• The Central Dogma of molecular biology

  • Environmental growth conditions

  • Cell metabolism
  • Cell cycles



    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 understanding of biological system (legome) can be derived from insight into 4 key properties :

    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 : In engineering systems, robustness is attained by using : Comprehensiveness in measurements requires consideration of 3 aspects : 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
  • Web resources
  • Bibliography :

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