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A Digital Communication Analysis of Gene Expression of Proteins in Biological Systems: A Layered Network Model View

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Abstract

Background/Introduction

Biological communication is a core component of biological systems, mainly presented in the form of evolution, transmitting information from a generation to the next. Unfortunately, biological systems also include other components and functionalities that would cause unwanted information processing and/or communication problems that manifest as diseases.

Methods

On the other hand, general communication systems, e.g. digital communications, have been well developed and analysed to yield accuracy, high performance, and efficiency. Therefore, we extend the theories of digital communication systems to analyse biological communications. However, in order to accurately model biological communication as digital ones, an analysis of the analogies between both systems is essential. In this work, we propose a novel stacked-layer network model that presents gene expression (i.e. the process by which the information carried by deoxyribonucleic acid or DNA is transformed into the appropriate proteins) and the role of the Golgi apparatus in transmitting these proteins to a target organ. This is analogous to the transmit process in digital communications where a transmitting device in some network would send digital information to a destination/receiver device in another network through a router.

Results

The proposed stacked-layer network model exploits key networks’ theories and applies them into the broad field genomic analysis, which in turn can impact our understanding and use of medical methods. For example, it would be useful in detecting a target site (e.g. tumour cells) for drug therapy, improving the targeting accuracy (addressing), and reducing side effects in patients from health and socio-economic perspectives.

Conclusions

Besides improving our understanding of biological communication systems, the proposed model unleashes the true duality between digital and biological communication systems. Therefore, it could be deployed into leveraging the advantages and efficiencies of biological systems into digital communication systems as well and to further develop efficient models that would overcome the disadvantages of either system.

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References

  1. Bush SF. Nanoscale communication networks. Norwood: Artech House; 2010. p. 53, 57, 67, 69, 70, 194, 195, 234.

  2. Siddique N, Adeli H. Nature inspired computing: an overview and some future directions. Cogn Comput. 2015;7(6):706–14.

    Article  Google Scholar 

  3. Dressler F, Akan OB. A survey on bio-inspired networking. Comput Netw. 2010;54(6):881–900.

    Article  Google Scholar 

  4. Nakano T, Moore M. Molecular communication paradigm overview. J Next Gener Inf Technol. 2011;2(1):9–16.

    Article  Google Scholar 

  5. Pérez ST, Vásquez JL, Travieso CM, Alonso JB. Artificial neural network in FPGA for temperature prediction. In: International conference on nonlinear speech processing. New York: Springer; 2011. p. 104–10.

  6. Rushdi A, Tuqan J, Strohmer T. Map-invariant spectral analysis for the identification of DNA periodicities. EURASIP J Bioinform Syst Biol. 2012;1:1–21.

    Google Scholar 

  7. Chahibi Y, Pierobon M, Akyildiz IF. Pharmacokinetic modeling and biodistribution estimation through the molecular communication paradigm. IEEE Trans Biomed Eng. 2015;62(10):2410–20.

    Article  PubMed  Google Scholar 

  8. IEEE Transactions on NanoBioscience. IEEE. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7728.

  9. Felicetti L, Femminella M, Reali G, Liò P. Applications of molecular communications to medicine: a survey. Nano Commun Netw. 2016;7:27–45.

    Article  Google Scholar 

  10. ns-3 and nanoscale simulations. 2016. https://www.nsnam.org/news/ns-3-and-nanoscale-simulations/.

  11. Lee WP, Lin CH. Combining expression data and knowledge ontology for gene clustering and network reconstruction. Cogn Comput. 2016;8(2):217–27.

    Article  Google Scholar 

  12. Farhan F, Mushfiq M. Biological cell and molecular communication technology: overview and challenges. Eur Sci J (ESJ). 2015;11(6):56–60.

    Google Scholar 

  13. Shannon C. A mathematical theory of communication. Bell Syst Tech J. 1948;27:379–423.

    Article  Google Scholar 

  14. Burbeck S. Complexity and the evolution of computing: biological principles for managing evolving systems. Comput Syst. 2007;2004–7. http://www.evolutionofcomputing.org

  15. Nakano T, Suda T, Koujin T, Haraguchi T, Hiraoka Y. Molecular communication through gap junction channels: system design, experiments and modeling. In: Bio-inspired models of network, information and computing systems (Bionetics 2007). 2nd ed. IEEE; 2007. p. 139–46.

