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Fundamental limits on the suppression of molecular fluctuations

Ioannis Lestas1, Glenn Vinnicombe1, and Johan Paulsson2

1 Department of Engineering, University of Cambridge

2 Department of Systems Biology, Harvard University

Abstract

Negative feedback is common in biological processes and can increase a system’s stability to

internal and external perturbations. But at the molecular level, control loops always involve

signaling steps with finite rates for random births and deaths of individual molecules. By

developing mathematical tools that merge control and information theory with physical chemistry

we show that seemingly mild constraints on these rates place severe limits on the ability to

suppress molecular fluctuations. Specifically, the minimum standard deviation in abundances

decreases with the quartic root of the number of signaling events, making it extraordinarily

expensive to increase accuracy. Our results are formulated in terms of experimental observables,

and existing data show that cells use brute force when noise suppression is essential, e.g.

transcribing regulatory genes 10,000s of times per cell cycle. The theory challenges conventional

beliefs about biochemical accuracy and presents an approach to rigorously analyze poorly

characterized biological systems.

Life in the cell is a complex battle between randomizing and correcting statistical forces:

births and deaths of individual molecules create spontaneous fluctuations in

abundances1,2,3,4 – noise – while many control circuits have evolved to eliminate, tolerate

or exploit the noise5,6,7,8. The net outcome is difficult to predict because each control

circuit in turn consists of probabilistic chemical reactions. For example, negative feedback

loops can compensate for changes in abundances by adjusting the rates of synthesis or

degradation7, but such adjustments are only certain to suppress noise if the individual

deviations immediately and surely affect the rates5. Even the simplest transcriptional

autorepression by contrast involves gene activation, transcription and translation,

introducing intermediate probabilistic events that can randomize or destabilize control.

Negative feedback may thus either suppress or amplify fluctuations depending on the exact

mechanisms, reaction steps and parameters9 – details that are difficult to characterize at the

single cell level and that differ greatly from system to system. This raises a fundamental

question: to what extent is biological noise inevitable and to what extent can it be

controlled? Could evolution simply favor networks – however elaborate or ingeniously

designed – that enable cells to homeostatically suppress any disadvantageous noise, or does

the nature of the mechanisms impose inherent constraints that cannot be overcome?

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Correspondence and requests for materials should be addressed to gv@eng.cam.ac.uk or johan_paulsson@hms.harvard.edu.

Supplementary Information is linked to the online version of the paper at www.nature.com/nature

Author contributions The three authors (I.L., G.V., and J.P.) contributed equally, and all conceived the study, derived the equations,

and wrote the paper.

Author information Reprints and permissions information is available at npg.nature.com/reprints. The authors declare no competing

financial interests.

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Published in final edited form as:

Nature. 2010 September 9; 467(7312): 174–178. doi:10.1038/nature09333.

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Control is limited by information loss

To address this question without oversimplifying or guessing at the complexity of cells, we

consider a chemical species X1 that affects the production of a second species X2, which in

turn indirectly controls the production of X1 via an arbitrarily complicated reaction network

with any number of components, nonlinear reaction rates, or spatial effects (Fig. 1). For

generality, we only specify three of the chemical events of the larger network:

(1)

where x1 and x2 are numbers of molecules per cell, the birth and death rates are probabilistic

reaction intensities, τ1 is the average lifetime of X1 molecules, f is a specified rate function,

and the unspecified control network allows u to be dynamically and arbitrarily set by the full

time history of X2 values. Death events for X2 are omitted because the results we derive

rigorously hold for all types and rates of X2 degradation mechanisms, as long as they do not

depend on X1. The generality of u and f allows X1 to represent many different biological

species: an mRNA with X2 as the corresponding protein, a protein with X2 as either its own

mRNA or an mRNA downstream in the control pathway, an enzyme with X2 as a product,

or a self-replicating DNA with X2 as a replication control molecule.

The arbitrary birth rate u represents a hypothetical ‘control demon’ that knows everything

about past and present values of x2 and uses this information to minimize the variance in x1.

This corresponds to an optimal reaction network capable of any type of time-integration,

frequency-based control, spatially extended dynamics, or other exotic actions. The sole

restriction is that the control system depends on x1 only via reaction (iii), an example of a

common chemical signaling relay where a concentration determines a rate. Because

individual X2 birth events are probabilistic, some information about X1 is then inevitably

and irrecoverably lost and the current value of X1 cannot be perfectly inferred from the X2

time-series. Specifically, the number of X2 birth events in a short time period is on average

proportional to f(x1), with a statistical uncertainty that depends on the average number of

events. If x1 remained constant, the uncertainty could be arbitrarily reduced by integrating

over a longer time, but because it keeps changing randomly on a time scale set by τ1,

integration can only help so much. The problem is thus equivalent to determining the

strength of a weak light source by counting photons: each photon emission is probabilistic,

and if the light waxes and wanes, counts from the past carry little information about the

current strength. The otherwise omniscient control demon thus cannot know the exact state

of the component it is trying to control.

