Biomolecular systems like molecular motors or pumps, transcription and translation machinery, and other enzymatic reactions can be described as Markov processes on a suitable network. We show quite generally that in a steady state the dispersion of observables like the number of consumed/produced molecules or the number of steps of a motor is constrained by the thermodynamic cost of generating it. An uncertainty ǫ requires at least a cost of 2kB T /ǫ 2 independent of the time required to generate the output.PACS numbers: 05.70.Ln, Biomolecular processes are generally out of equilibrium and dissipative, with the associated free energy consumption coming most commonly from adenosine triphosphate (ATP) hydrolysis. The role of energy dissipation in a variety of processes related to biological information processing has received much attention recently [1][2][3][4][5][6][7][8][9][10][11][12][13][14], to give just one class of examples for which one tries to uncover fundamental limits involving energy dissipation in biomolecular systems.Chemical reactions catalyzed by enzymes are of central importance for many cellular processes. Prominent examples are molecular motors [15][16][17][18][19], which convert chemical free energy from ATP into mechanical work. In this case an observable of interest is the number of steps the motor made. Another commonly analyzed output in enzymatic kinetics is the number of product molecules generated by an enzymatic reaction, for which the Michaelis-Menten scheme provides a paradigmatic case [2].Quite generally, chemical reactions are well described by stochastic processes. An observable, like the rate of consumed substrate molecules or the number of steps of a motor on a track, is a random variable subjected to thermal fluctuations. Single molecule experiments [20][21][22][23][24] provide detailed quantitative data on such random quantities. Obtaining information about the underlying chemical reaction scheme through the measurement of fluctuations constitutes a field called statistical kinetics [25][26][27][28]. A central result in this field is the fact that the Fano factor quantifying fluctuations provides a lower bound on the number of states involved in an enzymatic cycle [15,28].For a non-zero mean output, the chemical potential difference (or affinity) driving an enzymatic reaction must also be non-zero, leading to a free energy cost. Is there a fundamental relation between the relative uncertainty associated with the observable quantifying the output and the free energy cost of sustaining the biomolecular process generating it?In this Letter, we show that such a general bound does indeed exist. Specifically, for any process running for a time t, we show that the product of the average dissipated heat and the square of the relative uncertainty of a generic observable is independent of t and bounded by 2k B T . This uncertainty relation is valid for general networks and can be proved within linear response theory. Beyond linear response theory, we show it analytically for unicyclic...