Stochastic and Deterministic Decision in Cell Fate


From bacteria to mammals, individual cells from an isogenic population are able to assume roles resulting in phenotypic heterogeneity. The mechanisms used to make these cell fate decisions range from highly deterministic to essentially random. This wide range of behaviour springs from the interplay of intracellular molecular kinetics, the topologies of underlying gene regulator networks, epigenetic control mechanisms and cell–environment interactions. Cells utilise these factors to implement differentiation strategies such as developmental rigidity, which ensures the development of key structures in multicellular organisms, and bet hedging, the introduction of nongenetic variability to promote population fitness. Because decision‐making genes in natural systems are integrated with myriad other pathways, they can be difficult to study on their own. Synthetic biology offers a means to study cell differentiation in vivo in a manner separated from normal cellular functions.

Key Concepts:

  • Gene expression is an inherently stochastic process.

  • Despite stochastic gene expression, differentiation can proceed deterministically.

  • Other cell fate decisions are random due to stochastic gene expression.

  • Gene network topologies are employed to attenuate or increase the effects of noise.

  • The field of synthetic biology is uniquely suited for exploring the complex interactions that inform cell decisions.

Keywords: bet hedging; diversification; heterogeneity; motif; stochasticity; deterministic; cell fate; gene network; noise

Figure 1.

Several genetic motifs have been shown to reduce expression noise. Shown here are two of the most common noise buffering motifs. (a) Negative feedback loop (NFBL). (b) Coherent type‐1 feedforward loop (C1FFL).

Figure 2.

Genetic decision making is often characterised by hysteresis, where gene expression can be either high or low depending on the concentration of some input: usually another protein population or a small molecule species in the environment. (a) Varying the concentration of Input 1 reveals a region in which gene expression has two possible steady states. In a noise‐free system, this choice depends on the system's initial conditions. (b) If the bistable region is dependent on 2 inputs, their effects on the bistable region's shape can be plotted as a stability map. The dotted line indicates the concentration of Input 2 that would produce plot (a).

Figure 3.

Cell fate determination and stability can be thought of as a potential landscape. (a) In a noiseless system, the orange ball will remain in the central valley. As noise is added, the ball's motion may overcome the peaks to either side of it, allowing it to differentiate into either the teal or green states. Because there is a lower potential barrier to the right, the ball is more likely to jump to the green state. (b) The other means of differentiation is in changing the landscape itself. If the central well is removed, in the absence of noise the ball will roll to the left. Adding a small amount of noise allows the ball to occasionally be pushed right. This makes differentiation in both directions possible, although a higher incidence of the ball settling in the teal state is expected.



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Menn, David J, and Wang, Xiao(Apr 2014) Stochastic and Deterministic Decision in Cell Fate. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0025319]