Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Bistability, Probability Transition Rate and First-Passage Time in an Autoactivating Positive-Feedback Loop

  • Xiu-Deng Zheng,

    Affiliations Key Laboratory of Animal Ecology and Conservational Biology, Centre for Computational and Evolutionary Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People's Republic of China, Graduate University of the Chinese Academy of Sciences, Beijing, People's Republic of China

  • Xiao-Qian Yang,

    Affiliation School of Mathematical Sciences, Beijing Normal University, Beijing, People's Republic of China

  • Yi Tao

    yitao@ioz.ac.cn

    Affiliation Key Laboratory of Animal Ecology and Conservational Biology, Centre for Computational and Evolutionary Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People's Republic of China

Bistability, Probability Transition Rate and First-Passage Time in an Autoactivating Positive-Feedback Loop

  • Xiu-Deng Zheng, 
  • Xiao-Qian Yang, 
  • Yi Tao
PLOS
x

Abstract

A hallmark of positive-feedback regulation is bistability, which gives rise to distinct cellular states with high and low expression levels, and that stochasticity in gene expression can cause random transitions between two states, yielding bimodal population distribution (Kaern et al., 2005, Nat Rev Genet 6: 451-464). In this paper, the probability transition rate and first-passage time in an autoactivating positive-feedback loop with bistability are investigated, where the gene expression is assumed to be disturbed by both additive and multiplicative external noises, the bimodality in the stochastic gene expression is due to the bistability, and the bistability determines that the potential of the Fokker-Planck equation has two potential wells. Our main goal is to illustrate how the probability transition rate and first-passage time are affected by the maximum transcriptional rate, the intensities of additive and multiplicative noises, and the correlation of additive and multiplicative noises. Our main results show that (i) the increase of the maximum transcription rate will be useful for maintaining a high gene expression level; (ii) the probability transition rate from one potential well to the other one will increase with the increase of the intensity of additive noise; (iii) the increase of multiplicative noise strength will increase the amount of probability in the left potential well; and (iv) positive (or negative) cross-correlation between additive and multiplicative noises will increase the amount of probability in the left (or right) potential well.

Introduction

Bistability arises within a wide range of biological systems from the bacteriophage to cellular signal transduction pathways in mammalian cells [1], [2]. As a fundamental behavior of biological system, bistability has been studied extensively through experiments, theoretical analysis and numerical simulations. Hasty et al. [3] considered a single network derived from bacteriophage and constructed a two-parameter deterministic model describing the temporal evolution of the concentration of repressor protein. They showed how additive and multiplicative external noise can be used to regulate gene expression. In the case with only additive noise, they demonstrated the utility of such control through the concentration of protein switch, whereby protein production is turned “on” and “off” by using short noise pulse. In the case with multiplicative noise, they showed that small deviations in the transcription rate can lead to large fluctuations in the production of protein. Combining theory and experiments, Isaacs et al. [4] investigated the dynamics of an isolated genetic module, an in vivo autoregulatory gene network. As predicted by their theoretical model, temperature-induced protein destabilization led to the existence of two expression states. The result of Isaacs et al. shows clearly the effects of varying the strength of feedback activation on population heterogeneity (see also [5]). Recently, Acar et al. [6] experimentally explored how switching affects population growth by using the galactose utilization network of Saccharomyces cerevisiae.

Kaern et al. [2] pointed out that a hallmark of positive-feedback regulation is bistability, which gives rise to distinct cellular states with high and low expression levels, and that stochasticity in gene expression can cause random transitions between the two states, yielding bimodal population distributions (see also [7][9]). So, for the positive-feedback regulation with bistability, a challenging question is how to determine probability transition rates between the two states, or how the random transitions between the two states are affected by the transcription rate and noise strength. In this paper, a simple theoretical model for an autoactivating positive-feedback loop is investigated. Our main goal is to provide a theoretical analysis for the probability transition rate and first-passage time in the system with external noise. The paper is organized as follows. In section 2, the basic model and its bistability is presented. The analysis of the probability transition rate and first-passage time for the situations with additive and multiplicative external noises are given in section 3.