  16. Burbeck S, Jordan KE. An assessment of the role of computing in systems biology. IBM J Res Dev. 2006;50(6):529–43.

    Article  CAS  Google Scholar 

  17. Ganegoda GU, Li M, Wang W, Feng Q. Heterogeneous network model to infer human disease-long intergenic non-coding RNA associations. IEEE Trans Nanobiosci. 2015;14(2):175–83.

    Article  Google Scholar 

  18. Raz NR, Akbarzadeh-T MR, Tafaghodi M. Bioinspired nanonetworks for targeted cancer drug delivery. IEEE Trans Nanobiosci. 2015;14(8):894–906.

    Article  Google Scholar 

  19. Townsend J, Keedwell E, Galton A. Artificial development of biologically plausible neural-symbolic networks. Cogn Comput. 2014;6(1):18–34.

    Article  Google Scholar 

  20. Felicetti L, Femminella M, Reali G, Nakano T, Vasilakos AV. TCP-like molecular communications. IEEE J Sel Areas Commun. 2014;32(12):2354–67.

    Article  Google Scholar 

  21. Shrivastava S, Badlani R. Data storage in DNA. Int J Electr Energy. 2014;2(2):119–21.

    Article  Google Scholar 

  22. Chang WL, Vasilakos AV. DNA algorithms of implementing biomolecular databases on a biological computer. IEEE Trans Nanobiosci. 2015;14:104–11.

    Article  Google Scholar 

  23. Worthey EA. Transformation of big data into clinically actionable knowledge: supporting the personalized medicine revolution. In: 37th annual international conference of the IEEE Engineering in Medicine and Biology Society. 2015. http://embc.embs.org/2015/keynote-worthey/.

  24. Abdullah A, Hussain A, Khan IH. Introduction: dealing with big data-lessons from cognitive computing. Cogn Comput. 2015;7(6):635–6.

    Article  Google Scholar 

  25. Tanenbaum AS, Wetherall DJ. The TCP/IP reference model. New York: Pearson Education; 2012. p. 31, 43, 45, 46, 194, 216.

  26. Loscri V, Marchal C, Mitton N, Fortino G, Vasilakos AV. Security and privacy in molecular communication and networking: opportunities and challenges. IEEE Trans Nanobiosci. 2014;13(3):199.

    Article  Google Scholar 

  27. Choffnes ER, Relman DA, Pray L, et al. The science and applications of synthetic and systems biology: workshop summary. Washington, DC: National Academies Press; 2011.

    Google Scholar 

  28. Marchisio MA, Stelling J. Automatic design of digital synthetic gene circuits. PLoS Comput Biol. 2011;7(2):e1001083.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet. 2001;2(1):343–72.

    Article  CAS  PubMed  Google Scholar 

  30. Shaw IS. A study of analogies between processes in technical and biological systems. Johannesburg: University of Johannesburg; 2012.

    Google Scholar 

  31. Shirazi AZ, Mazinani SM, Eghbal SK. Protocol stack for nano networks. In: IEEE international symposium on computer, consumer and control (IS3C); 2012. p. 849–53.

  32. Nakano T, Moore MJ, Wei F, Vasilakos AV, Shuai J. Molecular communication and networking: opportunities and challenges. IEEE Trans Nanobiosci. 2012;11(2):135–48.

    Article  Google Scholar 

  33. Nakano T, Suda T, Okaie Y, Moore MJ, Vasilakos AV. Molecular communication among biological nanomachines: a layered architecture and research issues. IEEE Trans Nanobiosci. 2014;13(3):169–97.

    Article  Google Scholar 

  34. Singh RP, Sapre SD. Communication systems. New York: Tata McGraw-Hill Education; 2008.

    Google Scholar 

  35. Abu-Rgheff MA. Introduction to CDMA wireless communications. Cambridge: Academic Press; 2007. p. 51.

    Book  Google Scholar 

  36. Morris DJ. Communication for command and control systems: international series on systems and control, vol. 5. Amsterdam: Elsevier; 2014. p. 98–100.

