In this paper, we present a fluid-based experimental molecular communication (M C) testbed that, similar to the human cardiovascular system, operates in a closed circuit tube system. The proposed system is designed to be biocompatible, resource-efficient, and controllable from outside the tube. As signaling molecule, the testbed employs the green fluorescent protein variant “Dreiklang” (GFPD). GFPDs can be reversibly switched via light of different wavelengths between a bright fluorescent state and a less fluorescent state. Hence, this property allows for writing and erasing information encoded in the state of the G FPDs already present in the fluid via radiation from outside the tube. The concept of modulating the GFPDs existing in the channel at the transmitter for information transmission, instead of releasing new molecules, is a form of media modulation. In our testbed, due to the closed loop setup and the long experiment durations of up to 250 min, we observe new forms of inter-symbol interferences (ISI), which do not occur in short experiments and open loop systems. In particular, up to four different forms of ISI, namely channel ISI, inter-loop ISI, offset ISI, and permanent ISI, occur in the considered system. To mitigate inter-loop ISI and offset ISI, we propose a light based eraser unit. We experimentally demonstrate reliable information transmission in our testbed achieving error-free transmission of 500 bit at a data rate of 6 bit min−1based on a sub-optimal low-complexity detection scheme.

In conventional molecular communication (MC) systems, the signaling molecules used for information transmission are stored, released, and then replenished by a transmitter (TX). However, the replenishment of signaling molecules at the TX is challenging in practice. Furthermore, in most envisioned MC applications, e.g., in the medical field, it is not desirable to insert the TX into the MC system, as this might impair natural biological processes. In this paper, we propose the concept of media modulation based MC where the TX is placed outside the channel and utilizes signaling molecules already present inside the system. The signaling molecules can assume different states which can be switched by external stimuli. Hence, in media modulation based MC, the TX modulates information into the state of the signaling molecules. In particular, we exploit the group of photochromic molecules, which undergo light-induced reversible state transitions, for media modulation. We study the usage of these molecules for information transmission in a three-dimensional duct system, which contains an eraser, a TX, and a receiver for erasing, writing, and reading of information via external light, respectively. We develop a statistical model for the received signal which accounts for the distribution of the signaling molecules in the system, the initial states of the signaling molecules, the reliability of the state control mechanism, the randomness of irrepressible, spontaneous state switching, and the randomness of molecule propagation. We adopt a maximum likelihood detector and show that it can be reduced to a threshold based detector. Furthermore, we derive analytical expressions for the optimal threshold value and the resulting bit error rate (BER), respectively. Both the statistical model and BER results are verified by computer simulations. Our results reveal that media modulation enables reliable information transmission, validating it as a promising alternative to MC based on molecule emitting TXs.

In molecular communication (MC) systems, the expected number of molecules observed at the receiver over time after the instantaneous release of molecules by the transmitter is referred to as the channel impulse response (CIR). Knowledge of the CIR is needed for the design of detection and equalization schemes. In this paper, we present a training-based CIR estimation framework for MC systems which aims at estimating the CIR based on the observed number of molecules at the receiver due to emission of a sequence of known numbers of molecules by the transmitter. In particular, we derive maximum likelihood (ML) and least sum of square errors (LSSE) estimators. We also study the Cramer Rao (CR) lower bound and training sequence design for the considered system. Simulation results confirm the analysis and compare the performance of the proposed estimation techniques with the CR lower bound.

In molecular communication (MC) systems, the expected number of molecules observed at the receiver over time after the instantaneous release of molecules by the transmitter is referred to as the channel impulse response (CIR). Knowledge of the CIR is needed for the design of detection and equalization schemes. In this paper, we present a training-based CIR estimation framework for MC systems, which aims at estimating the CIR based on the observed number of molecules at the receiver due to emission of a sequence of known numbers of molecules by the transmitter. Thereby, we distinguish two scenarios depending on whether or not statistical channel knowledge is available. In particular, we derive maximum likelihood and least sum of square errors estimators, which do not require any knowledge of the channel statistics. For the case, when statistical channel knowledge is available, the corresponding maximum a posteriori and linear minimum mean square error estimators are provided. As performance bound, we derive the classical Cramer Rao (CR) lower bound, valid for any unbiased estimator, which does not exploit statistical channel knowledge, and the Bayesian CR lower bound, valid for any unbiased estimator, which exploits statistical channel knowledge. Finally, we propose the optimal and suboptimal training sequence designs for the considered MC system. Simulation results confirm the analysis and compare the performance of the proposed estimation techniques with the respective CR lower bounds.

MOTIVATION: Short linear peptide motifs mediate protein-protein interaction, cell compartment targeting and represent the sites of post-translational modification. The identification of functional motifs by conventional sequence searches, however, is hampered by the short length of the motifs resulting in a large number of hits of which only a small portion is functional. RESULTS: We have developed a procedure for the identification of functional motifs, which scores pattern conservation in homologous sequences by taking explicitly into account the sequence similarity to the query sequence. For a further improvement of this method, sequence filters have been optimized to mask those sequence regions containing little or no linear motifs. The performance of this approach was verified by measuring its ability to identify 576 experimentally validated motifs among a total of 15 563 instances in a set of 415 protein sequences. Compared to a random selection procedure, the joint application of sequence filters and the novel scoring scheme resulted in a 9-fold enrichment of validated functional motifs on the first rank. In addition, only half as many hits need to be investigated to recover 75% of the functional instances in our dataset. Therefore, this motif-scoring approach should be helpful to guide experiments because it allows focusing on those short linear peptide motifs that have a high probability to be functional.