Results

Basic model

It is well known that the simplest circuit motif able to exhibit multiple stable states is the autoactivating positive-feedback loop [5], [9][11], in which a single gene encodes a protein (activator), and the activator monomers bind into dimers that subsequently bind to the upstream regulatory site of the gene, activating production of the activator monomers (see Figure 1). For example, the autoactivation of CI protein by the promoter of phage [4]. The autoactivating positive-feedback loop is expected to exhibit bistability for the protein synthesis level, i.e., a higher level and a low level of protein concentration [2], [9]. Let and denote the concentrations of mRNA and activator protein at time , respectively. Then, in general the macroscopic rate equation for and can be expressed as(1)where represents the mRNA transcription rate, which is defined as a function of activator protein concentration with , the parameter denotes the translation rate, and the parameters and are the degradation rates of mRNA and protein, respectively, with , i.e., the concentration of mRNA is a fast variable compared with the concentration of activator protein [12], [13]. For the mRNA transcription rate , we take it as a Hill-type function(2)where is the maximum transcription rate, the Hill coefficient where we take , the Hill constant, and the basal transcription rate with [14]. In biology, these parameters mean that: (i) represents the mRNA transcription rate when the activator protein concentration is large enough; (ii) implies that the activator binding processes are considered comparatively rapid and close to equilibrium, so the concentration of activator homodimer is proportional to the square of activator monomer concentration [14]; (iii) is the dissociation constant of activator dimer from the regulatory site; and (iv) is the mRNA transcription rate when the activator protein concentration is very low [15].

thumbnail
Figure 1. Modeling of autoactivating positive-feedback loop.

In this model, the transcriptional activator monomers bind into dimers that bind to specific DNA sequences near the promoter, activating production of the activator monomers. The dotted line represents the positive auto-regulation. The degradation of both mRNA and protein is denoted by the slashed circle.

https://doi.org/10.1371/journal.pone.0017104.g001

Notice that can be considered to be a fast variable compared with since . Then, Eq. 1 can be reduced as(3)i.e., the fast variable can be assumed to be at an effective equilibrium, whereas the slow variable is responsible for the dynamics of the system [5], [16], [17]. In mathematics, one of the most important properties of Eq. 3 is its bistability, i.e., Eq. 3 has at most three fixed points, denoted by , and with . For the stability of , and , it is easy to see that both and are locally asymptotically stable and is unstable since for and (see also [15]).

In biology, we are more interested in how the dynamic properties of Eq. 3 is affected by the maximum transcription rate (see also [15]). The relationship between the bistability and is plotted in Figure 2 (i.e. vs. for ) where, following Smolen et al. [15], the parameters are taken as , , , and (see also [16]) (in this paper, we keep these parameters to be fixed). Clearly, for the stability of Eq. 3, the parameter has two bifurcation values, denoted by and , respectively, with (where and ), i.e., the bistability exists if is in the interval . For the situation with (or ), the system will be monostable, i.e., the system has only one fixed point (or ) if (or if ). On the other hand, if (or ), then the system will have two fixed points and (or and ), i.e., if exactly equal its bifurcation value, then Eq. 3 will have two fixed points, in which one is stable and the other semi-stable.

thumbnail
Figure 2. Bifurcation analysis of bistability in the deterministic gene expression as a function of .

The parameters , , , and are taken as , , , and (see also the main text). The bistability exists if , where and ). If (or , then the system will be monostable (see also Ref. [14]).

https://doi.org/10.1371/journal.pone.0017104.g002

Similar to Hasty et al. [3], we here consider also how the dynamics of protein concentration is affected by the additive and multiplicative external noises. As discussed above, if the bistability exists, then in the absence of noise, the system state will evolve identically to one of the two fixed points. The presence of a noise source will lead to the fluctuation of the system state. Hasty et al. [3] pointed out that an additive noise source alters the “background” protein production. This means that we need to consider the effect of a randomly varying external field on the biochemical reactions. For example, To et al. [18] provided an experiment evident to show that the change of the external noise can induce bimodality in positive transcriptional feedback loops without bistability. On the other hand, Hasty et al. [3] also pointed out that although transcription is represented by a single biochemical reaction, it is actually a complex sequence of reactions, and it is natural to assume that this part of the gene regulatory sequence is likely to be affected by fluctuations of many internal or external parameters. This implies that the transcription rate can be also considered to be a random variable.

