LabStats

Biological and neural systems operate under substantial intrinsic stochasticity. Despite this, they maintain stable distributions of molecular, cellular, and population-level states. Occasionally, however, these systems exhibit rare, high-impact deviations. Sanov’s theorem provides a unifying framework for quantifying the improbability of such events through the geometry of empirical distributions. 1. Introduction Rare events in living systems often correspond not to isolated fluctuations but to atypical empirical distributions emerging from many interacting components. Sanov’s theorem characterizes the exponential decay of the probability of such deviations using the Kullback–Leibler divergence. This post surveys applications across molecular biology, cell dynamics, and neuroscience. Notation The following symbols and variables are used throughout this post: \(X_1, \dots, X_n\) : observations or molecular states sampled from a biological system. \(P\) : bas...

  A follow‑up to “Cell Count Analysis with cycleTrendR” — focused on neurodegenerative drift In our previous post , we explored how cycleTrendR can be applied to cell‑count trajectories in longitudinal culture experiments. That example sparked an insightful question on LinkedIn : How does cycleTrendR perform when the signal is slow, noisy, and stretched across long timelines — as in neurodegenerative disease models? This post is a direct response. Neurodegenerative phenotypes often evolve gradually, with subtle changes accumulating over weeks or months. Meanwhile, experimental noise — imaging variability, segmentation artefacts, media‑change cycles — fluctuates rapidly and can dominate the signal. cycleTrendR was designed precisely for this challenge: to extract slow biological drift from noisy, irregular, and cyclic time series. How cycleTrendR isolates slow biological drift from fast experimental noise Longitudinal cell‑culture experiments — especially those modelling neurodege...

Complexity is an ubiquitous condition. We all make decisions of varying importance every day, so one may find strange that decision making involves rather sophisticated mechanisms. However, studies have shown that most people are much poorer at decision making than they think. The understanding of the decision making processes has been the aim of scientific research for many years.  Decision making rules act not only at a macro-level, whose results we can observe  as economic aggregates, but take a major role also at the micro-level in terms of interactions among neuronal populations.  In both cases the comprehension of the phenomena relies on robust computational approach. In this sense, statistics offers the tools for modelling and understanding  economic (i.e., social) behaviors and neuronal activities.