We then quantify how finite signaling rates restrict noise suppression, without linearizing or

otherwise approximating the control systems, by analytically deriving a feedback-invariant

upper limit on the mutual information10 between X1 and X2 – an information-theoretic

entropic measure for how much knowing one variable reduces uncertainty about another –

and derive lower bounds on variances in terms of this limit. We use a continuous stochastic

differential equation for the dynamics of species X1, an approximation that makes it easier to

extend the results to more contexts and processes, but keep the signaling and control

processes discrete. After considerable dust has settled, this theory (summarized in Box 1 and

detailed in the Supplementary Information, SI) allows us to calculate fundamental lower

bounds on variances.

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Box 1

Outline of underlying theory

Statistical uncertainties and dependencies are often measured by variances and

correlation coefficients, but both uncertainty and dependence can also be defined purely

in terms of probabilities (pi), without considering the actual states of the system. The

Shannon entropy H (X) = Σpilogpi measures inherent uncertainty rather than how

different the outcomes are, and the mutual information between random variables I (X1;

X2) = H (X1)–H (X1|X2) measures how much knowing one variable reduces entropic

uncertainty in another, regardless of how their outcomes may correlate10,27. Despite the

fundamental differences between these measures, however, there are several points of

contact that can be used to predict limits on stochastic behavior.

First, because imperfectly estimating the state of a system fundamentally restricts the

ability to control it (SI), there is a hard bound on variances whenever there is incomplete

mutual information between the signal X2 and the controlled variable X1. We quantify

the bound by means of Pinsker’s nonanticipatory epsilon entropy28, a rarely utilized

information-theoretic concept that exploits the fact that the transmission of information in

a feedback system must occur in real time. This shows (SI) how an upper bound on the

mutual information I (X1; X2) – i.e. a limited Shannon capacity in the channel from X1 to

X2 – imposes a lower bound on the mean squared estimation error E (X1X̂1)2, where the

‘estimator’ X̂1 is an arbitrary function of the discrete signal X2 time series and the X1

dynamics at equilibrium is described by a stochastic differential equation. Since the

capacity of the molecular channels we consider is not increased by feedback, this results

in a lower limit in the variance of X1, in terms of the channel capacity C, that holds for

arbitrary feedback control laws: .

Second, the Shannon capacity is potentially unlimited when information is sent over

point process ‘Poisson channels’29,

a controlled variable affects the rate of a probabilistic signaling event. However, infinite

capacity requires that the rate f (x1) is unrestricted and thus that X1 is unrestricted –

contrary to the purpose of control. Here we consider two types of restrictions. First, if the

rate has an upper limit fmax it follows30 that C=K<f> where K= log(fmax/<f>). The

channel capacity then equals the average intensity multiplied by the natural logarithm of

the effective dynamic range fmax/<f>, and the noise bound follows

. This allows for any nonlinear function f (x1) but, for specific

functions, restricting the variance in x1 can further reduce the capacity. For example, we

analytically show that the capacity of the generic Poisson channel subject to mean and

, as in stochastic reaction networks where

variance constraints follows

variance in f and thereby make it harder to transmit the information that is fundamentally

required to reduce noise. Combining this expression for the channel capacity with the

feedback limit above reveals hard limits beyond which no improvements can be made:

any further reduction in the variance would require a higher mutual information, which is

impossible to achieve without instead increasing the variance. When f is linear in x1 this

produces the result in Eq. (2). Analogous calculations allow us to derive capacity and

noise results when f is a Hill function, or for processes with bursts, extrinsic noise,

parallel channels, and cascades (SI). Finite channel capacities are the only fundamental

constraints considered here, so at infinite capacity perfect noise suppression is possible

by construction.

. Having less noise in x1will reduce the

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Noise limited by 4th root of signal rate

When the rate of making X2 is proportional to X1, f =αx1, for example when X1 is a

template or enzyme producing X2, the hard lower bound on the (squared) relative standard

deviation created by the loss of information follows:

(2)

where <…> denotes population averages and N1 = <u>τ1 = <x1> and N2 = α<x1>τ1 are the

numbers of birth events of X1 and X2 made on average during time τ1. Thus no control

network can significantly reduce noise when the signal X2 is made less frequently than the

controlled component. When the signal is made more frequently than the controlled

component, the minimal relative standard deviation (square root of Eq. (2)) at most

decreases with the quartic root of the number of signal birth events. Reducing the standard

deviation of X1 10-fold thus requires that the signal X2 is made at least 10,000 times more

frequently. This makes it hard to achieve high precision, and practically impossible to

achieve extreme precision, even for the slowest changing X1 in the cell where the signals X2

may be faster in comparison.