In our model, the additive noise, denoted by , alters the “background” protein production, and is defined as a white noise with and where measures the level of additive noise strength. The multiplicative noise alters the transcription rate. We vary the transcription rate by allowing the parameter to vary stochastically, i.e., let , where is also a white noise with and where measures the level of multiplicative noise strength. Here a natural question is whether the additive and multiplicative noises are statistically correlated. However we could imagine the correlation arising from the feedback regulation, i.e. the transcription rate (affected by noise) is chemically coupled to the protein concentration (also affected by noise). We define that the cross-correlation of and is where is the cross-correlation intensity [19]. In fact, for the cross-correlation between the additive noise and multiplicative noise , we have no experimental evidence that indicate that the parameter should be positive, or negative. We also noticed that in a previous model developed by Hasty et al. [3], the effect of the cross-correlation between the additive and multiplicative noises on the stochastic gene expression is ignored. Thus, for the effect of we will only provide some theoretical possibilities.

According to the above definitions about and , the Langevin equation corresponding to Eq. 3 is given by(4)where(5)

Let denote the probability density distribution that the concentration of activator protein exactly equals at time . Then, from Risken [20], the Fokker-Planck equation of corresponding to Eq. 4 can be given by(6)The stationary distribution is where is the normalized constant and(7)is called the potential of the Fokker-Planck equation.

Additive noise

In this subsection, we consider only the effect of additive noise on the bistability, and assume that  = 0 (i.e. we here ignore the multiplicative noise). Clearly, for , Eq. 6 can be reduced to(8)and Eq. 7 can be rewritten as(9)with . The stationary solution of Eq. 8 is where . Obviously, is a bimodal distribution if , i.e., stationary distribution has two peaks corresponding to the two stable points and , respectively. This also implies that the stable point () must correspond to the local minimum of the potential , and the unstable point to the local maximum of . In physics, the local minimum of corresponding to (or ) is also called the potential well, and the local maximum of corresponding to the potential barrier [20]. Thus, for convenience we call the potential well corresponding to (or ) the left (or right) well.

The depth of the right well is defined as , and, similarly, the depth of the left well is . The depth of the potential well varies as the function of where we take the parameters , , , and to be fixed. The bistable potential is plotted in Figure 3A for two different values, where (solid curve) with and (dotted curve) with . This strongly implies that the depth of left well decreases with the increase of but the depth of right well increases with the increase of . The relationship between the depth of potential well and is plotted in Figure 3B, i.e. the system state should be more easily attracted by the left well with the increase of the maximum transcription rate. It is also easy to see that there must exist a value such that (in Figure 3B, if ).

thumbnail
Figure 3. Effect of on the potential .

A) The potential for different values, where (solid curve) with and (dotted curve) with . B) The relationship between the depth of potential well and . When , both right and left potential wells have the same depth, i.e., .

https://doi.org/10.1371/journal.pone.0017104.g003

The effects of and on the stationary distribution are plotted in Figure 4. We show that the decrease of will increase the total probability in the left well if (with ) and the total probability in the right well if (with ). The stationary distribution with (), () and () are plotted in Figure 4A, 4B and 4C, respectively. We noticed that this result has been used to explain how the external noise can be used to control the level of protein synthesis [2], [3]. For example, Hasty et al. investigated the autoregulation of bacteriophage repressor expression network which contains three operator sites known as OR1, OR2, and OR3 and constructed a two-parameter deterministic model describing the temporal evolution of the concentration of repressor protein [3]. They showed how the bistable regime is enhanced with the addition of the first operator site in the promoter region through comparing two models with only the last two operator sites and the full operator regions, respectively. They also showed how external noise can be used to regulate expression through adding external additive noise or multiplicative noise using the stochastic simulations. For the case with additive noise, they demonstrated the utility of such control through the successful switch of the concentration of a protein, whereby protein production is turned “on” and “off” by using short noise pulses, where the short noise pulse means that the noise of the system is rapidly increased to a high level in a short period of time, and after this pulse, the noise is returned to its original value.

thumbnail
Figure 4. Effect of on .

The effects of additive noise strength on the stationary distribution for different values are plotted, where in A, in B and in C.

https://doi.org/10.1371/journal.pone.0017104.g004

When the system state is near the stable point (), the steady-state statistics of the system can be given by and (the proof is given in Text S1) (see also [21]). This result shows clearly that when the system state is near the stable point , the intensity of stochastic fluctuations around is proportional to the noise strength but inversely proportional to . On the other hand, for the bistable potential function , a more challenging question is how the system state jumps from one potential well to the other (i.e. the state is switches from one well to the other) because of the external noise.