    Google Scholar 

  37. Akyildiz I, Pierobon M, Balasubramaniam S, Koucheryavy Y. The internet of bio-nano things. IEEE Commun Mag. 2015;53(3):35.

    Article  Google Scholar 

  38. Gerstein MB, Bruce C, Rozowsky JS, Zheng D, Du J, Korbel JO, et al. What is a gene, post-ENCODE? History and updated definition. Genome Res. 2007;17(6):669–81.

    Article  CAS  PubMed  Google Scholar 

  39. Tipton HF, Krause M. Information security management handbook. 6th ed. v. 2. Boca Raton: CRC Press; 2008. https://books.google.it/books?id=EqpjYH_Z6MQC.

  40. Bettelheim F, Brown W, Campbell M, Farrell S, Torres O. Introduction to general, organic and biochemistry. Boston: Cengage Learning; 2013. p. 722.

    Google Scholar 

  41. Lieberman M, Marks AD. Marks’ basic medical biochemistry: a clinical approach. Philadelphia: Lippincott Williams & Wilkins; 2009. p. 206.

    Google Scholar 

  42. Alberts B, Johnson A, Lewis J, Morgan D, Raff M, Roberts K, et al. Molecular biology of the cell. 500 tips series. New York: Garland Science; 2014. p. 334–60. https://books.google.co.uk/books?id=1ZUDoQEACAAJ.

  43. Farsad N, Yilmaz HB, Eckford A, Chae CB, Guo W. A comprehensive survey of recent advancements in molecular communication. New York: Garland Science; 2014.

    Google Scholar 

  44. Alberts B, Bray D, Hopkin K, Johnson A, Lewis J, Raff M, et al. Essential cell biology. New York: Garland Science; 2013. p. 185–7, 383–407, 487–513.

  45. Goodman SR. Medical cell biology. Amsterdam: Elsevier; 2007. p. 175. https://books.google.es/books?id=tRbCHk9easQC.

  46. Starr C, McMillan B. Human biology. Belmont: Cengage Learning; 2013. p. 412–3.

    Google Scholar 

  47. Zhang C, Liu H, Zheng K, Hao Y, Tan Z. DNA G-quadruplex formation in response to remote downstream transcription activity: long-range sensing and signal transducing in DNA double helix. Nucleic Acids Res. 2014;136(4):1381–90.

    CAS  Google Scholar 

  48. Jorde LB, Carey JC, Bamshad MJ. Medical genetics. Amsterdam: Elsevier; 2015. p. 6–11. https://books.google.com.ec/books?id=FrRgCgAAQBAJ.

  49. Nussbaum RL, McInnes RR, Willard HF. Thompson & Thompson genetics in medicine. Amsterdam: Elsevier; 2015. p. 23. https://books.google.com.ec/books?id=4yV1CQAAQBAJ.

  50. Solomon E, Martin C, Martin D, Berg L. Biology. Stamford: Cengage Learning; 2014. p. 305–7. https://books.google.es/books?id=Z0rAAgAAQBAJ.

  51. Anttalainen T. Introduction to telecommunications network engineering. Artech House telecommunications library. Norwood: Artech House; 2003. p. 144–8. https://books.google.es/books?id=S0_Pvzr-TeEC.

  52. Laskar J, Chakraborty S, Pham AV, Tantzeris MM. Advanced integrated communication microsystems, vol. 174. Hoboken: Wiley; 2009. p. 10.

    Book  Google Scholar 

  53. Snape A, Papachristodoulou D, Elliott WH, Elliott DC. Biochemistry and molecular biology. Oxford: Oxford University Press; 2014. p. 377.

    Google Scholar 

  54. Snustad DP, Simmons MJ. Principles of genetics, binder ready version. Hoboken: Wiley; 2015. p. 267–74. https://books.google.com.ec/books?id=NBB0CgAAQBAJ.

  55. Reece JB, Meyers N, Urry LA, Cain ML, Wasserman SA, Minorsky PV, et al. Campbell biology Australian and New Zealand version. Melbourne: Pearson Higher Education AU; 2014. p. 340–4. https://books.google.es/books?id=5t6aBQAAQBAJ.

