Recent investigations into exercise-induced tumor suppression suggest that higher exercise frequency enhances tumor control when the total duration of exercise within a specified time window is not constrained. An equally compelling avenue for exploration is the effect of increased exercise frequency under the condition of a fixed total exercise duration within the same time frame. Using a mathematical model of IL-6-mediated interactions between natural killer cells and tumor cells, here we explore how different combinations of exercise and rest intervals-while maintaining a constant overall exercise volume-affect tumor suppression. Our results reveal a nonmonotonic tumor response and key metrics such as the time of maximum tumor suppression and the duration of tumor suppression are found to decrease with increasing exercise frequency. Interestingly, unlike earlier study where increasing exercise frequency leads to increase in tumor suppression, here we find that under fixed exercise volume constraints, increased frequency diminishes therapeutic efficacy of exercise, suggesting exercise bouts with longer duration are more effective in suppressing tumors. These findings highlight the importance of considering total exercise volume when designing exercise-based cancer interventions.

Latency is the major obstacle in eradicating HIV from infected patients. Recent studies have shown that BET protein inhibitors can successfully reverse this latency by inhibiting the binding of BET proteins with positive cellular cofactor P-TEFb. Thus, availability of P-TEFbs plays an important role in HIV transactivation. However, in cells of our immune system which are primarily infected by the virus, number of P-TEFb is very low and is considered as one of the factors in inducing viral latency. At such small numbers of P-TEFb, the internal fluctuations can have a decisive role in the cell fate decision and fluctuations in the P-TEFb levels can switch the HIV to either a state of active replication or to a state of latency. Aimed at quantitative understanding of how BET inhibitors affect the statistics of P-TEFb level, we develop a coarse-grained stochastic model. However, the interaction between P-TEFb and BET proteins makes the problem analytically challenging. To address the nonlinearity arising due to such interactions, we use Langevin equation based approach to study the statistics of steady-state P-TEFb levels and explore the variations of some of the important quantities such as noise and fano factor associated with P-TEFb as well as correlations between BET and P-TEFb levels with model parameters. The analytic results derived exhibit that these quantities, in general, show non-monotonic response with respect to the parameters of the model. The results derived will be helpful in estimating the model parameters as well in identifying the pathways that can be intervened for effective HIV transactivation.

Recent experimental studies have shown that physical exercise has the potential to suppress tumor progression. Such suppression has been reported to be mediated by the exercise-induced activation of natural killer (NK) cells through the release of IL-6, a cytokine. Aimed at shedding light on how exercise-induced NK cell activation helps in the suppression of cancer, we developed a coarse-grained mathematical model based on a system of ordinary differential equations describing the interaction between IL-6, NK-cells, and tumor cells. The model is then used to study how exercise duration and exercise intensity affect tumor suppression. Our results show that increasing exercise intensity or increasing exercise duration leads to greater and sustained tumor suppression. Furthermore, multi-bout exercise patterns hold promise for improving cancer treatment strategies by adjusting exercise intensity and frequency. Thus, the proposed mathematical model provides insights into the role of exercise in tumor suppression and can be instrumental in guiding future experimental studies, potentially leading to more effective exercise interventions.

Gene expression is an inherently stochastic process with transcription of mRNAs often occurring in bursts: short periods of activity followed by typically longer periods of inactivity. While a simple model involving switching between two promoter states has been widely used to analyze transcription dynamics, recent experimental observations have provided evidence for more complex kinetic schemes underlying bursting. Specifically, experiments provide evidence for complexity in promoter dynamics during the switch from the transcriptionally inactive to the transcriptionally active state. An open question in the field is: what is the minimal complexity needed to model promoter dynamics and how can we determine this? Here, we show that measurements of mRNA fluctuations can be used to set fundamental bounds on the complexity of promoter dynamics. We study models wherein the switching time distribution from transcriptionally inactive to active states is described by a general waiting-time distribution. Using approaches from renewal theory and queueing theory, we derive analytical expressions which connect the Fano factor of mRNA distributions to the waiting-time distribution for promoter switching between inactive and active states. The results derived lead to bounds on the minimal number of promoter states and thus allow us to derive bounds on the minimal complexity of promoter dynamics based on single-cell measurements of mRNA levels.

Post-transcriptional regulation of gene expression is often a critical component of cellular processes involved in cell-fate decisions. Correspondingly, considerable efforts have focused on modeling post-transcriptional regulation of stochastic gene expression and on quantifying its impact on the mean and variance of protein levels. However, the impact of post-transcriptional regulation on rare events corresponding to large deviations in gene expression is less well understood. Here, we study a simple model involving post-transcriptional control of gene expression and characterize the impact of regulation on large deviations in activity fluctuations. We derive analytical results for the large deviation function for protein production rate and for the corresponding driven process which characterizes system fluctuations conditional on the rare event. Our results suggest that fluctuations in burst size rather than frequency play a dominant role in controlling rare events. The results derived also provide insight into how post-transcriptional regulation can be used to fine-tune the probability of rare fluctuations and to thereby control phenotypic variation in a population of cells.