Notice that the probability exchange between two potential wells occurs only near the potential barrier (i.e., at the unstable point ). Thus, the increase (or decrease) of the amount of probability in one potential well must result in the decrease (or increase) of the amount of probability in the other. Let denote the total probability in the right well at time , i.e., , and, similarly, the total probability in the left well at time , i.e., (where we must have ). Then the master equations of and can be given by(10)where is the probability transition rate from the right well to the left well, and the probability transition rate from the left well to the right well, which are given by(11)respectively, (the mathematical derivation is given in Text S1) (see also [22]). Obviously, Eq. 10 has two eigenvalues, one is , and the other . The first one is the eigenvalue of the Fokker-Plack equation corresponding to the stationary distribution, and the latter is the lowest non-vanishing eigenvalue where measures the largest time scale of the probability transition between two potential wells. From Eq. 11, we have that:

  1. For both and , we have and . This means that the increase of will promote the probability exchange between two wells (see Figure 5A).
  2. The transition rate will decrease with the increase of the well depth, i.e., and (where and ). Hence, from Figure 3B, we have also that will increase with the increase of but will decrease with the increase of . In biology, this means that the increase of will promote the probability transfer from the left well to the right well, or the increase of will be useful for maintaining a high gene expression level (see also Figure 4).
  3. For convenience, we use ratio
thumbnail
Figure 5. Effect of on probability transition rate.

The effects of additive noise strength on both probability transition rates and are shown: A) For different values, both and will increase with the increase of . B) The ratio of will increase (or decrease) with the increase of if (or ). For , the ratio is a constant.

https://doi.org/10.1371/journal.pone.0017104.g005

(12)to measure the relative intensity of probability exchange between two wells, i.e., if , then the probability is more easily transferred from the right well to the left well, and, conversely, if , then the probability is more easily transferred from the left well to the right well. The effect of noise strength on the ratio mainly depends on the difference between the depths of right and left wells (i.e., ) since we have that if , and if . This result shows clearly that the increase of will promote probability exchange from the deep well to the shallow well. The relationship between , and is plotted in Figure 5B. Clearly, the ratio is an increasing function of if (where ), and a decreasing function of if (where ). Particularly, when , the ratio keeps a constant (where ), i.e., it is independent of . On the other hand, it is also easy to see that the equilibrium solution of Eq. 10 must satisfy , i.e. and . This shows clearly how the probabilities in the right and left wells are affected by through the ratio .

In physics, the first-passage time is defined as the time at which the stochastic variable first leaves a given domain [20]. In general, the first-passage time can be used to measure the robustness of the system steady-state. For our model, let () denote the first-passage time at which the system state first leaves the right (left) well across the potential barrier. Under the weak noise (i.e., ), the expectations of and can be approximated as(13)respectively, and the variances of and , denoted by and , are(14)respectively (the mathematical derivations of Eqs 13 and 14 are given in Text S1). This strongly implies that under the weak noise, both and should approximately obey the exponential distribution. Statistically, all transition events (called also the escape events) in a given direction (for example, from the left well to the right well, or from the right well to the left well) can be roughly considered to be independent of each other with a given average rate, i.e. the number of the transition events in a given time interval should be a Poisson process. Thus, the distribution of the first-passage time should be an exponential distribution, and its scale parameter is the inverse of the probability transition rate [23].

The numerical simulation results for the statistical properties of and are given in Table 1 (The simulation algorithm is given in Text S1). It is easy to see that for different values (where we take ), the average of () (i.e. the mean first-passage time) and its standard deviation are almost same. The Monte Carlo simulations also show that under the weak noise, the relation is true (see Table 2). All of these simulation results exactly match the theoretical predictions.

thumbnail
Table 1. The Monte Carlo simulation results for the effect of additive noise strength on the statistical properties of first passage time and with (FPT: first-passage time).

https://doi.org/10.1371/journal.pone.0017104.t001

thumbnail
Table 2. The Monte Carlo simulation results for both ratios and with different values of and .

https://doi.org/10.1371/journal.pone.0017104.t002

Multiplicative noise

For , we first consider the situation with , i.e., and are independent of each other. According to this definition, we have that(15)and that(16)(see Eqs. 6 and 7). In this situation, the effect of on is plotted in Figure 6A, where the parameters and are taken as and . In subsection 3.1, we have shown that for , both right and left wells have the same depth if . We will select the special value in the following analysis. We can find that the depth of the right well will decrease with the increase of , but the change in the depth of the left well is very small. The stationary distribution and the Monte Carlo simulation corresponding to Figure 6A are plotted in Figure 6B–6D, respectively. These results show clearly that the amount of probability in the left (right) well will increase (decrease) with the increase of .

thumbnail
Figure 6. Effect of on and .

