Dynamics matter: differences and similarities between alternatively designed mechanisms

Differential roles of transcriptional and translational negative autoregulations in protein dynamics

Cells continuously respond to stimuli to function properly by employing a wide variety of regulatory mechanisms that often involve protein up or down regulations. This study focuses on dynamics of a protein with negative autoregulations in E. coli, and assumes that the input signal up-regulates the protein, and then the protein down-regulates its own production via 2 distinct types of mechanisms. The mathematical models describe the dynamics of mRNA and protein for 3 scenarios: (i) a simplistic model with no regulation, (ii) a model with transcriptional negative autoregulation, and (iii) a model with translational negative autoregulation. Our analysis shows that the negative autoregulation models produce faster responses and quicker return times to the input signals compared to the model with no regulation, while the transcriptional autoregulation model is the only model capable of producing oscillatory dynamics. The stochastic simulations predict that the transcriptional autoregulation model is the noisiest followed by the simplistic model, and the translational autoregulation model has the least noise. The noise level depends on the strength of inhibition. Furthermore, the transcriptional autoregulation model filters out the noise in the input signal for longer periods of time, and this time increases as the strength of the feedback gets stronger.

Mathematical modeling reveals differential regulation of MAPK activity by phosphatase proteins in the yeast pheromone response pathway

To prevent indefinite cellular responses to external signals, cells utilize various adaptation mechanisms.

Mathematical modeling deciphers the benefits of alternatively-designed conserved activatory and inhibitory gene circuits

Cells employ a variety of mechanisms as a response to external signals to maintain cellular homeostasis. In this study, we examine four activatory and four inhibitory protein synthesis mechanisms at both population and single cell level that can be triggered by a transient external signal. Activation mechanisms result from the assumption that cells can employ four different modes to temporarily increase the levels of a protein: decreased mRNA degradation, increased mRNA synthesis, decreased protein degradation and increased protein synthesis. For the inhibition mechanisms it is assumed that a cell can reduce a protein's level through four ways: increased mRNA degradation, reduced mRNA synthesis, increased protein degradation and reduced protein synthesis. Deterministic and stochastic models were developed to analyze the dynamic responses of these eight mechanisms to a transient signal. Three different response metrics were used to measure different aspects of the response. These metrics are (i) mid-protein abundance (mP), (ii) time required for the protein to reach the mid-protein level (mT), and (iii) duration of response (D), which is defined as the total time for which the protein (P) abundance are above or below of mid-protein level. Our simulations show that of the activation mechanisms, the signal-dependent increase in mRNA synthesis and protein synthesis are more effective and faster, than the signal dependent decrease in mRNA and protein degradation. On the other hand, the mechanism involving signal dependent increase in protein synthesis is noisier than the signal dependent increase in mRNA synthesis in regard to all metrics used. Of the four inhibition mechanisms, the signal-dependent increase in the protein degradation is the most effective and fastest of the four inhibition mechanisms. It is also noisiest of the four inhibition mechanisms before the protein levels reach a steady state around 100 minutes.

Systems Epidemiology: What's in a Name?

Systems biology is an interdisciplinary effort to integrate molecular, cellular, tissue, organ, and organism levels of function into computational models that facilitate the identification of general principles. Systems medicine adds a disease focus. Systems epidemiology adds yet another level consisting of antecedents that might contribute to the disease process in populations. In etiologic and prevention research, systems-type thinking about multiple levels of causation will allow epidemiologists to identify contributors to disease at multiple levels as well as their interactions. In public health, systems epidemiology will contribute to the improvement of syndromic surveillance methods. We encourage the creation of computational simulation models that integrate information about disease etiology, pathogenetic data, and the expertise of investigators from different disciplines.