An official website of the United States government
The natural communication ability of cells is explored in this paper by providing preliminary results in the estimation of the Mutual Information (MI) of signaling pathway communication channels. These results, based on an application of Molecular Communication (MC) and information theory concepts to multi-scale integrated Flux-Balance Analysis (iFBA) models are a first step to evaluate the potential of cells and their biochemical processes as a substrate for enabling engineered MC channels for the future internet of Bio-Nano Things.
more »
« less
Optimizing Information Transfer Through Chemical Channels in Molecular Communication
The optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes underlying these channels poses challenges in adopting analytical approaches for their information-theoretic modeling and analysis. In this paper, a novel iterative methodology is proposed to numerically estimate achievable information rates. Based on the Nelder-Mead optimization, this methodology does not necessitate analytical for-mulations of MC components and their stochastic behavior, and, when applied to well-known scenarios, it demonstrates consistent results with theoretical bounds and superior performance to prior literature. A numerical example that abstracts communications between genetically engineered cells via simulation is presented and discussed in light of possible future applications to support the design and engineering of realistic MC systems.
more »
« less
- Award ID(s):
- 1816969
- PAR ID:
- 10377517
- Date Published:
- Journal Name:
- 2021 IEEE Global Communications Conference (GLOBECOM)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The realization of interfaces between the biological world and elec- tronics has the potential to propel the field of Molecular Commu- nication (MC) to novel frontiers. Plugging MC-enabled devices to our electrical cyber-world will enable revolutionary applications, especially in the biomedical field. By stemming from a seminal proof-of-concept prototype that enables communication between a biological system and an electrical circuit, based on redox biochem- ical reactions, this paper introduces the first frequency analysis of such system and its characterization in terms of communication performance (capacity). To achieve these results, made possible by a linearity property in the analytical model of the system, an em- pirical methodology is followed to obtain the frequency response and the noise power spectral density of the system from the results of a simulation framework. The latter was developed in prior work and made accessible publicly through a web app. A water filling capacity estimation algorithm is applied to the obtained results to give a preliminary idea on the communication performance of such system, which results in a transmission rate equivalent to 0.0587 bits/hour. While orders of magnitude slower than common electrical or optical communications, these results are in line with the inherent timescales of the biological systems envisioned to be interfaced with this technology.more » « less
-
Autonomous chemical process development and optimization methods use algorithms to explore the operating parameter space based on feedback from experimentally determined exit stream compositions. Measuring the compositions of multicomponent streams is challenging, requiring multiple analytical techniques to differentiate between similar chemical components in the mixture and determine their concentration. Herein, we describe a universal analytical methodology based on multitarget regression machine learning (ML) models to rapidly determine chemical mixtures' compositions from Fourier transform infrared (FTIR) absorption spectra. Specifically, we used simulated FTIR spectra for up to 6 components in water and tested seven different ML algorithms to develop the methodology. All algorithms resulted in regression models with mean absolute errors (MAE) between 0–0.27 wt%. We validated the methodology with experimental data obtained on mixtures prepared using a network of programmable pumps in line with an FTIR transmission flow cell. ML models were trained using experimental data and evaluated for mixtures of up to 4-components with similar chemical structures, including alcohols ( i.e. , glycerol, isopropanol, and 1-butanol) and nitriles ( i.e. , acrylonitrile, adiponitrile, and propionitrile). Linear regression models predicted concentrations with coefficients of determination, R 2 , between 0.955 and 0.986, while artificial neural network models showed a slightly lower accuracy, with R 2 between 0.854 and 0.977. These R 2 correspond to MAEs of 0.28–0.52 wt% for mixtures with component concentrations between 4–10 wt%. Thus, we demonstrate that ML models can accurately determine the compositions of multicomponent mixtures of similar species, enhancing spectroscopic chemical quantification for use in autonomous, fast process development and optimization.more » « less
-
Uncertainty analysis in the outcomes of model predictions is a key element in decision-based material design to establish confidence in the models and evaluate the fidelity of models. Uncertainty Propagation (UP) is a technique to determine model output uncertainties based on the uncertainty in its input variables. The most common and simplest approach to propagate the uncertainty from a model inputs to its outputs is by feeding a large number of samples to the model, known as Monte Carlo (MC) simulation which requires exhaustive sampling from the input variable distributions. However, MC simulations are impractical when models are computationally expensive. In this work, we investigate the hypothesis that while all samples are useful on average, some samples must be more useful than others. Thus, reordering MC samples and propagating more useful samples can lead to enhanced convergence in statistics of interest earlier and thus, reducing the computational burden of UP process. Here, we introduce a methodology to adaptively reorder MC samples and show how it results in reduction of computational expense of UP processes.more » « less
-
For the pulping process in a pulp & paper plant that uses woodchips as raw material, the moisture content (MC) of the woodchips is a major process disturbance that affects product quality and consumption of energy, water, and chemicals. Existing woodchip MC sensing technologies have not been widely adopted by the industry due to unreliable performance and/or high maintenance requirements that can hardly be met in a manufacturing environment. To address these limitations, we propose a non-destructive, economic, and robust woodchip MC sensing approach utilizing channel state information (CSI) from industrial Internet-of-Things (IIoT) based Wi-Fi. While these IIoT devices are small, low-cost, and rugged to stand for harsh environment, they do have their limitations such as the raw CSI data are often very noisy and sensitive to woodchip packing. Thus, direct application of machine learning (ML) algorithms leads to poor performance. To address this, statistics pattern analysis (SPA) is utilized to extract physically and statistically meaningful features from the raw CSI data, which are sensitive to woodchip MC but not to packing. The SPA features are then used for developing multiclass classification models as well as regression models using various linear and nonlinear ML techniques to provide potential solutions to woodchip MC estimation for the pulp and paper industry.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GLOBECOM46510.2021.9685247
