Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development.

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The iris codes for the left and right iris of a person have previously been reported to be uncorrelated. We replicate this result using images from 327 persons from the iris image dataset used in the Iris Challenge Evaluations. The same images are then used in an experiment in which subjects view a left and a right iris image, and judge whether they are correctly paired by similarity of iris texture. Subjects are able to distinguish between iris images belonging to the same person versus belonging to different persons with over 86% accuracy overall, and over 93% accuracy when they judge their decision as confident. Thus, there clearly is a similarity in the texture pattern of the left and right iris of the same person that can reliably be detected by novice observers with a brief viewing time. This is the first research that we are aware of that investigates the abilities of human observers in judging similarity or dissimilarity of iris texture.

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