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Electromagnetism & Resonant Recognition Model
Bio-macromolecules are guided by fields generated from energy of delocalised electrons

Pablo Andueza Munduate

The Resonant Recognition Model (RRM) is a theory proposed by Cosic et al [1] and experimentally started to be proved, where it is considered a resonant electromagnetic (EM) energy and information transfer between interacting biomolecules (for example proteins) specific for each function that is calculated considering the energy of delocalised electrons of each amino acid. ...

This theory predicts that macromolecular activity is based on electromagnetic resonances, the delocalised electrons moving along macromolecular (protein, DNA, RNA) backbone-like helical structure, can produce electromagnetic radiation, absorption and resonance with spectral characteristics corresponding to energy distribution along macromolecule. The calculus is done as described in [2], here a presentation:

" All proteins, DNA and RNA can be considered as a linear sequence of their constitutive elements: amino acids or nucleotides. The RRM model interprets this linear information as a numerical series by assigning each amino acid a physical parameter representing the energy of delocalised electrons of each amino acid and then transforming this numerical series into the frequency domain using Fourier Transform."

They discovered that each specific biological function within the protein or DNA is characterised by one frequency, that all protein sequences with the common biological function have common frequency component, related to the protein biological function, and that proteins and their targets have the same characteristic frequency in common. [1]

The theory has been corroborated by some experimental findings, for example Murugan et al. [3] show that evola strains that are lethal and not lethal have different biophotonic emmissions (there is a section [4] dedicated to biophotons in this web), and that those emissions can be calculated with a very precission (about 10 nm) based on RRM calculus. Also the fluctuating wavelengths of ultraweak photon emissions (UPE) (or biophoton emissions) from stressed Cancer cells can be calculated based on this theory.

Even complete cellular signaling pathways can be described in this electromagnetic resonance terms, Karbowki et al. [4] wrote:

" Cosic discovered that spectral analyses of a protein sequence after each constituent amino acid had been transformed into an appropriate pseudopotential predicted a resonant energy between interacting molecules. Several experimental studies have verified the predicted peak wavelength of photons within the visible or near-visible light band for specific molecules. Here, this concept has been applied to a classic signaling pathway, JAK–STAT, traditionally composed of nine sequential protein interactions. The weighted linear average of the spectral power density (SPD) profiles of each of the eight “precursor” proteins displayed remarkable congruence with the SPD profile of the terminal molecule (CASP-9) in the pathway. These results suggest that classic and complex signaling pathways in cells can also be expressed as combinations of resonance energies."

The same occur for classic ERK-MAP signaling pathways between the plasma cell membrane and the nucleus [5],

" Spectral analyses of sequences of pseudopotentials that reflect de-localized electrons of amino acids for the 11 proteins in the pathway were computed. The spectral power density of the terminal protein (cFOS) was shown to be the average of the profiles of the precursor proteins. The results demonstrated that in addition to minute successive alterations in molecular structure wave-functions and resonant patterns can also describe complex molecular signaling pathways in cells. Different pathways may be defined by a single resonance profile."

Cosic, based on his theory also proposed some therapeutic aplication, for example to neutralize malaria parasite [5]. In this site there are sections [6] dedicated to low level light therapy and experiments where light is applied to achieve different biological responses and where the mechanism by which those effects are provoked can be related in most cases to resonant effects like those predicted by the RRM, moreover there are two sections [7,8] where are listed papers that specifically atribute it to consecuences derived from RRM.

Also is interesting to note an fruitful attempt to detect Hepatitis C Virus [9] based on electromagnetic detection of the resonant frequency (as predicted by Cosic).

It must be said that there are some alternative resonance models that also are going to be included in this section, for example [10] propose a model that differ some to that of RRM:

" Compared to this previous work, our contribution is twofold. First, whereas the determination of RRM-based hotspots initially requires the computation of the characteristic frequency of a family of proteins, we do not impose such a constraint. Second, rather than a purely DSP-based approach as in [2], [14]–[17] aimed at detecting local residues associated with the characteristic frequency, we combine DSP tools and mutagenesis principles."

Or the model of [11] that modified RRM by using the wavelet transform.

The importance of the electromagnetic resonant recognition between macromolecules in regard to an electromagnetic mind theory is that this recognition fields are another information field, one to incorporate to the more general and multi-level electromagnetic mind (of the cell, of the brain, of the body, etc..,).

References:

1. Ćosić, Irena, et al. "Electromagnetic properties of biomolecules." FME Transactions 34.2 (2006): 71-80.

2. Cosic, Irena, Drasko Cosic, and Katarina Lazar. "Is it possible to predict electromagnetic resonances in proteins, DNA and RNA?." EPJ Nonlinear Biomedical Physics 3.1 (2015): 1.

3. Murugan, Nirosha J., Lukasz M. Karbowski, and Michael A. Persinger. "Cosic’s Resonance Recognition Model for Protein Sequences and Photon Emission Differentiates Lethal and Non-Lethal Ebola Strains: Implications for Treatment." Open Journal of Biophysics 5.01 (2014): 35.

4. Karbowski, Lukasz M., Nirosha J. Murugan, and Michael A. Persinger. "Novel Cosic resonance (standing wave) solutions for components of the JAK–STAT cellular signaling pathway: A convergence of spectral density profiles." FEBS open bio 5.1 (2015): 245-250.

5. Cosic, Irena, JoseLuis Hernandes Caceres, and Drasko Cosic. "Possibility to interfere with malaria parasite activity using specific electromagnetic frequencies." EPJ Nonlinear Biomedical Physics 3.1 (2015): 1.

6. EMMIND › Applied Fields - Experimental › Light & Near-Light Effects ›

7. EMMIND › Applied Fields - Experimental › Light & Near-Light Effects › Resonant Recognition Model - Infrared

8. EMMIND › Applied Fields - Experimental › Light & Near-Light Effects › Light - Resonant Recognition Model

9. Shiha, G., et al. "1174 A Novel Method for Non-Invasive Diagnosis of Hepatitis C Virus Using Electromagnetic Signal Detection: A Multicenter International Study." Journal of Hepatology 58 (2013): S477.

10. Nguyen, Quang-Thang, Ronan Fablet, and Dominique Pastor. "Protein interaction hotspot identification using sequence-based frequency-derived features." IEEE Transactions on Biomedical Engineering 60.11 (2013): 2993-3002.

11. Liu, Xiang, and Yifei Wang. "A modified resonant recognition model to predict protein-protein interaction." Frontiers of Biology in China 2.3 (2007): 268-271.

Very related sections:

expand this introductory text

text updated: 17/05/2016
tables updated: 24/07/2017

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