  56. FitzGerald J, Dennis A. Business data communications and networking. Hoboken: Wiley; 2009. p. 136. https://books.google.es/books?id=EKW1_wcuOpgC.

  57. Hillis DM. Principles of life. Sunderland: Palgrave Macmillan; 2011.

    Google Scholar 

  58. Holmes R, et al. Salters-Nuffield advanced biology: AS student book. Oxford: Heinemann; 2005.

    Google Scholar 

  59. Fitzgerald-Hayes M, Reichsman F. DNA and biotechnology. Cambridge: Academic Press; 2010.

    Google Scholar 

  60. Del Vecchio D, Murray RM. Biomolecular feedback systems. Princeton: Princeton University Press; 2014. p. 48.

    Book  Google Scholar 

  61. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. How cells read the genome: from DNA to protein. New York: Garland Science; 2014.

    Google Scholar 

  62. Lodish H, Berk A, Zipursky SL, Matsudaira P, Baltimore D, Darnell J. Nucleic acids, the genetic code, and the synthesis of macromolecules. NewYork: W.H. Freeman; 2000.

    Google Scholar 

  63. Ignatova Z, Martinez-Perez I, Zimmermann KH. DNA computing models. Berlin: Springer; 2008.

    Google Scholar 

  64. Drlica KS, Perlin DS. Antibiotic resistance: understanding and responding to an emerging crisis. New York: Pearson Education; 2011. p. 217. https://books.google.co.uk/books?id=YUppJBKFwPwC.

  65. Lewin B. Genes 9. v. 9. Burlington: Jones & Bartlett Learning; 2008. p. 110. https://books.google.co.uk/books?id=BrNpbPkzoxAC.

  66. Jackson ARW, Jackson JM. Environmental science: the natural environment and human impact. New York: Pearson Education; 2000. p. 139–40. https://books.google.co.uk/books?id=WLYCdqaHj1MC.

  67. Russell PJ. Biology: the dynamic science, vol. 1. Belmont: Cengage Learning; 2008. p. 323. https://books.google.co.uk/books?id=VYMFAAAAQBAJ.

  68. Plopper G, Plopper RPIG. Principles of cell biology. 2nd ed. Navigate advantage access. Burlington: Jones & Bartlett Learning, LLC; 2014. p. 277–85. https://books.google.es/books?id=z_ZGBQAAQBAJ.

  69. Stabler LB, Metz M, Wilkes A. Kaplan AP biology 2016. Kaplan test prep. New York: Kaplan Publishing; 2015. p. 124. https://books.google.es/books?id=DFgRCgAAQBAJ.

  70. Dubitzky W, Southgate J, Fuß H. Understanding the dynamics of biological systems: lessons learned from integrative systems biology. New York: Springer; 2011. p. 1. https://books.google.es/books?id=MNtTKe0S4ngC.

  71. Cracraft J, Donoghue MJ. Assembling the tree of life. Oxford: Oxford University Press; 2004. p. 60.

    Google Scholar 

  72. Bolsover SR, Shephard EA, White HA, Hyams JS. Cell biology: a short course. Hoboken: Wiley; 2011. p. 223–31. https://books.google.es/books?id=Kt_hL1stQQkC.

  73. Surgeons AAO, Elling B, Elling KM, Rothenberg MA. Paramedic: anatomy & physiology. Sudbury: Jones & Bartlett Learning; 2012. p. 22. https://books.google.es/books?id=-LhaI-g-OUgC.

  74. Beckerman M. Molecular and cellular signaling. New York: Springer; 2006. p. 6–9.

    Google Scholar 

  75. Seth G, Hossler P, Yee JC, Hu S. Engineering cells for cell culture bioprocessing—physiological fundamentals. In: Hu WS, editor. Cell culture engineering, vol. 101. New York: Springer; 2006. p. 119–64.

    Chapter  Google Scholar 

  76. Sircar S. Principles of medical physiology. New York: Thieme; 2008. p. 30. https://books.google.es/books?id=zFl7y5xqHj4C.

  77. Pusztai A, Bardocz S. Lectins: biomedical perspectives. London: Taylor & Francis; 2005. p. 168. https://books.google.es/books?id=qK40HAP6bGgC.

