Strategies for cellular decision-making

Abstract

Stochasticity pervades life at the cellular level. Cells receive stochastic signals, perform detection and transduction with stochastic biochemistry, and grow and die in stochastic environments. Here we review progress in going from the molecular details to the information-processing strategies cells use in their decision-making. Such strategies are fundamentally influenced by stochasticity. We argue that the cellular decision-making can only be probabilistic and occurs at three levels. First, cells must infer from noisy signals the probable current and anticipated future state of their environment. Second, they must weigh the costs and benefits of each potential response, given that future. Third, cells must decide in the presence of other, potentially competitive, decision-makers. In this context, we discuss cooperative responses where some individuals can appear to sacrifice for the common good. We believe that decision-making strategies will be conserved, with comparatively few strategies being implemented by different biochemical mechanisms in many organisms. Determining the strategy of a decision-making network provides a potentially powerful coarse-graining that links systems and evolutionary biology to understand biological design.

Figure 1

Factors influencing cellular decision-making. A cell senses signals generated by a change in the environment and must decide an appropriate response. This decision-making can depend on the cell's predictions for the current and future state of the environment based on the signals it has sensed, the short-term history of the cell, the expected benefits and the costs of each potential response, the actions of other cells, which may be competitive or cooperative, and the time taken to both decide and generate the response. The response may be a change in internal state or an action that changes the environment itself.

Figure 2

Biochemical networks may use statistical inference to infer the probable state of the extracellular environment. (A) Inference in an environment with two states corresponding to low and high amounts of extracellular sugar S. The state low in sugar generates the blue distribution of intracellular sugar; the state high in sugar generates the red intracellular distribution. The environmental state is ambiguous for intracellular concentrations of sugar lying in the overlap between the two distributions. The Bayesian posterior probability of the state high in sugar given intracellular levels of S is the black, sigmoid-like curve. (B) The response function of the lac operon measured by its rate of transcription in populations of E. coli as a function of the chemical IPTG, a non-hydrolysable version of the sugar lactose, and cyclic AMP (cAMP), whose concentration in vivo is inversely proportional to the concentration of glucose (Makman and Sutherland, 1965). Data taken from Setty et al (2003). In the interpretation of Libby et al, the extracellular environment has two states: one high in lactose (IPTG) and low in glucose (high cAMP), and the other low in lactose and high in glucose (low cAMP). (C) An example of the probability distributions for lactose and cAMP in the two extracellular states (Libby et al, 2007). If the extracellular environment has two states with these distributions, then the response function measured in panel B is similar to the posterior probability of the state high in lactose and low in glucose (high in cAMP). (D) The posterior probability of the state high in lactose and low in glucose given the two distributions in panel C. Compare with the measured response function in panel B.

Figure 3

Cells may evolve to make optimal decisions. (A) The cost of expressing the lac operon in E. coli is measured by the reduction in relative growth rate when the operon is expressed in environments without lactose. The red curve is given by a fit of equation (4). (B) Once the cost has been found, the benefit of expressing the operon can be obtained by measuring the increase in the relative growth rate when the operon is fully expressed in environments with different amounts of extracellular lactose. The red curve is given by a fit of equation (5) with equation (6). Data in panels A and B are from Dekel and Alon (2005). (C) The level of the expression of the lac operon, Z, under conditions of zero glucose. Z_WT is the level of expression of the operon when fully induced. Data are from Kalisky et al (2007). The red curve is the predicted level of expression of the operon by Kalisky et al found by maximizing the benefit minus the cost as a function of the extracellular concentration of lactose (equation (7)). Bars indicate standard errors throughout.

Figure 4

The tragedy of the commons. Blue cooperator cells secrete enzymes, shown by a pentagon, which hydrolyse an extracellular metabolite, shown as two joined circles, into a form that cells can import (two separated circles). The enzymatic reaction is highlighted within the dotted circle. Green cheater cells benefit from the cooperative action of synthesizing the enzyme by importing the hydrolysed molecules. They do not, however, pay the associated cost because they do not synthesize the enzyme themselves, and hence have a growth advantage. As the number of cheater cells grows, the resource is used less and less efficiently, and the fitness of the population of cells decreases.