The effects of multiplicative noise strength on the potential and stationary distribution with and are shown: A) The depth of the right potential well will decrease with the increase of , but the depth of the left potential well is not sensitive to the change of . B–D) The Monte Carlo simulation results for different values, i.e., in B, in C and in D, where the solid curves denote the theoretical stationary distribution.

https://doi.org/10.1371/journal.pone.0017104.g006

Similar to the analysis in subsection 3.1, if both additive and multiplicative noises are weak, i.e., , the the expectations of the first-passage times and can be approximated as(17)(the mathematical derivation is given in Text S1) (see also [20], [22]). The Monte Carlo simulation results for the statistical properties of and are given in Table 3, in which we can find that not only both and should obey the exponential distribution but also is more sensitive for than . This implies that the increase of the multiplicative noise strength will be useful for maintaining the protein concentration at the low level.

thumbnail
Table 3. The Monte Carlo simulation results for the effect of multiplicative noise strength on the statistical properties of first passage time and with and .

https://doi.org/10.1371/journal.pone.0017104.t003

Secondly, for , we are interested in how the stochastic dynamics of the system is affected by the correlation between and . The effect of on is plotted in Figure 7A where , and , in which we can also find that the depth of the right well is more sensitive for the change of than the depth of the left well, and that if is negative, then the depth of the right well will increase with the increase of (i.e. absolute value of ), and, conversely, if is positive, the depth of the right well will decrease with the increase of . The effect of on the stationary distribution and the Monte Carlo simulation corresponding to Figure 7A are plotted in Figure 7B–7D. It shows clearly that the positive correlation () will increase the amount of probability in the left well, and, conversely, the negative correlation () will increase the amount of probability in the right well.

thumbnail
Figure 7. Effect of on and .

The effects of on the potential and stationary distribution with , and are shown: A) The negative (or positive) correlation between additive and multiplicative noises will increase (or decrease) the depth of the right potential well. The effect of on the left potential well is very small. B–D) The Monte Carlo simulation results for different values, i.e., in B, in C and in D, where the solid curves denote the theoretical stationary distribution. Clearly, the negative (or positive) correlation will increase the probability in the right (or left) potential well.

https://doi.org/10.1371/journal.pone.0017104.g007

For the effect of on the first-passage time, the Monte Carlo simulation results are showed in Figure 8, in which both and will decrease with the increase of , and the change rate of is obviously larger than that of . We can also notice that the ratio will decrease with the increase of (see Figure 8B). This also implies that the positive (or negative) correlation between and will promote the probability transition from the right (left) well to the left (right) well (see also Figure 7B–7D). Clearly, the dependence of the ratio on reflects how the stationary distribution is influenced by the cross-correlation between additive and multiplicative noises, or, theoretically, the cross-correlation between and should be also used to control the protein synthesis.

thumbnail
Figure 8. Effect of on first-passage time.

The Monte Carlo simulation for the effect of on the first passage time , and ratio with , and . A) Both and will decrease with the increase of . B) The ratio also decreases with the increase of .

https://doi.org/10.1371/journal.pone.0017104.g008

Discussion

In this paper, the probability transition rate and first-passage time in an autoactivating positive-feedback loop with bistability are investigated. In our model, similar to Hasty et al. [3], the gene expression is assumed to be disturbed by both additive and multiplicative external noises, and the bimodality in the stochastic gene expression is due to the bistatility. The bistability of the deterministic dynamics Eq. 3 implies that the potential of the Fokker-Planck equation Eq. 6 has two potential wells, which correspond to the two stable points of Eq. 3, respectively, and that the stationary solution (i.e. stationary distribution) of the Fokker-Planck equation is a bimodal distribution. For our main goal, we are interested in how the system state jumps from one potential well to the other because of the external noise. In subsection 3.1, for the situation with only additive noise, our main results show that (i) both probability transition rates and will increase with the increase of ; (ii) will increase with the increase of but will decrease with the increase of , i.e., the increase of will be useful for maintaining a high gene expression level; (iii) the ratio measures the relative intensity of probability exchange between two potential wells, and there is a critical value of (which is in our case) such that the ratio is an increasing function (or a decreasing function) of if is larger (or smaller) than the critical value; and (iv) for both first-passage times and , if (i.e. the additive noise is weak), then they obey the exponential distribution with expectations and , respectively. In subsection 3.2, for the situation with both additive and multiplicative noises, we show that (i) for (i.e. the additive noise and multiplicative noise are independent of each other), the increase of will be useful for maintaining the protein concentration at the low level; and (ii) for , the positive (or negative) correlation between additive and multiplicative noises will promote the probability transition from the right (left) well to the left (right) well, i.e., the positive correlation will increase the amount of probability in the left potential well, and, conversely, the negative correlation will increase the amount of probability in the right potential well.