Phenotypic heterogeneity in cancer cells is widely observed and is often linked to drug resistance. In several cases, such heterogeneity in drug sensitivity of tumors is driven by stochastic and reversible acquisition of a drug tolerant phenotype by individual cells even in an isogenic population. Accumulating evidence further suggests that cell-fate transitions such as the epithelial to mesenchymal transition (EMT) are associated with drug resistance. In this study, we analyze stochastic models of phenotypic switching to provide a framework for analyzing cell-fate transitions such as EMT as a source of phenotypic variability in drug sensitivity. Motivated by our cell-culture based experimental observations connecting phenotypic switching in EMT and drug resistance, we analyze a coarse-grained model of phenotypic switching between two states in the presence of cytotoxic stress from chemotherapy. We derive analytical results for time-dependent probability distributions that provide insights into the rates of phenotypic switching and characterize initial phenotypic heterogeneity of cancer cells. The results obtained can also shed light on fundamental questions relating to adaptation and selection scenarios in tumor response to cytotoxic therapy.

In a recent work (Harbola et al 2014 Phys. Rev. E 90 022136), we introduced a minimal random walk model which gives rise to diffusion, sub- and super-diffusion dynamics with a single sweep of a parameter that determines how well the walker follows the past history. Here we investigate the effects of the 'memory loss' within the minimal model. To incorporate such a memory-loss we study the dynamics of a random walker which switches states stochastically between two internal states: in one of the states, the walker performs memory driven random walk, while in the other state its dynamics is not affected by the history of previous steps. The model is analytically tractable and the derived exact expressions for the moments give rise to interesting asymptotic dynamic behavior. Our results reveal that even for an infinitesimal possibility for the 'memory loss' the subiliffusive behavior is completely lost and its dynamics is characterized only by diffusive and superdiffusive behavior.

Noncoding small RNAs (sRNAs) are known to play a key role in regulating diverse cellular processes, and their dysregulation is linked to various diseases such as cancer. Such diseases are also marked by phenotypic heterogeneity, which is often driven by the intrinsic stochasticity of gene expression. Correspondingly, there is significant interest in developing quantitative models focusing on the interplay between stochastic gene expression and regulation by sRNAs. We consider the canonical model of regulation of stochastic gene expression by sRNAs, wherein interaction between constitutively expressed sRNAs and mRNAs leads to stoichiometric mutual degradation. The exact solution of this model is analytically intractable given the nonlinear interaction term between sRNAs and mRNAs, and theoretical approaches typically invoke the mean-field approximation. However, mean-field results are inaccurate in the limit of strong interactions and low abundances; thus, alternative theoretical approaches are needed. In this work, we obtain analytical results for the canonical model of regulation of stochastic gene expression by sRNAs in the strong interaction limit. We derive analytical results for the steady-state generating function of the joint distribution of mRNAs and sRNAs in the limit of strong interactions and use the results derived to obtain analytical expressions characterizing the corresponding protein steady-state distribution. The results obtained can serve as building blocks for the analysis of genetic circuits involving sRNAs and provide new insights into the role of sRNAs in regulating stochastic gene expression in the limit of strong interactions.

The sporadic nature of gene expression at the single-cell level-long periods of inactivity punctuated by bursts of mRNA or protein production-plays a critical role in diverse cellular processes. To elucidate the cellular role of bursting in gene expression, synthetic biology approaches have been used to design simple genetic circuits with bursty mRNA or protein production. Understanding how such genetic circuits can be designed with the ability to control burst-related parameters requires the development of quantitative stochastic models of gene expression. In this work, we analyze stochastic models for the regulation of gene expression bursts by strongly interacting small RNAs. For the parameter range considered, results based on mean-field approaches are significantly inaccurate and alternative analytical approaches are needed. Using simplifying approximations, we obtain analytical results for the corresponding steady-state distributions that are in agreement with results from stochastic simulations. These results indicate that regulation by small RNAs, in the strong interaction limit, can be used to effectively modulate the frequency of bursting. We explore the consequences of such regulation for simple genetic circuits involving feedback effects and switching between promoter states.

Auto-regulation, a process wherein a protein negatively regulates its own production, is a common motif in gene expression networks. Negative feedback in gene expression plays a critical role in buffering intracellular fluctuations in protein concentrations around optimal value. Due to the nonlinearities present in these feedbacks, moment dynamics are typically not closed, in the sense that the time derivative of the lower-order statistical moments of the protein copy number depends on high-order moments. Moment equations are closed by expressing higher-order moments as nonlinear functions of lower-order moments, a technique commonly referred to as moment closure. Here, we compare the performance of different moment closure techniques. Our results show that the commonly used closure method, which assumes a priori that the protein population counts are normally distributed, performs poorly. In contrast, conditional derivative-matching, a novel closure scheme proposed here provides a good approximation to the exact moments across different parameter regimes. In summary our study provides a new moment closure method for studying stochastic dynamics of genetic negative feedback circuits, and can be extended to probe noise in more complex gene networks.

