Negative auto-regulation increases the input dynamic-range of the arabinose system of Escherichia coli

<p>Abstract</p> <p>Background</p> <p>Gene regulation networks are made of recurring regulatory patterns, called network motifs. One of the most common network motifs is negative auto-regulation, in which a transcription factor represses its own production. Negative auto-regulation has several potential functions: it can shorten the response time (time to reach halfway to steady-state), stabilize expression against noise, and linearize the gene's input-output response curve. This latter function of negative auto-regulation, which increases the range of input signals over which downstream genes respond, has been studied by theory and synthetic gene circuits. Here we ask whether negative auto-regulation preserves this function also in the context of a natural system, where it is embedded within many additional interactions. To address this, we studied the negative auto-regulation motif in the arabinose utilization system of <it>Escherichia coli</it>, in which negative auto-regulation is part of a complex regulatory network.</p> <p>Results</p> <p>We find that when negative auto-regulation is disrupted by placing the regulator <it>araC </it>under constitutive expression, the input dynamic range of the arabinose system is reduced by 10-fold. The apparent Hill coefficient of the induction curve changes from about <it>n </it>= 1 with negative auto-regulation, to about <it>n </it>= 2 when it is disrupted. We present a mathematical model that describes how negative auto-regulation can increase input dynamic-range, by coupling the transcription factor protein level to the input signal.</p> <p>Conclusions</p> <p>Here we demonstrate that the negative auto-regulation motif in the native arabinose system of <it>Escherichia coli </it>increases the range of arabinose signals over which the system can respond. In this way, negative auto-regulation may help to increase the input dynamic-range while maintaining the specificity of cooperative regulatory systems. This function may contribute to explaining the common occurrence of negative auto-regulation in biological systems.</p

An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

<p>Abstract</p> <p>Background</p> <p>Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both <it>ex vivo </it>and <it>in vivo </it>neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors.</p> <p>Results</p> <p>In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale <it>ex vivo </it>cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in <it>ex vivo </it>neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR.</p> <p>Conclusions</p> <p>In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA.</p