However, our results may provide some theoretical intuitions for real gene expression. For example, Hasty et al. [3] investigated the autoregulation of repressor expression in the lysis-lysogeny pathway in the virus, and they showed why the additive and multiplicative noises can be used to regulate expression (or to control the protein production). Our results further show how the probability transition between two stable states (i.e. two levels of protein synthesis) is affected, and why the cross-correlation between the additive and multiplicative external noises can be also used to regulate expression. Finally, we would like to say that the further analysis incorporating more realistic dynamics should be carried out in future studies.

Supporting Information

Text S1.

Steady-state statistics, Probability transition rate, First-passage time, Method for stochastic simulation.

https://doi.org/10.1371/journal.pone.0017104.s001

(PDF)

Acknowledgments

The authors thank the anonymous referee for helpful suggestions on the original paper.

Author Contributions

Conceived and designed the experiments: YT XZ. Performed the experiments: XZ. Analyzed the data: XZ YT. Contributed reagents/materials/analysis tools: XZ XY. Wrote the paper: XZ YT.

References

  1. 1. Hasty J, McMillen D, Isaacs F, Collins JJ (2001) Computational studies of gene regulatory networks: in numero molecular biology. Nat Rev Genet 2: 268–279.J. HastyD. McMillenF. IsaacsJJ Collins2001Computational studies of gene regulatory networks: in numero molecular biology.Nat Rev Genet2268279
  2. 2. Kaern M, Elston TC, Blake WJ, Collins JJ (2005) Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 6: 451–464.M. KaernTC ElstonWJ BlakeJJ Collins2005Stochasticity in gene expression: from theories to phenotypes.Nat Rev Genet6451464
  3. 3. Hasty J, Pradines J, Dolink M, Collins JJ (2000) Noise-based switches and amplifiers for gene expression. Proc Natl Acad Sci USA 97: 2075–2080.J. HastyJ. PradinesM. DolinkJJ Collins2000Noise-based switches and amplifiers for gene expression.Proc Natl Acad Sci USA9720752080
  4. 4. Isaacs FJ, Hasty J, Cantor CR, Collins JJ (2003) Prediction and measurement of an autoregulatory genetic module. Proc Natl Acad Sci USA 100: 7714–7719.FJ IsaacsJ. HastyCR CantorJJ Collins2003Prediction and measurement of an autoregulatory genetic module.Proc Natl Acad Sci USA10077147719
  5. 5. Bennett MR, Volfson D, Tsimring L, Hasty J (2007) Transient dynamics of genetic regulatory networks. Biphysical J 92: 3501–3512.MR BennettD. VolfsonL. TsimringJ. Hasty2007Transient dynamics of genetic regulatory networks.Biphysical J9235013512
  6. 6. Acar M, Mettetal JT, van Oudenaarden A (2008) Stochastic switching as a survival strategy in fluctuating environments. Nature Genetics 40: 471–475.M. AcarJT MettetalA. van Oudenaarden2008Stochastic switching as a survival strategy in fluctuating environments.Nature Genetics40471475
  7. 7. Becskei A, Seraphin B, Serrano L (2001) Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J 20: 2528–2533.A. BecskeiB. SeraphinL. Serrano2001Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion.EMBO J2025282533
  8. 8. Ozbudak EM, Thattai M, Lim HN, Shraiman BI, van Oudenaarden A (2004) Multistability in the lactose utilization network of Escherichia coli. Nature 427: 737–740.EM OzbudakM. ThattaiHN LimBI ShraimanA. van Oudenaarden2004Multistability in the lactose utilization network of Escherichia coli.Nature427737740
  9. 9. Smits WK, Kuipers OP, Veening JW (2006) Phynotypic variation in bacteria: the role of feedback regulation. Nature Reviews Microbiology 4: 259–271.WK SmitsOP KuipersJW Veening2006Phynotypic variation in bacteria: the role of feedback regulation.Nature Reviews Microbiology4259271
