Mutual information upper bound of molecular communication based on cell metabolism

Pierobon, Massimiliano; Sakkaff, Zahmeeth; Catlett, Jennie L.; Buan, Nicole R.

doi:10.1109/spawc.2016.7536835

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…Although it is outside of the scope of this work, we also remark that other computational techniques have successfully been used to build on CBM approaches and study the multiomic nature of various organisms. These include Bayesian [18] and metaheuristic optimization algorithms [19,20], as well as methods drawn from the theory of games [21], graphs [22], Markov chains [23], and information [24].…”

Section: Introductionmentioning

confidence: 99%

Machine and deep learning meet genome-scale metabolic modeling

et al. 2019

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Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

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Section: Introductionmentioning

confidence: 99%

Machine and deep learning meet genome-scale metabolic modeling

et al. 2019

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“…In Fig. 6, the elements corresponding to the case where a type-2 diabetes patient is part of the IoBNT network [45] [50], is sketched. A nano device carries out the action of sensing the flux of charged proteins through the convoluted tubule where the urine is formed.…”

Section: Elements Of the Iobnt Network Applied To Nanomedicinementioning

confidence: 99%

Prospective Usage of the Internet of Bio-Nano Things For The Very Anticipated Detection of Charged Giants Proteins in Type-2 Diabetes Patients: Stopping the Progress of Diabetic Nephropathy with Nanomedicine

Nieto-Chaupis¹

2018

Proceedings of the 16th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Innovation in Educat

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A natural upgrade of the Internet of Things (IoT) and the Internet of Nano-Things (IoNT) is the so-called Internet of Bio-Nano Things by the which the organs of the human body would be part of a communication's network and emitting signals continuously. In this paper we have paid attention to the prospective scheme of surveillance that aims to measure the charged bunches of proteins which are fluxing through the renal glomerulus. We propose the idea that these bunches of albumin might have anomalous displacements out the glomerulus due to the presence of dipoles of glucose. Our theoretical formulation consists essentially in a physics model. Under this view, the electrodynamics of albumin is governed by repulsive and attractive forces that pushes out theses proteins to the spaces of urine formation. For this end, we apply electrodynamics to calculate the electric potential and fields. All these processes might be seen as the very beginning of the well-known diabetes kidney disease. Under the assumption that the presence of a nanodevice contains capabilities for sensing the bunches of albumin, we estimated the efficiencies of charge detection using the Monte Carlo method. We present simulations about the possible tracks of albumin after of their interaction with others charged negatively cells like podocytes. The present analysis has considered others charged proteins which would play the role of noise or background. This methodology is embedded in the framework of the IoBNT and the respective quality of service is estimated as well.

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“…Similar analyses could elucidate the limits of electrochemical communication channels. Such an analysis would also be key in determining the major participants in the information flow through a single cell ( Pierobon et al., 2016 ), and throughout a synthetic communication network ( Harper et al., 2018 ). This issue is fundamental to redox communication, which uses semi-specific molecular interactions.…”

Section: Introductionmentioning

confidence: 99%

Redox Electrochemistry to Interrogate and Control Biomolecular Communication

et al. 2020

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Summary Cells often communicate by the secretion, transport, and perception of molecules. Information conveyed by molecules is encoded, transmitted, and decoded by cells within the context of the prevailing microenvironments. Conversely, in electronics, transmission reliability and message validation are predictable, robust, and less context dependent. In turn, many transformative advances have resulted by the formal consideration of information transfer. One way to explore this potential for biological systems is to create bio-device interfaces that facilitate bidirectional information transfer between biology and electronics. Redox reactions enable this linkage because reduction and oxidation mediate communication within biology and can be coupled with electronics. By manipulating redox reactions, one is able to combine the programmable features of electronics with the ability to interrogate and modulate biological function. In this review, we examine methods to electrochemically interrogate the various components of molecular communication using redox chemistry and to electronically control cell communication using redox electrogenetics.

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Mutual information upper bound of molecular communication based on cell metabolism

Cited by 14 publications

References 21 publications

Machine and deep learning meet genome-scale metabolic modeling

Machine and deep learning meet genome-scale metabolic modeling

Prospective Usage of the Internet of Bio-Nano Things For The Very Anticipated Detection of Charged Giants Proteins in Type-2 Diabetes Patients: Stopping the Progress of Diabetic Nephropathy with Nanomedicine

Redox Electrochemistry to Interrogate and Control Biomolecular Communication

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