Biomedical subjects

A Kremling

Publications and source records attributed to A Kremling.

9 recordsLinked to original sources

PubMed2007

Comment on mathematical models which describe transcription and calculate the relationship between mRNA and protein expression ratio.

Mathematical models to describe transcription (Arnold et al. (2001); Biotech Bioeng 72:548-561) and translation (Mehra et al. (2003); Biotech Bioeng 84:822-841) in bacteria are modified in order to improve reaction kinetics and to include the number of polymerase molecules that are active on the DNA, as well as to include the number of ribosomes that are active on the nascent and on the completed mRNA, respectively.

Bacteria↗

PubMed2006

Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems.

A quantitative description of dynamical systems requires the estimation of uncertain kinetic parameters and an analysis of their precision. A method frequently used to describe the confidence intervals of estimated parameters is based on the Fisher-Information-Matrix. The application of this traditional method has two important shortcomings: (i) it gives only lower bounds for the variance of a parameter if the solution of the underlying model equations is non-linear in parameters. (ii) The resulting confidence interval is symmetric with respect to the estimated parameter. Here, we show that by applying the bootstrap method a better approximation of (possibly) asymmetric confidence intervals for parameters could be obtained. In contrast to previous applications devoted to non-parametric problems, a dynamical model describing a bio-chemical network is used to evaluate the method.

Algorithms↗

PubMed2004

Time hierarchies in the Escherichia coli carbohydrate uptake and metabolism.

The analysis of metabolic pathways with mathematical models contributes to the better understanding of the behavior of metabolic processes. This paper presents the analysis of a mathematical model for carbohydrate uptake and metabolism in Escherichia coli. It is shown that the dynamic processes cover a broad time span from some milliseconds to several hours. Based on this analysis the fast processes could be described with steady-state characteristic curves. A subsequent robustness analysis of the model parameters shows that the fast part of the system may act as a filter for the slow part of the system; the sensitivities of the fast system are conserved. From these findings it is concluded that the slow part of the system shows some robustness against changes in parameters of the fast subsystem, i.e. if a parameter shows no sensitivity for the fast part of the system, it will also show no sensitivity for the slow part of the system.

Escherichia coli↗

PubMed2004

Analysis of two-component signal transduction by mathematical modeling using the KdpD/KdpE system of Escherichia coli.

A mathematical model for the KdpD/KdpE two-component system is presented and its dynamical behavior is analyzed. KdpD and KdpE regulate expression of the kdpFABC operon encoding the high affinity K+ uptake system KdpFABC of Escherichia coli. The model is validated in a two step procedure: (i) the elements of the signal transduction part are reconstructed in vitro. Experiments with the purified sensor kinase and response regulator in presence or absence of DNA fragments comprising the response regulator binding-site are performed. (ii) The mRNA and molecule number of KdpFABC are determined in vivo at various extracellular K+ concentrations. Based on the identified parameters for the in vitro system it is shown, that different time hierarchies appear which are used for model reduction. Then the model is transformed in such a way that a singular perturbation problem is formulated. The analysis of the in vivo system shows that the model can be separated into two parts (submodels which are called functional units) that are connected only in a unidirectional way. Hereby one submodel represents signal transduction while the second submodel describes the gene expression.

Base Sequence↗

PubMed2003

Modular modeling of cellular systems with ProMoT/Diva.

MOTIVATION: Need for software to setup and analyze complex mathematical models for cellular systems in a modular way, that also integrates the experimental environment of the cells. RESULTS: A computer framework is described which allows the building of modularly structured models using an abstract, modular and general modeling methodology. With this methodology, reusable modeling entities are introduced which lead to the development of a modeling library within the modeling tool ProMot. The simulation environment Diva is used for numerical analysis and parameter identification of the models. The simulation environment provides a number of tools and algorithms to simulate and analyze complex biochemical networks. The described tools are the first steps towards an integrated computer-based modeling, simulation and visualization environment Availability: Available on request to the authors. The software itself is free for scientific purposes but requires commercial libraries. SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/promot

Algorithms↗

PubMed2003

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

Database Management Systems↗

PubMed2001

The organization of metabolic reaction networks. II. Signal processing in hierarchical structured functional units.

Based on the analysis of molecular interactions of proteins with DNA binding sites, a new approach to developing mathematical models describing gene expression is introduced. Detection of hierarchical structures in metabolic networks can be used to decompose complex reaction schemes. This will be achieved by assigning each regulator protein to one level in the hierarchy. Signals are then transduced from the top level to the lower level, but not vice versa. The method is shown by a simple example with two interacting proteins. A comparison of simulation results shows good agreement between a model taking all interactions into account and a model developed with the new approach. Finally, the method is applied to the crpA modulon in Escherichia coli, which controls uptake and metabolism for a number of carbohydrates. Here, RNA polymerase represents the top level, CrpA the second level, and the lactose-specific repressor LacI the lowest level, respectively. Besides the lactose operon, the method is applied to the adenylate cyclase gene and the gene for the regulator CrpA.

Adenosine Monophosphate↗

PubMed2001

The organization of metabolic reaction networks. III. Application for diauxic growth on glucose and lactose.

A mathematical model to describe carbon catabolite repression in Escherichia coli is developed and in part validated. The model is aggregated from two functional units describing glucose and lactose transport and degradation. Both units are members of the crp modulon and are under control of a global signal transduction system which calculates the signals that turn on or off gene expression for the specific enzymes. Using isogenic mutant strains, our model is validated by a set of experiments. In these experiments, substrate composition of the preculture and of the experimental culture are varied in order to stimulate the system in different ways. With the obtained measurements (three states in the liquid phase and one intracellular component) a part of the model parameters could be estimated. Therefore all experiments could be sufficiently described with a single set of parameters.

Biological Transport↗

PubMed2000

The organization of metabolic reaction networks: a signal-oriented approach to cellular models.

Complex metabolic networks are characterized by a great number of elements and many regulatory loops. The description of these networks with mathematical models requires the definition of functional units that group together several cellular processes. The approach presented here is based on the idea that cellular functional units may be assigned directly to mathematical modeling objects. Because the proposed modeling objects have defined inputs and outputs, they can be connected with other modeling objects until eventually the whole metabolism is covered. This modular approach guarantees a high transparency for biologists as well as for engineers. Three criteria are introduced to demarcate functional units. The criteria consider the physiological pathways, the organization of the corresponding genes, and the observation that cellular systems can be structured into units showing a hierarchy of signal transduction and processing. As an example, the carbon catabolic reactions in Escherichia coli are discussed as members of a functional unit catabolism.

Bacterial Proteins↗