scholarly journals Nearly maximal information gain due to time integration in central dogma reactions

2022 ◽  
Author(s):  
Swarnavo Sarkar ◽  
Jayan Rammohan
Keyword(s):  
Rate Constants ◽  
Cellular Response ◽  
Channel Model ◽  
Information Gain ◽  
Time Integration ◽  
Information Channel ◽  
Information Theoretic ◽  
Central Dogma ◽  
New Information ◽  
In Series

Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we show that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain due to time integration while also keeping the loss due to stochasticity in translation relatively low (< 0.5 bits).

scholarly journals Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199958
Author(s):  
Larkin Folsom ◽  
Masahiro Ono ◽  
Kyohei Otsu ◽  
Hyoshin Park
Keyword(s):  
Path Planning ◽  
Lower Risk ◽  
Information Gain ◽  
Probability Distributions ◽  
Single Agent ◽  
Uncertain Environments ◽  
Information Theoretic ◽  
Highly Correlated ◽  
Naive Approach

Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route.

Nitrogen removal characteristics of nitrification and denitrification filters

1994 ◽  
Vol 29 (10-11) ◽  
pp. 409-416 ◽  
Author(s):  
F. Çeçen ◽  
I. E. Gönenç
Keyword(s):  
Nitrogen Removal ◽  
Rate Constants ◽  
Order Rate Constant ◽  
Zero Order ◽  
Ammonia Removal ◽  
Nitrite Accumulation ◽  
Order Kinetic ◽  
Order Rate ◽  
In Series ◽  
Nitrification And Denitrification

The kinetics of nitrogen removal was studied in upflow submerged nitrification and denitrification filters in series. Nitrification followed first-, half-, and zero-order kinetics. For the half-order range the half-order rate constant was about 0.9gNH4-N1/2m−1/2d−1. The zero-order rate constants for the DO ranges of 2-3 mg/L and 4-5 mg/L were found as 0.47 gNH4-Nm−2d−1 and 1.82 gNH4-Nm−2d−1, respectively. In the zero-order region ammonia removal proceeded as a half-order reaction in oxygen concentration and the half-order rate constants were about 1.4-2.7 gO21/2m−1/2d−1. Nitrite accumulation reached a considerable degree at bulk oxygen to bulk ammonia ratios lower than 5 since the formation of nitrate was inhibited. Similar to nitrification half- and zero-order kinetic regions were also observed in denitrification. The half- and zero-order rate constants for carbon unlimited cases (influent COD/NOx-N&gt;5) were about 0.23 gNOx-N1/2m−1/2d−1 and 1.9 gNOx-Nm−2d−1, respectively. The nitrite produced in the nitrification stage could be reduced in denitrification. The removal kinetics in the presence of nitrite was found to be similar to the kinetics when the influent consisted of nitrate only.

scholarly journals Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets

Entropy ◽  
2018 ◽  
Vol 20 (7) ◽  
pp. 540 ◽  
Author(s):  
Subhashis Hazarika ◽  
Ayan Biswas ◽  
Soumya Dutta ◽  
Han-Wei Shen
Keyword(s):  
Weather Forecasting ◽  
Conditional Entropy ◽  
Specific Information ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Information Measures ◽  
New Information ◽  
Real World Datasets ◽  
The Individual ◽  
Information Theoretic Approach

Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments.

scholarly journals Image-model coupling: a simple information theoretic perspective for image sequences

2009 ◽  
Vol 16 (2) ◽  
pp. 197-210
Author(s):  
N. D. Smith ◽  
C. N. Mitchell ◽  
C. J. Budd
Keyword(s):  
Channel Model ◽  
Coupling Model ◽  
Model Coupling ◽  
Model Output ◽  
Image Model ◽  
Physical Processes ◽  
Information Theoretic ◽  
Discrete Channel ◽  
History Of ◽  
Earth’S Climate

Abstract. Images are widely used to visualise physical processes. Models may be developed which attempt to replicate those processes and their effects. The technique of coupling model output to images, which is here called "image-model coupling", may be used to help understand the underlying physical processes, and better understand the limitations of the models. An information theoretic framework is presented for image-model coupling in the context of communication along a discrete channel. The physical process may be regarded as a transmitter of images and the model as part of a receiver which decodes or recognises those images. Image-model coupling may therefore be interpreted as image recognition. Of interest are physical processes which exhibit "memory". The response of such a system is not only dependent on the current values of driver variables, but also on the recent history of drivers and/or system description. Examples of such systems in geophysics include the ionosphere and Earth's climate. The discrete channel model is used to help derive expressions for matching images and model output, and help analyse the coupling.

scholarly journals An Information-Theoretic Framework for Complex Systems

2018 ◽  
Vol 140 (12) ◽  
pp. S16-S23
Author(s):  
Hanieh Agharazi ◽  
Wanchat Theeranaew ◽  
Kolacinski Richard M. ◽  
Kenneth A. Lopaor
Keyword(s):  
Communication Network ◽  
Complex Systems ◽  
Information Channel ◽  
Information Theoretic

We propose an information-theoretic framework for modeling complex systems as a communication network where physical devices can be organized into subsystems and subsystems are communicating through an information channel governed by the dynamics of the system.

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