General Cell Biology

 

Biology is chemistry coupled with natural selection : a biological solution is not just any chemically possible way, but a chemically feasible procedure whose material was available, whose fitness value can be demonstrated, and whose existence does not disturb any other critical process that previously worked. We are just beginning to write the “user’s guide” that must accompany the huge “parts catalog” emerging from efforts in genomics and proteomics. While such a list provides a catalog of the individual components, by itself it is not sufficient to understand the complexity underlying the engineered object. A system-level understaing of biological system (legome) can be derived from insight into 4 key properties :

  • system structures : these include the netwrok of gene interactions and biochemical pathways, as well as the mechanism by which such interactions modulate the physical properties of intracellular and multicellular structures
  • system dynamics : how a system behaves over time under various conditions can be understood through metabolic analysis, sensitivity analysis, dynamic analysis methods such as phase portrait and bifurcation analysis traces time-varying change(s) in the state of the system in a multidimensional space where each dimension represents a particular concentration of the biochemical factor invovled
  • control method : mechanisms that systematically control the state of the cell can be modulated to minimize malfunctions and provide potential therapeutic targets for treatment of disease.
  • design method : strategies to modify and construct biological systems having desired properties can be devised based on definite design principles and simulations, instead of blind trial-and-error

The hope is that intensive investigation will reveal  a possible evolutionary family of circuits as well as a “periodic table” for functional regulatory circuits.
Robustness is an essential property of biological systems, exhibiting phenomena that can be classidied into 3 areas :

  • adaptation, which denotes the ability to cope with environmental changes
  • parameter insensitivity, which indicates a system’s relative insensitivity to specific kinetic parameters
  • graceful degradation, which reflects the characteristic slow degradation of a system’s functions after damage, rather than catastrophic failure

In engineering systems, robustness is attained by using :

  • a form of system control such as negative-feedback and feed-forward control
  • redundancy, whereby multiple components with equivalent functions are introduced for backup
  • structural stability, where intrinsic mechanisms are built to promote stability
  • modularity, where subsystems are physically and functionally insulated so that failure in one module does not spread to other parts and lead to system-wide catastrophe

Comprehensiveness in measurements requires consideration of 3 aspects :

  • factor comprehensiveness, which reflects the number of mRNA transcripts and proteins that can be measured at once
  • time-line comprehensiveness, which represents the time frame within which measurements are made
  • item comprehensiveness, which refers to multiple items, such as mRNA and protein concentrations, phosphorylation, localization, and so forth

Currently, there is a community quite at home in dealing with huge complexity: modern day microchip designers. Given the statistics on modern chip design, one wonders if, in fact, cellular complexity has been surpassed. For example, with the recent move to 90-nm fabrication technology, the average transistor is now less that 50 nm in diameter – only 5 times bigger than the average intracellular protein. Not only are the parts getting smaller, the number of parts fabricated onto a single die is quite astounding. For example, the AMD Athlon 64 has about 106 million transistors. Given that a single kinase/phosphatase cycle has a dynamic response similar to a transistor, with approximately 518 kinases known to be expressed in humans, we are left with the embarrassing notion that a human cell’s computational capacity is significantly less than even the very first microprocessor – the Intel 4004, which had just over 2,000 transistors. This comparison is perhaps unfair, since it assumes that cellular signaling pathways “compute” digitally like human-made microprocessors. Signaling pathways more likely operate like an analog computer. Most external signals are themselves analog, and protein kinetics are eminently suitable for analog computationref1, ref2. Assuming that a single kinase/phosphatase unit behaves as a modest analog element such as an operational amplifier, it puts human protein networks somewhere around an Intel 8086 microprocessor in terms of complexity. That’s still not particularly high. Even if we take into account the added complexity of gene networks, gene splicing, and the great variety of covalent states, we might still only be able to increase the complexity a little more than 10-fold – comparable to, say, a 486 processor. Ok, perhaps these numbers are meaningless, but it makes one think for a moment that cells may not be as functionally complicated as it seems, given the relatively small number of components. 2 essentially equivalent theories, metabolic control analysis (MCA) and the biochemical systems theory (BST) are both excellent starting points (DA Fell Understanding the control of metabolism London: Portland Pressa 1996). A deep connection exists between classical control theory and MCA/BSTref1, ref2.

  • Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cellsref.
  • Some definitions
    • geobiology : the biology of terrestrial life.
    • living organism : every matter aggregate which has the capability to reproduce itself.
    • protoplasm : an old term for describing the living matter
      • mitome : a thready network of the protoplasm of a cell; the more solid portion of cell protoplasm
    • cell theory : the doctrine that all living matter is composed of cells and that cell activity is the essential process of life.
    • cell : the smallest protoplasm unit capable of independent life
      • biological membranes : Danielli-Davson model (1935) => Singer-Nicholson “fluid mosaic” model (1972) : the generally accepted theory that cell membranes are composed of bilayers made up of external phospholipids and a central hydrophobic region, with membrane proteins floating in the phospholipids and held in position by various chemical and physical bonding mechanisms
        • membrane proteins
          • type I : peripheral membrane proteins (electrostatic binding)
          • type II : monotopic
          • type III : bitopic (single-passage) membrane proteins
            • type I transmembrane proteins : NTD on the lumenal side and CTD on the cytosolic side
            • type II transmembrane proteins : CTD on the lumenal side and NTD on the cytosolic side
          • type IV : polytopic (loops are numbered beginning from N-terminal)
          • type V : covalently anchored to GPI (extracellular layer) or prenyl groups (cytosolic layer)
        • Nascent polypeptides that translocate into the ER lumen are often modified at Asn residues by the attachment of the sugar moiety

Glc3Man9GlcNAc2

          . The terminal 3 Glc residues are then sequentially removed by the action of

glucosidase I

          and

glucosidase II

          , creating Man

9

          GlcNAc

2

        species.
    • cytoplasm :
      • ectoplasm : the outer part of cytoplasm
      • endoplasm : the inner part of cytoplasm
      • hyaloplasm : an old term for describing the indifferentiate aspect of cytoplasm under optical microscopy without staining
      • paraplasm : an old term for describing the energy storages of a cell = glycogen, starch, triglycerids and proteins
      • cytosol : the physical sol contained in cytoplasm
  • Web resources
  • Alberts, Bruce; Johnson; Lewis, Julian; Raff, Martin; Roberts, Keith; Walter : Molecular Biology of the Cell, 4th edition, New York and London: Garland Publishing; © 2002 [free at NCBI BookShelf !]
  • Karp, Gerald : Cellular and Molecular Biology
  • Lodish, Harvey; Berk, Arnold; Zipursky, S. Lawrence; Matsudaira, Paul; Baltimore, David; Darnell, James E.: Molecular Cell Biology, 4th ed., New York: W H Freeman & Co; © 1999 [free at NCBI BookShelf !]
  • Cooper, Geoffrey M. : The Cell, a molecular approach, 2nd edition, Sunderland, Massachusetts: Sinauer Associates, Inc.; © 2000. ISBN 0 87893 106 6 [free at NCBI BookShelf !]
  • Eurekah Bioscience Collection : chapters taken from the Eurekah Bioscience database
  • Systems biology in Science Vol.295, 1 March 2002