Systems with nonlinear amplification before the infrequent signaling step are also subject to

bounds. For arbitrary nonlinear encoding where f is an arbitrary functional of the whole x1

time history – corresponding to a second control demon between X1 and X2 – the quartic

root limit turns into a type of square root limit (Box 1 and SI). However, gene regulatory

functions typically saturate at full activation or leak at full repression, as the generalized Hill

function with K1<K2. Here X1 may be an activator or repressor, and

X2 an mRNA encoding either X1 or a downstream protein. Without linearizing f or

restricting the control demon, an extension of the methods above (SI) reveals similar quartic

root bounds as in Eq. (2), with the difference that N2 is replaced by γN2,max where γ is on

the order of one in a wide range of biologically relevant parameters (SI), and N2,max= vτ1 =

N2 v/<f>. Cells can then produce much fewer signal molecules without reducing the

information transfer, depending on the maximal rate increase v/<f>, but the quartic root

effect still strongly dampens the impact on the noise limit. If X2 is an mRNA, N2,max is also

limited because transcription events tend to be relatively rare even for fully expressed genes.

Many biological systems show much greater fluctuations due to upstream sources of noise,

or sudden ‘bursts’ of synthesis4,11,12. If X1 molecules are made or degraded in bursts (size

b1, averaged over births and deaths) there is much more noise to suppress, and if signal

molecules X2 are produced in bursts (size b2) each independent burst only counts as a single

signaling event in terms of the Shannon information transfer, and:

(3)

The effective average number of molecules or events is thus reduced by the size of the burst,

which can increase the noise limits greatly in many biological systems. The effect of slower

upstream fluctuations in turn depends on their time-scales, how they affect the system, and

whether or not the control system can monitor the source of such noise directly. If noise in

the X1 birth rate is extrinsic to X1 but not directly accessible by the controller, the predicted

noise suppression limits can follow similar quartic root principles for both fast and slow

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extrinsic noise, while for intermediate time-scales the power-law is between 3/8 and ¼ (SI,

and Fig 2).

Information losses in cascades

Signaling in the cell typically involves numerous components that change in probabilistic

events with finite rates. Information about upstream states is then progressively lost at each

step much like a game of ‘broken telephone’ where messages are imperfectly whispered

from person to person. If each signaling component Xi+1 decays exponentially and is

produced at rate αixi, an extension of the theory (SI) shows that if a control demon monitors

Xn+1 and controls X1, N2 above is replaced by

(4)

where Nj is the average number of birth events (or bursts, as in Eq. (3)) of species j during

time period τ1. Information transfer in cascades is thus limited by the components made in

the lowest numbers, and because the total average number of birth events over the n steps

obeys Ntot≥ n2Neff, a five-step linear cascade requires at least 25 times more birth events to

maintain the same capacity to suppress noise as a single-step mechanism. This effect of

information loss is superficially similar to noise propagation where variation in inputs cause

variation in outputs, but though both effects reflect the probabilistic nature of infrequent

reactions, the governing principles are very different. In fact, the mechanisms for preventing

noise propagation – such as time-averaging or kinetic robustness to upstream changes6 –

cause a greater loss of information, while mechanisms that minimize information losses –

such as all-or-nothing nonlinear effects13 – instead amplify noise. Large variation in

signaling intermediates is thus not necessarily a sign of reduced precision but could reflect

strategies to minimize information loss, which in turn allows tighter control of downstream

components.

The rapid loss of information in cascades also suggests another trade-off: effective control

requires a combination of appropriately nonlinear responses and small information losses,

but nonlinear amplification in turn requires multiple chemical reactions with a loss of

information at each step. The actual bounds may thus be much more restrictive than

predicted above, where assuming Hill functions or arbitrary control networks conceals this

trade-off. One of the greatest challenges in the cell may be to generate appropriately

nonlinear reaction rates without losing too much information along the way.

Parallel signal and control systems can instead improve noise suppression, since each

signaling pathway contributes independent information about the upstream state. However,

for a given total number of signaling events, parallel control cannot possibly reduce noise

below the limits above: the loss of information is determined only by the total frequency of

the signaling events, not their physical nature. The analyses above in fact implicitly allow

for arbitrarily parallel control with f interpreted as the total rate of making control molecules

affected directly by X1 (SI).

Systems selected for noise suppression

The results above paint a grim picture for suppression of molecular noise. At first glance this

seems contradicted by a wealth of biological counterexamples: molecules are often present

in low numbers, signaling cascades where one component affects the rates of another are

ubiquitous, and yet many processes are extremely precise. How is this possible if the limits

apply universally? First, the transmission of chemical information is not fundamentally

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