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Scalability in Gene Regulation: A Statistical Mechanics Approach

This paper by Alessandro Giuliani explores the concept of scalability in biological systems, particularly in gene regulation and expression. It discusses how scalable systems display behaviors that emerge from ensemble interactions rather than individual components. The research emphasizes the relationship between local and general properties, the significance of network topology in metabolism, and the dynamics of the cytokine response. Moreover, it highlights how population-level phenomena provide insights that extend beyond single-cell analyses, proposing a new perspective on pharmacological interventions.

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Scalability in Gene Regulation: A Statistical Mechanics Approach

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  1. Scalability : a statistical mechanics perspective on gene regulation and expression Alessandro Giuliani

  2. Scalability: a refresher A scalable system displays behaviours dependent on the presence of an ensemble and not directly derivable from microscopic properties of the single elements. A portion sufficiently large has the same properties of the whole system.

  3. An highly connected system displays scalable properties if has the possibility of establishinglong range correlations

  4. Local properties have a general originGeneral properties come from local correlations

  5. At odds with a road map, in which the block of a road with an high capacity cannot be overcame by the deviation of traffic on a narrow road, metabolism seems to be only dependent of topology: no lethal purely kynetic mutants.

  6. http://www.blackwell-synergy.com/toc/ejb/275/10

  7. The citokyne response is the ‘local’ portion of the network response, that can be elicited only when Toll-like receptor system is active. The genome-wide scalable response is the ‘whole netowrk resonant’ response that is elicited even when Toll-like receptor system is out of work

  8. The correlation computed on the entire profile of micro-RNA between progenitor (CD34) and different lineages decays monotonously in time. This behavior is exactly the same with a much smaller random set of probes: scalability !

  9. step1 step2 step3 0,0552 0,0928 0,1619 e-mk 0,0678 0,1145 0,1537 e-g 0,0747 0,1269 0,1750 e-mo 0,0260 0,0653 0,1192 g-mk 0,0159 0,0496 0,0546 g-mo 0,0289 0,0808 0,1413 mo-mk

  10. Rugged Landscapes

  11. Conclusions: • Biological systems, at every observation scale, display a wide spectrum • of behaviours from the extreme specificity (local actions) to scalability (general • effects). • Network paradigm allows to rationalize both specificity and scalability in terms of • attacks to crucial nodes and fault tolerance to random errors. • 3. The single cell is not necessarily the place of ‘definitive explanations’ in biology. • Many macroscopic very reliable phenomena ask for a explanation at the • population level: ecology in a plate. • 4. The consideration of cell lines phenotypes as attractors of an high dimensional • systems asks for a different approach in pharmacological intervention with respect • to usual ‘target identification’ pharmacology.

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