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Zhu et al. (2007), A theoretical study on activation of transcription factor modulated by intracellular Ca2+ oscillations.

April 2010, model of the month by Benedetta Frida Baldi
Original model: BIOMD0000000166

Calcium is a ubiquitous and fundamental messenger in a wide variety of cellular processes. Its concentration rise between a basal level of 100nM to 20μM, or more in specific restricted domain of cytosol. Calcium regulate different processes such as muscle contraction, fluid secretion in pancreatic tissue and in neurons through different gene transcription, can activate Long Term Potentiation (LTP) and Long Term Depression (LTD) that led to synaptic plasticity and learning memory.

A central question is how can just a single molecule, an ion, regulate so many different processes?

This versatility lies in the variability of calcium signaling mechanism in term of speed, amplitude and spatial-temporal patterning. Calcium signals are often seen to be propagated as waves, oscillations, spikes or puffs. Focusing on gene transcription regulations, it is known that the oscillatory behaviour of calcium contribute to the efficiency and specificity of signals. These repetitive calcium spikes were observed for the first time in 1987 in hepatocytes [1] and now have been observed in almost every cell type. The mechanism behind these waves and oscillatory propagation of calcium, is a positive feedback in which, an initial amount of calcium released into the cytosol in response to an external stimuli, induces the release of further calcium stored in the intracellular stores such as Endoplasmic Reticulum. This process is known as Calcium-Induced Calcium Release (CICR) [2]. Figure 1 (figure taken from SABioscience), represents the generic intracellular calcium signaling pathways.

Generic intracellular calcium pathways.

Figure 1: Generic intracellular calcium pathways. Figures taken from SABioscience

Figure 2

Figure 2: Schematic representation of the model. Figure taken from [4].

Experiment based on patch clamp techniques show, how at low level of stimulation, oscillations enhance the signaling efficiency and specificity. On the other side, at high level of stimulation, a small but constant increase in calcium concentration does not affect sensitivity and specificity either [3]. But, how fluctuations of signal can influence the activation of gene expression?

Zhu et al. ([4], BIOMD0000000166) in this paper, describes a simple and generic model (Figure 2), based on the model proposed by Smolen et al. [5] to address these questions. A generic transcriptional activator TF-A in its activated form, a bi-phosphorylated homodimer, binds the gene tf-a to its responsive element (TF-RE) and activates transcription. The amount of phosphorylated dimer depends on the activity of kinases and phosphatases whose activities depend on the external stimuli (β). The stimulus also induces an influx of calcium from the extracellular space to the cytosol. The oscillation is due to the balance between the free calcium in the cytosol, the calcium in the inositol tri-phosphate (IP3) sensitive pool and the one activated by calcium itself (CICR).

The deterministic simulation of this model shows that, the oscillation of intracellular calcium concentration can decrease the threshold for the activation of gene expression (as shown in Figure 3), affecting the efficiency of the system. While increasing the oscillation period of intracellular calcium led to a decrease of transcription factor activation as shown in Figure 4.

The stochastic simulation of the model shows a “zero-order ultra-sensitivity” relation between the average of transcription (average of TF-A) and the average of calcium concentration. Taking into account different cell volume size (Ω) the average level of TF-A jumps from a low level to a high value at a critical average of intracellular calcium concentration (Figure 5). Thus large noise or small cell volume can enhance gene expression efficiency, reducing the threshold of the average calcium concentration.

Figure 3

Figure 3: Average activation of TF-A (X) versus the cytosolic calcium concentration (Z). The solid line represent an increased mean level of calcium oscillation and the dotted line represent an equivalent continuous increase. The simulation is preformed using two different values for TF-A phosphorylation rate (Kfo). Figure taken from [4].

Figure 4

Figure 4: Average activation of TF-A (X) versus the period of cytosolic calcium concentration (Period of Z). The simulation is performed using two different values for the TF-A phosphorylation rate (kf0). Figure taken from [4].

Figure 5

Figure 5: Steep sigmoidal nature of the relation between the average of TF-A (X) and average calcium concentration (Z) at different cell volume size (Ω). Figure taken from [4].

This model has pointed out the importance of fluctuation of cytosolic calcium concentration in the regulation of gene expression in a simple and generic way. Model like this one can be easily used as starting point for modelling more complexes and specific pathways.

Bibliographic References

  1. Dolmetsch RE, Xu K, Lewis RS. Calcium oscillations increase the efficiency and specificity of gene expression. Nature, 392(6679): 933-6, 1998. [CiteXplore]
  2. Roderick HL, Berridge MJ, Bootman MD. Calcium-induced calcium release. Curr. Biol., 13(11): R425, 2003. [CiteXplore]
  3. Woods NM, Cuthbertson KS, Cobbold PH. Agonist-induced oscillations in cytoplasmic free calcium concentration in single rat hepatocytes. Cell Calcium, 8(1): 79-100, 1987. [CiteXplore]
  4. Zhu CL, Zheng Y, Jia Y. A theoretical study on activation of transcription factor modulated by intracellular Ca2+ oscillations. Biophys. Chem., 129(1): 49-55, 2007. [CiteXplore]
  5. Smolen P, Baxter DA, Byrne JH. Frequency selectivity, multistability, and oscillations emerge from models of genetic regulatory systems. Am. J. Physiol., 274 (Issue: 2 Pt 1):C531-42, 1998. [CiteXplore]