses of EGF could help elucidate the relative contribution of different feedback loops within the EGFR network. These experimental results suggest that models from the literature do not accurately represent the relative strength of the various feedback loops in this pathway. In particular, we observed that the endocytosis and feedback loop was less strong than predicted by models, and that other feedback mechanisms were likely necessary to deactivate ERK after EGF stimulation. Second, chemical kinetic models contain many unknown parameters, at least some of which must be estimated by fitting to time-course data. We examined this question in the context of a pathway model of EGF and neuronal growth factor (NGF) signaling. Computationally, we generated a palette of experimental perturbation data that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, we identified a set of five complementary experiments that could. These results suggest that there is reason to be optimistic about the prospects for parameter estimation in even large models. Third, there is no standard formulation for chemical kinetic models of biological signaling. We propose a general and concise formulation of mass action kinetics based on sparse matrices and Kronecker products. This formulation allows any mass action model and its partial derivatives to be represented by simple matrix equations, which enabled straightforward application of several numerical methods. We show that models that use other rate laws such as MichaelisMenten can be converted to our formulation. We demonstrate this by converting a model of Escherichia coli central carbon metabolism to use only mass action kinetics. The dynamics of the new model are similar to the original model. However, we argue that because our model is based on fewer approximations it has the potential to be more accurate over a wider range of conditions. Taken together, the work presented here demonstrates that experimental design methodology can be successfully used to improve the quality of mechanism-based chemical kinetic models.by Joshua Farley Apgar.Ph.D

Investigating the dynamic behavior of biochemical networks using model families

Supporting the evolutionary modeling process of dynamic biochemical networks based on sampled in vivo data requires more than just simulation. In the course of the modeling process, the modeler is typically concerned not only with a single model but also with sequences, alternatives and structural variants of models. Powerful automatic methods are then required to assist the modeler in the organization and the evaluation of alternative models. Moreover, the structure and peculiarities of the data require dedicated tool support.To support all stages of an evolutionary modeling process, a new general formalism for the combinatorial specification of large model families is introduced. It allows for automatic navigation in the space of models and excludes biologically meaningless models on the basis of elementary flux mode analysis. An incremental usage of the measured data is supported by using splined data instead of state variables. With MMT2, a versatile tool has been developed as a computational engine intended to be built into a tool chain. Using automatic code generation, automatic differentiation for sensitivity analysis and grid computing technology, a high performance computing environment is achieved. MMT2 supplies XML model specification and several software interfaces. The performance of MMT2 is illustrated by several examples from ongoing research projects.http://www.simtec.mb.uni-siegen.de/[email protected]