Surprisal analysis
Recently, it has been extended to characterize the state of living cells, specifically monitoring and characterizing biological processes in real time using transcriptional data.
Surprisal analysis was formulated at the Hebrew University of Jerusalem as a joint effort between Raphael David Levine, Richard Barry Bernstein and Avinoam Ben-Shaul in 1972.
[1] Alhassid and Levine first applied surprisal analysis in nuclear physics, to characterize the distribution of products in heavy ion reactions.
[2]* Maximum entropy methods are at the core of a new view of scientific inference, allowing analysis and interpretation of large and sometimes noisy data.
This enables surprisal analysis to be an effective method of information quantification and compaction and of providing an unbiased characterization of systems.
Foremost, surprisal analysis identifies the state of a system when it reaches its maximal entropy, or thermodynamic equilibrium.
Surprisal analysis is applied to both identify and characterize these constraints.
A numerical algorithm for determining Lagrange multipliers has been introduced by Agmon et al.[4] Recently, singular value decomposition and principal component analysis of the surprisal was utilized to identify constraints on biological systems, extending surprisal analysis to better understanding biological dynamics as shown in the figure.
Surprisal (a term coined[5] in this context by Myron Tribus[6]) was first introduced to better understand the specificity of energy release and selectivity of energy requirements of elementary chemical reactions.
[1] This gave rise to a series of new experiments which demonstrated that in elementary reactions, the nascent products could be probed and that the energy is preferentially released and not statistically distributed.
[1] Surprisal analysis was initially applied to characterize a small three molecule system that did not seemingly conform to principles of thermodynamics and a single dominant constraint was identified that was sufficient to describe the dynamic behavior of the three molecule system.
Similar results were then observed in nuclear reactions, where differential states with varying energy partitioning are possible.
Often chemical reactions require energy to overcome an activation barrier.
[8] Surprisal analysis was extended to better characterize and understand cellular processes,[9] see figure, biological phenomena and human disease with reference to personalized diagnostics.
[10] Similarly, it has been used to discern two distinct phenotypes during the EMT of cancer cells.
