Stochasticity pervades life at the cellular level. Cells receive stochastic signals, perform detection and transduction with stochastic biochemistry, and grow and die in stochastic environments. Here we review progress in going from the molecular details to the information-processing strategies cells use in their decision-making. Such strategies are fundamentally influenced by stochasticity. We argue that the cellular decision-making can only be probabilistic and occurs at three levels. First, cells must infer from noisy signals the probable current and anticipated future state of their environment. Second, they must weigh the costs and benefits of each potential response, given that future. Third, cells must decide in the presence of other, potentially competitive, decision-makers. In this context, we discuss cooperative responses where some individuals can appear to sacrifice for the common good. We believe that decisionmaking strategies will be conserved, with comparatively few strategies being implemented by different biochemical mechanisms in many organisms. Determining the strategy of a decision-making network provides a potentially powerful coarse-graining that links systems and evolutionary biology to understand biological design.

A fundamental problem in functional genomics is to determine the structure and dynamics of genetic networks based on expression data. We describe a new strategy for solving this problem and apply it to recently published data on early Drosophila melanogaster development. Our method is orders of magnitude faster than current fitting methods and allows us to fit different types of rules for expressing regulatory relationships. Specifically, we use our approach to fit models using a smooth nonlinear formalism for modeling gene regulation (gene circuits) as well as models using logical rules based on activation and repression thresholds for transcription factors. Our technique also allows us to infer regulatory relationships de novo or to test network structures suggested by the literature. We fit a series of models to test several outstanding questions about gap gene regulation, including regulation of and by hunchback and the role of autoactivation. Based on our modeling results and validation against the experimental literature, we propose a revised network structure for the gap gene system. Interestingly, some relationships in standard textbook models of gap gene regulation appear to be unnecessary for or even inconsistent with the details of gap gene expression during wild-type development.

Summary Background Neurons require highly specialized intracellular membrane trafficking, especially at synapses. Rab GTPases are considered master regulators of membrane trafficking in all cells and only very few Rabs have known neuron-specific functions. Here, we present the first systematic characterization of neuronal expression, subcellular localization and function of Rab GTPases in an organism with a brain. Results We report the surprising discovery that half of all Drosophila Rabs function specifically or predominantly in distinct subsets of neurons in the brain. Furthermore, functional profiling of the GTP/GDP-bound states reveals that these neuronal Rabs are almost exclusively active at synapses and the majority of these synaptic Rabs specifically mark synaptic recycling endosomal compartments. Our profiling strategy is based on Gal4 knock-ins in large genomic fragments that are additionally designed to generated mutants by ends-out homologous recombination. We generated 36 large genomic targeting vectors and transgenic rab-Gal4 fly strains for 25 rab genes. Proof-of-principle knock-out of the synaptic rab27 reveals a sleep phenotype that matches its cell-specific expression. Conclusions Our findings suggest that up to half of all Drosophila Rabs exert specialized synaptic functions. The tools presented here allow systematic functional studies of these Rabs and provide a method that is applicable to any large gene family in Drosophila.

SUMMARY Pax3 and Pax7 regulate stem cell function in skeletal myogenesis. However, molecular insight into their distinct roles has remained elusive. Using gene expression data combined with genome wide binding-site analysis we show that both Pax3 and Pax7 bind identical DNA motifs and jointly activate a large panel of genes involved in muscle stem cell function. Surprisingly, in adult myoblasts Pax3 binds a subset (6.4%) of Pax7 targets. Despite a significant overlap in their transcriptional network, Pax7 regulates distinct panels of genes involved in the promotion of proliferation and inhibition of myogenic differentiation. We show that Pax7 has a higher binding affinity to the homeodomain-binding motif relative to Pax3, suggesting that intrinsic differences in DNA binding contribute to the observed functional difference between Pax3 and Pax7 binding in myogenesis. Together, our data demonstrates distinct attributes of Pax7 function and provides mechanistic insight into the non-redundancy of Pax3 and Pax7 in muscle development.