  10. 10. Pomerening JR, Sontag ED, Ferrell JE Jr (2003) Building a cell cycle oscillator: hysteresis and bistabiltiy in the activation of Cdc2. Nature Cell Biology 5: 346–351.JR PomereningED SontagJE Ferrell Jr2003Building a cell cycle oscillator: hysteresis and bistabiltiy in the activation of Cdc2.Nature Cell Biology5346351
  11. 11. Scott M, Ternece Hwa T, Ingalls B (2007) Deterministic characterization of stochastic genetic circuits. Proc Natl Acad Sci USA 104: 7402–7407.M. ScottT. Ternece HwaB. Ingalls2007Deterministic characterization of stochastic genetic circuits.Proc Natl Acad Sci USA10474027407
  12. 12. Thattai M, van Oudenaarden A (2001) Intrinsic noise in gene regulatory networks. Proc Natl Acad Sci USA 98: 8614–8619.M. ThattaiA. van Oudenaarden2001Intrinsic noise in gene regulatory networks.Proc Natl Acad Sci USA9886148619
  13. 13. Paulson J (2004) Summing up the noise in gene networks. Nature 427: 415–418.J. Paulson2004Summing up the noise in gene networks.Nature427415418
  14. 14. Rosenfeld N, Young JW, Alon U, Swain PS, Elowitz MB (2005) Gene regulation at the single-cell level. Science 307: 1962–1965.N. RosenfeldJW YoungU. AlonPS SwainMB Elowitz2005Gene regulation at the single-cell level.Science30719621965
  15. 15. Smolen P, Baxter DA, Byrne JH (1998) Frequency selectivity, multistability, and oscillations emerge from models of genetic regulatory systems. Am J Cell Physiol 274: 531–542.P. SmolenDA BaxterJH Byrne1998Frequency selectivity, multistability, and oscillations emerge from models of genetic regulatory systems.Am J Cell Physiol274531542
  16. 16. Vilar JMG, Kueh HY, Barkai N, Leibler S (2002) Mechanisms of noise-resistance in genetic oscillators. Proc Natl Acad Sci USA 99: 5988–5992.JMG VilarHY KuehN. BarkaiS. Leibler2002Mechanisms of noise-resistance in genetic oscillators.Proc Natl Acad Sci USA9959885992
  17. 17. Elf J, Ehrenberg M (2003) Fast evaluation of fluctuations in biochemical networks with the linear noise approximation. Genome Res 13: 2475–2484.J. ElfM. Ehrenberg2003Fast evaluation of fluctuations in biochemical networks with the linear noise approximation.Genome Res1324752484
  18. 18. To TL, Maheshri N (2010) Noise can induce bimodality in positive transcriptional feedback loops without bistability. Science 327: 1142–1145.TL ToN. Maheshri2010Noise can induce bimodality in positive transcriptional feedback loops without bistability.Science32711421145
  19. 19. Denisov SI, Vitrenko AN, Horsthemke W (2003) Nonequilibrium transitions induced by the cross-correlation of white noises. Phys Rev E 68: 046132.SI DenisovAN VitrenkoW. Horsthemke2003Nonequilibrium transitions induced by the cross-correlation of white noises.Phys Rev E68046132
  20. 20. Risken H (1992) The Fokker-Planck Equation: Methods of Solution and Applications. Berlin: Springer. H. Risken1992The Fokker-Planck Equation: Methods of Solution and Applications.BerlinSpringer
  21. 21. van Kampen NG (1992) Stochastic Process Theory in Physics and Chemistry. Amsterdam: North-Holland. NG van Kampen1992Stochastic Process Theory in Physics and Chemistry.AmsterdamNorth-Holland
  22. 22. Hu G (1992) Stochastic Forces and Nonlinear System. Shanghai Press for Science, Technology and Education (Chinese version). G. Hu1992Stochastic Forces and Nonlinear System.Shanghai Press for Science, Technology and Education (Chinese version)
  23. 23. Papoulis A, Pillai SU (2001) Probability, Random Variables and Stochastic Processes. New York: McGraw-Hill. A. PapoulisSU Pillai2001Probability, Random Variables and Stochastic Processes.New YorkMcGraw-Hill