(291d) Modeling Stochasticity in the Cell Cycle

Stochasticity is endemic to biology. Biological systems are intrinsically noisy and heterogeneous at all levels, from the molecular to the population scale. On the cell-to-cell level, differences typically arise due to a randomness in reaction events [1]. The intrinsic noise in biological systems present an element of challenge for models attempting to emulate them appropriately. Although deterministic models are often the preferred method due to the ease of integration, they sometimes fail to represent biological processes which are susceptible to noise in a realistic manner [2]. Erratic influences present in nonlinear dynamic systems are sometimes so significant that they dictate the state and stability of that system [3]. This phenomenon, termed stochastic bifurcation, occurs when the level of noise drives the system from one steady state to another [4]. The meaning of these states can be polar opposite; in the context of inflammation for example, Reynolds et al. report that the level of noise can drive the system from a healthy state to that of asceptic death [5]. As the mathematical representation of biological models grows in complexity, the need to extend the capabilities of such models to capture stochastic behavior and to better understand noise-induced phenotypes is becoming increasingly apparent.

As with all biological responses, stochasticity occurs naturally in the cell cycle, the biological process regulating the rate and extent to which a population of cells proliferate. The cell cycle is generally considered to have four distinctive phases: the S phase, when DNA replication occurs, the mitotic (M) phase, and the G1 and G2 growth phases. Non-dividing cells exist in a quiescent state (G0) [6]. As a consequence of the outstanding work by Tyson, Novak, and Goldbeter, models have been developed that describe the key mechanisms regulating the cell cycle [7-9]. These models, however, remain deterministic in nature. While some simulations have incorporated stochastic components into the modeling network [10], a more detailed exploration of the impact of stochasticity on cell cycle progression is required.

This work aims to further examine the effects of noise on modulating the cell cycle by considering three main hypotheses. The first consideration explores the source of stochasticity. Which portion(s) of the cell cycle exhibit stochasticity? Does the noise originate from the microenvironment, such as variability in a growth factor, or does it arise out of the complex interactions among the cyclin/cyclin-dependent kinase (CDKs) network? Does noise propagate from one phase of the cycle to the next? Stemming from these questions, the sensitivity of the network must also be considered: are certain variables or parameters more robust to stochasticity? This analysis could provide information about whether cells have more rigorous control and regulation of certain aspects of the cell cycle. If the focus of the cell cycle is to ensure proper replication of genetic material [7], then perhaps one can hypothesize that phases involved in DNA replication are less tolerant to stochastic influences. Lastly, this work examines the time-dependence of noise on the cell cycle. The time at which noise is introduced has been shown to influence the state of dynamic systems if the system has multiple stable (or unstable) states.

Understanding the core regulatory mechanisms of the cell cycle and the role played by intrinsic stochasticity provides interesting therapeutic opportunities. The deregulation of the cell cycle has been affiliated with numerous pathologies, such as cancer, where aberrant cell division occurs [11]. Guided by a more detailed insight of the underlying influences of stochasticity, drugs could be more appropriately designed to either restore a disrupted cell cycle or hinder the proliferation of malignant cells.

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