  78. Demchenko AP. Introduction to fluorescence sensing. New York: Springer; 2008.

    Google Scholar 

  79. Starck JM, Ricklefs RE. Avian growth and development: evolution within the altricial-precocial spectrum, vol. 8. Oxford: Oxford University Press; 1998. p. 174.

    Google Scholar 

  80. Lister AL, Van Der Kraak GJ. Endocrine disruption: why is it so complicated? Water Qual Res J Can. 2001;36(2):175–90.

    CAS  Google Scholar 

  81. Roper MG, Guillo C. New technologies in affinity assays to explore biological communication. Anal Bioanal Chem. 2009;393(2):459–65.

    Article  CAS  PubMed  Google Scholar 

  82. Nakano T, Moore M, Enomoto A, Suda T. Molecular communication technology as a biological ICT. In: Sawai H, editor. Biological functions for information and communication technologies, vol. 320. Berlin: Springer; 2011. p. 49–86.

    Chapter  Google Scholar 

  83. Du W, Cao Z, Wang Y, Zhou F, Pang W, Chen X, et al. Specific biomarkers: detection of cancer biomarkers through high-throughput transcriptomics data. Cogn Comput. 2015;7(6):652–66.

    Article  Google Scholar 

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Funding

This study has not explicit funding; it was made through a scholarship given to Ph.D. Student Yesenia Cevallos by National University of Chimborazo of Ecuador.

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Correspondence to Yesenia Cevallos.

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Yesenia Cevallos, Lorena Molina, Alex Santillán, Floriano De Rango, Ahmad Rushdi and Jesús B. Alonso declare that they have no conflict of interest.

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Glossary: Cell Biology

Glossary: Cell Biology

5′–3′:

The direction of nucleic acid synthesis such that the 5′ phosphate of ribose or deoxyribose is joined to the 3′ hydroxyl of the immediately preceding ribose or deoxyribose in a growing RNA or DNA chain. When a single strand is written, the 5′ end of the molecule is conventionally written on the left and the 3′ end is written on the right

Active transport:

Transport of a substance across a membrane that does not rely on the potential energy of a concentration gradient for the substance being transported and therefore requires an additional energy source (often ATP)

Amino acid:

A chemical building block of proteins. There are 20 standard amino acids. A protein consists of a specific sequence of amino acids

Aminoacyl (A) site:

A binding site on a ribosome that accepts the incoming aminoacyl-tRNA

Anticodon:

A “triplet” of three nucleotides in a transfer RNA (tRNA) that is complementary to a codon in a messenger RNA (mRNA). During protein synthesis, base pairing between a codon and anticodon aligns the incoming tRNA carrying the new amino acid with the tRNA carrying the growing peptide chain

ATP, adenosine triphosphate:

The major source of energy for biochemical reactions in all organisms

Calcium signalling:

Signal transduction mechanisms whereby calcium mobilization (from outside the cell or from intracellular storage pools) to the cytoplasm is triggered by external stimuli. Calcium signals are often seen to propagate as waves, oscillations, spikes, sparks, or puffs. The calcium acts as an intracellular messenger by activating calcium-responsive proteins

Capping:

Covalent modification of mRNA at the 5′ end where a modified guanidine is covalently attached in a 5′–3′ linkage

Cell:

Fundamental structural unit of all life. The cell consists primarily of an outer plasma membrane, which separates it from the environment; the genetic material (DNA), which encodes heritable information for the maintenance of life; and the cytoplasm, a heterogeneous assemblage of ions, molecules, and fluid

Cell membrane:

The outer membrane of a cell, which separates it from the environment. Also called a plasma membrane or plasma lemma

Chemotaxis:

The movement of a cell towards or away from the source of a chemical

Chromosome:

A cellular structure containing genes. Excluding sperm and egg cells, humans have 46 chromosomes (23 pairs) in each cell

Codon:

A trinucleotide (triplet, or three-word) that specifies an amino acid or stop when parsed by the translation apparatus. The codons in mRNA molecules base pair with the anticodons of the cognate tRNAs during protein synthesis

Cytoplasm:

All the contents of a cell, including the plasma membrane, but not including the nucleus

Cytoskeleton:

Integrated system of molecules within eukaryotic cells which provides them with shape, internal spatial organization, motility, and may assist in communication with other cells and the environment. Red blood cells, for instance, would be spherical instead of flat if it were not for their cytoskeleton

Cytosol:

The semi-fluid portion of the cytoplasm, excluding the organelles. The cytosol is a concentrated solution of proteins, salts, and other molecules

DNA, deoxyribonucleic acid:

The substance of heredity. A long, helical, double-stranded molecule that carries the cell’s genetic information

Dynein:

Member of a family of ATP− powered motor proteins that move towards the (−) end of microtubules by sequentially breaking and forming new bonds with microtubule proteins.

Embryonic stem cell:

A cell found in early embryos that can renew itself and differentiate into the many cell types that are found in the human body

Endocrine system:

A network of glands distributed throughout the body forms the endocrine system. These glands produce hormones that are released into the circulation and distributed to distant target sites via the blood. Hormones produced by these glands act as chemical messengers to control body functions such as growth, metabolism, sexual development, and egg and sperm production

Enhancer:

A regulatory sequence in eukaryotic DNA that may be located at a great distance from the gene it controls. Binding of specific proteins to an enhancer modulates the rate of transcription of the associated gene

Enzyme:

A protein that speeds up a specific chemical reaction without being permanently altered or consumed

ER, endoplasmic reticulum:

Network of membranes in eukaryotic cells which helps in control of protein synthesis and cellular organization

Eukaryote:

An organism whose cells have cytoskeletons for support and their DNA contained in a nucleus, separated from the other contents of the cell

Exit (E) site:

The tRNA-binding site on the ribosome that binds each uncharged tRNA just prior to its release

Fick’s first law:

An observed law stating that the rate at which one substance diffuses through another is directly proportional to the concentration gradient of the diffusing substance

Fick’s second law:

Is used in non-steady state diffusion, i.e. when the concentration within the diffusion volume changes with respect to time

GA, golgi apparatus:

Eukaryotic organelle which package cell products, such as enzymes and hormones, and coordinate their transport to the outside of the cell

Gap junctions:

Channel formed by proteins that allows ions and other molecules to pass between adjacent cells

Gene:

A unit of heredity; a segment of DNA that contains the code for making a specific protein or RNA molecule

Guanyl transferase:

Guanylyl transferase is a capping enzyme complex. Guanylyl transferase is used to label either 5’ di- and triphosphate ends of RNA molecules, or capped 5’ ends of RNA after chemical removal of the terminal 7-methyl-guanosine residue

Hormone:

A molecule that stimulates specific cellular activity; made in one part of the body and transported via the bloodstream to tissues and organs. Examples include insulin, oestrogen, and testosterone

Hydrolysis:

Reaction in which a covalent bond is cleaved with addition of an H from water to one product of the cleavage and of an OH from water to the other

Intron:

Part of a primary transcript (or the DNA from which it is transcribed). The intron is removed during RNA processing (splicing) and is not found in the mature, functional RNA

Kinesin:

Member of a family of motor proteins that use energy released by ATP hydrolysis to move towards the (+) end of a microtubule, transporting vesicles or particles in the process

Law mass action:

The law stating that the rate of any given chemical reaction is proportional to the product of the activities (or concentrations) of the reactants

Ligand:

A substance that is able to bind to and form a complex with a biomolecule to serve a biological purpose

Methylation:

The addition of a methyl group (–CH3) to a molecule, most commonly in the context of DNA where cytosine and, less often, adenine residues can be modified in this way, sometimes resulting in a change in transcription

Microtubule:

Part of the cytoskeleton; a strong, hollow fibre that acts as a structural support for the cell. Microtubules also serve as tracks for transporting vesicles and give structure to flagella and cilia

Motor protein:

Any member of a special class of enzymes that use energy from ATP hydrolysis to walk or slide along a microfilament (myosin) or a microtubule (dynein and kinesin)

Myosin:

The most common protein in muscle cells, responsible for the elastic and contractile properties of muscle

Nucleotide:

A monomer unit of nucleic acid, consisting of a purine or pyrimidine base, a sugar molecule (ribose or deoxyribose), and phosphate group (s)

Nucleus:

Membrane-bound organelle which contains the DNA in the form of chromosomes. It is the site of DNA replication, and the site of RNA synthesis

Organelle:

A specialized, membrane-bounded structure that has a specific function in a cell. Examples include the nucleus, Golgi, ER

Passive transport:

A kind of transport by which ions or molecules move along a concentration gradient, which means movement from an area of higher concentration to an area of lower concentration

Pheromones:

A chemical secreted by an animal, especially an insect, that influences the behaviour or physiology of others of the same species, as by attracting members of the opposite sex or marking the route to a food source

Peptide:

A natural or synthetic compound containing two or more amino acids linked together by peptide bonds

Peptidyl (P) site:

The binding site on a ribosome that contains the tRNA attached to the growing polypeptide chain

Polarity:

DNA synthesis uses 5′ nucleotide triphosphates as precursor and proceeds by linking the 5′ triphosphate of the incoming nucleotide to the 3′OH of the growing chain. Hence DNA strands have polarity i.e. a 5′ and a 3′ end

Polypeptide:

A linear polymer of amino acids held together by peptide linkages

Poly (A) polymerase:

An enzyme that adds consecutive adenosines to the 3′ termini of eukaryotic mRNAs to generate poly (A) tails

Promoter:

A piece of genetic material that acts as a gene switch, so that a gene can become expressed in the cell. It is the region at which the RNA polymerase binds to start transcription. Most promoters are located upstream of the gene, except that some eukaryotic genes have promoters internal to the gene

Protein:

A molecule composed of amino acids lined up in a precise order determined by a gene, then folded into a specific three-dimensional shape. Proteins are responsible for countless biological functions and come in a wide range of shapes and sizes

Random walk:

It is no directional drift of information molecules and no chemical reaction of information molecules during propagation. Random walk is the most fundamental mechanism that molecular communication relies on to propagate a molecule. Random walk does not require any additional mechanism to propagate a molecule

Random walk with chemical reactions by amplifiers:

Amplifiers in the environment can increase the reliability of molecular propagation by increasing the number of propagating information molecules. Amplifiers are located in the environment and react with molecules that propagate in the environment. As a result, amplifiers produce a copy of the molecule which propagates in the environment. This class of molecular communication may be enabled by exploiting protein molecules such as those responsible for amplifying calcium ions

Random walk with drift:

Information molecules may undergo a directional drift which continuously propagates molecules in the direction of the drift. An example of this class of molecular communication is found in our body. Cells in the body secrete hormonal substances which circulate with the flow of the blood stream and propagate to distant target cells distributed throughout the body

Ribosome:

A molecular complex in which proteins are made. In eukaryotic cells, ribosomes either are free in the cytoplasm or are attached to the rough endoplasmic reticulum

RRNA, ribonucleic acid:

A molecule very similar to DNA that plays a key role in making proteins. There are three main types: messenger RNA (mRNA) is an RNA version of a gene and serves as a template for making a protein, ribosomal RNA (rRNA) is a major component of ribosomes, and transfer RNA (tRNA) transports amino acids to the ribosome and helps position them properly during protein production

RNA polymerase:

An enzyme that makes RNA using DNA as a template in a process called transcription

RNA splicing:

A process that results in the precise cutting of RNA, removal of introns to produce a fully functional RNA

SRP, signal recognition particle:

Protein–RNA complex that binds to signal sequences and targets polypeptide chains to the endoplasmic reticulum

Start codon:

The mRNA triplet (AUG) that is recognized by the ribosome as a signal for the start of translation

Stop codons:

One of three codons in mRNA (UAG, UGA, UAA) that function as signals for the termination of translation by ribosomes

Transcription:

The process of copying information from genes (made of DNA) into messenger RNA

Translation:

The process of making proteins based on genetic information encoded in messenger RNA. Translation occurs in ribosomes

Vesicle:

A small, membrane-bounded sac that transports substances between organelles as well as to and from the cell membrane

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Cevallos, Y., Molina, L., Santillán, A. et al. A Digital Communication Analysis of Gene Expression of Proteins in Biological Systems: A Layered Network Model View. Cogn Comput 9, 43–67 (2017). https://doi.org/10.1007/s12559-016-9434-4

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