Gene expression in individual cells is highly variable and sporadic, often resulting in the synthesis of mRNAs and proteins in bursts. Such bursting has important consequences for cell-fate decisions in diverse processes ranging from HIV-1 viral infections to stem-cell differentiation. It is generally assumed that bursts are geometrically distributed and that they arrive according to a Poisson process. On the other hand, recent single-cell experiments provide evidence for complex burst arrival processes, highlighting the need for analysis of more general stochastic models. To address this issue, we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions. These results are then used to derive noise signatures, i.e. explicit conditions based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process. For non-Poisson arrivals, we develop approaches for accurate estimation of burst parameters. The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions.

Stochasticity in gene expression can give rise to fluctuations in protein levels and lead to phenotypic variation across a population of genetically identical cells. Recent experiments indicate that bursting and feedback mechanisms play important roles in controlling noise in gene expression and phenotypic variation. A quantitative understanding of the impact of these factors requires analysis of the corresponding stochastic models. However, for stochastic models of gene expression with feedback and bursting, exact analytical results for protein distributions have not been obtained so far. Here, we analyze a model of gene expression with bursting and feedback regulation and obtain exact results for the corresponding protein steady-state distribution. The results obtained provide new insights into the role of bursting and feedback in noise regulation and optimization. Furthermore, for a specific choice of parameters, the system studied maps on to a two-state biochemical switch driven by a bursty input noise source. The analytical results derived provide quantitative insights into diverse cellular processes involving noise in gene expression and biochemical switching.

A discrete-time dynamics of a non-Markovian random walker is analyzed using a minimal model where memory of the past drives the present dynamics. In recent work [N. Kumar et al., Phys. Rev. E 82, 021101 (2010)] we proposed a model that exhibits asymptotic superdiffusion, normal diffusion, and subdiffusion with the sweep of a single parameter. Here we propose an even simpler model, with minimal options for the walker: either move forward or stay at rest. We show that this model can also give rise to diffusive, subdiffusive, and superdiffusive dynamics at long times as a single parameter is varied. We show that in order to have subdiffusive dynamics, the memory of the rest states must be perfectly correlated with the present dynamics. We show explicitly that if this condition is not satisfied in a unidirectional walk, the dynamics is only either diffusive or superdiffusive (but not subdiffusive) at long times.

Auto-regulation, a process wherein a protein negatively regulates its own production, is a common motif in gene expression networks. Negative feedback in gene expression plays a critical role in buffering intracellular fluctuations in protein concentrations around optimal value. Due to the nonlinearities present in these feedbacks, moment dynamics are typically not closed, in the sense that the time derivative of the lower-order statistical moments of the protein copy number depends on high-order moments. Moment equations are closed by expressing higher-order moments as nonlinear functions of lower-order moments, a technique commonly referred to as moment closure. Here, we compare the performance of different moment closure techniques. Our results show that the commonly used closure method, which assumes a priori that the protein population counts are normally distributed, performs poorly. In contrast, conditional derivative matching, a novel closure scheme proposed here provides a good approximation to the exact moments across different parameter regimes. In summary our study provides a new moment closure method for studying stochastic dynamics of genetic negative feedback circuits, and can be extended to probe noise in more complex gene networks.

The process of transcription has been intensively studied for several decades, however there is still much to learn about the underlying biochemical processes. Recent advances in single-molecule techniques have provided new experimental data that highlights the role of transcriptional pausing in the regulation of gene expression. In some cases, it has been shown that transcriptional pauses are rate-limiting stochastic processes, thus a quantitative understanding requires stochastic modeling of the underlying processes. We propose a coarse-grained stochastic model to analyze the dwell-time distribution for transcriptional pausing. The proposed kinetic scheme can also be used to model transcriptional initiation and to analyze the corresponding noise in gene expression. We obtain analytical solutions which can provide useful insights into current and future experiments focusing on time-resolved single-molecule studies of transcriptional pausing and noise in gene expression.

We describe a single-level quantum dot in contact with two leads as a nanoscale finite-time thermodynamic machine. The dot is driven by an external stochastic force that switches its energy between two values. In the isothermal regime, it can operate as a rechargeable battery by generating an electric current against the applied bias in response to the stochastic driving and then redelivering work in the reverse cycle. This behavior is reminiscent of the Parrondo paradox. If there is a thermal gradient the device can function as a work-generating thermal engine or as a refrigerator that extracts heat from the cold reservoir via the work input of the stochastic driving. The efficiency of the machine at maximum power output is investigated for each mode of operation, and universal features are identified.

We derive upper and lower bounds for the efficiency of an isothermal molecular machine operating at maximum power. The upper bound is reached when the activated state is close to the fueling or reactant state (Eyring-like), while the lower bound is reached when the activated state is close to the product state (Kramers-like).

We study the Turing bifurcation in general two-species reaction-transport systems, where particle dispersal is governed by diffusion, including cross diffusion. By performing a linear stability analysis, we find conditions for the Turing instability and compare the results with the standard Turing conditions. We apply our results to two model systems, the Lengyel-Epstein model and the Brusselator, and find strong effects of cross diffusion on the Turing bifurcation.