The potential relevance for improving bone formation in and around tissue-engineered constructs. The objectives of this study were to investigate erosion and release of biologically active protein from CAP/PF-127 blends in an environment more representative of local growth factor delivery into a low clearance site. Within this study, effects of polymer blend ratio, sampling interval, and multilayering were examined with the ultimate goal of obtaining multiphasic release profiles over a period of several days.

Association polymers for modulated release of bioactive proteins

Stochastic effects in biochemical networks can affect the functioning of these systems significantly. Signaling pathways, such as calcium signal transduction, are particularly prone to random fluctuations. Thus, an important question is how this influences the information transfer in these pathways. First, a comprehensive overview and systematic classification of stochastic simulation methods is given as methodical basis for the thesis. Here, the focus is on approximate and hybrid approaches. Also, the hybrid solver in the software system Copasi is described whose implementation was part of this PhD work. Then, in most cases, the dynamic behavior of biochemical systems shows a transition from stochastic to deterministic behavior with increasing particle numbers. This transition is studied in calcium signaling as well as other test systems. It turns out that the onset of stochastic effects is very dependent on the sensitivity of the specific system quantified by its divergence. Systems with high divergence show stochastic effects even with high particle numbers and vice versa. Finally, the influence of noise on the performance of signaling pathways is investigated. Simulated and experimentally measured calcium time series are stochastically coupled to an intracellular target enzyme activation process. Then, the information transfer under different cellular conditions is estimated with the information-theoretic quantity transfer entropy. The amount of information that can be transferred increases with rising particle numbers. However, this increase is very dependent on the current dynamical mode of the system, such as spiking, bursting or irregular oscillations. The methods developed in this thesis, such as the use of the divergence as an indicator for the transition from stochastic to deterministic behavior or the stochastic coupling and information-theoretic analysis using transfer entropy, are valuable tools for the analysis of biochemical systems.

Stochastic simulation and analysis of biochemical networks

The topology of signaling cascades has been studied in quite some detail. However, how information is processed exactly is still relatively unknown. Since quite diverse information has to be transported by one and the same signaling cascade (e.g. in case of different agonists), it is clear that the underlying mechanism is more complex than a simple binary switch which relies on the mere presence or absence of a particular species. Therefore, finding means to analyze the information transferred will help in deciphering how information is processed exactly in the cell. Using the information-theoretic measure transfer entropy, we studied the properties of information transfer in an example case, namely calcium signaling under different cellular conditions. Transfer entropy is an asymmetric and dynamic measure of the dependence of two (nonlinear) stochastic processes. We used calcium signaling since it is a well-studied example of complex cellular signaling. It has been suggested that specific information is encoded in the amplitude, frequency and waveform of the oscillatory Ca2+-signal. We set up a computational framework to study information transfer, e.g. for calcium signaling at different levels of activation and different particle numbers in the system. We stochastically coupled simulated and experimentally measured calcium signals to simulated target proteins and used kernel density methods to estimate the transfer entropy from these bivariate time series. We found that, most of the time, the transfer entropy increases with increasing particle numbers. In systems with only few particles, faithful information transfer is hampered by random fluctuations. The transfer entropy also seems to be slightly correlated to the complexity (spiking, bursting or irregular oscillations) of the signal. Finally, we discuss a number of peculiarities of our approach in detail. This study presents the first application of transfer entropy to biochemical signaling pathways. We could quantify the information transferred from simulated/experimentally measured calcium signals to a target enzyme under different cellular conditions. Our approach, comprising stochastic coupling and using the information-theoretic measure transfer entropy, could also be a valuable tool for the analysis of other signaling pathways.

/pdf/information-transfer-in-signaling-pathways-a-study-using-3hladkwlse.pdf

Information transfer in signaling pathways: A study using coupled simulated and experimental data

/pdf/the-energetic-heart-biolectromagnetic-interactions-within-5amaowa9mj.pdf

The Energetic Heart: Biolectromagnetic Interactions Within and Between People

In a variety of cell types extracellular hormonal stimuli varying in time are transfered across the cell membrane into repetitive spikes of the intracellular calcium concentration ([Ca2+]i) Distinct temporal patterns of [Ca2+]i spikes are capable of regulating the function and structure of target cells Here, we investigate the ability of transmembrane signaling to encode time-varying hormonal stimulations (bandlimited Gaussian white noise) in a model of receptor-controlled [Ca2+]i oscillations The encoding of hormonal signals in [Ca2+]i spike trains is quantified by using an information-theoretic approach allowing to estimate the hormonal stimulus from [Ca2+]i spike trains Our results suggest that intracellular [Ca2+]i spike trains convey faithful information on temporal variations of extracellular hormonal concentrations at scales of 30-200 sec, corresponding to cut-off frequencies between 5 and 30 mHz of the random hormonal stimulation

Coding of time-varying hormonal signals in intracellular calcium spike trains.

Most hormones are secreted in an pulsatile rather than in a constant manner. This temporal pattern of pulsatile hormone release plays an important role in the regulation of cellular function and structure. In healthy humans growth hormone (GH) secretion is characterized by distinct pulses whereas patients bearing a GH producing tumor accompanied with excessive secretion (acromegaly) exhibit a highly irregular pattern of GH release. It has been hypothesized that this highly disorderly pattern of GH release in acromegaly arises from random events in the GH-producing tumor under decreased normal control of GH secretion. Using a context-free grammar complexity measure (algorithmic complexity) in conjunction with random surrogate data sets we demonstrate that the temporal pattern of GH release in acromegaly is not significantly different from a variety of stochastic processes. In contrast, normal subjects clearly exhibit deterministic structure in their temporal patterns of GH secretion. Our results support the hypothesis that GH release in acromegaly is due to random events in the GH-producing tumorous cells which might become independent from hypothalamic regulation. 17 refs., 1 fig., 2 tabs.

Algorithmic complexity of growth hormone release in humans.

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Coding of time-varying hormonal signals in intracellular calcium spike trains.

Algorithmic complexity of growth hormone release in humans.