Duplication of the genome in mammalian cells occurs in a defined temporal order referred to as its replication-timing (RT) program. RT changes dynamically during development, regulated in units of 400-800 kb referred to as replication domains (RDs). Changes in RT are generally coordinated with transcriptional competence and changes in subnuclear position. We generated genome-wide RT profiles for 26 distinct human cell types, including embryonic stem cell (hESC)-derived, primary cells and established cell lines representing intermediate stages of endoderm, mesoderm, ectoderm, and neural crest (NC) development. We identified clusters of RDs that replicate at unique times in each stage (RT signatures) and confirmed global consolidation of the genome into larger synchronously replicating segments during differentiation. Surprisingly, transcriptome data revealed that the well-accepted correlation between early replication and transcriptional activity was restricted to RT-constitutive genes, whereas two-thirds of the genes that switched RT during differentiation were strongly expressed when late replicating in one or more cell types. Closer inspection revealed that transcription of this class of genes was frequently restricted to the lineage in which the RT switch occurred, but was induced prior to a late-to-early RT switch and/or down-regulated after an early-to-late RT switch. Analysis of transcriptional regulatory networks showed that this class of genes contains strong regulators of genes that were only expressed when early replicating. These results provide intriguing new insight into the complex relationship between transcription and RT regulation during human development.

DNA replication occurs in a defined temporal order known as the replication timing (RT) program and is regulated during development, coordinated with 3D genome organization and transcriptional activity. However, transcription and RT are not sufficiently coordinated to predict each other, suggesting an indirect relationship. Here, we exploit genome-wide RT profiles from 15 human cell types and intermediate differentiation stages derived from human embryonic stem cells to construct different types of RT regulatory networks. First, we constructed networks based on the coordinated RT changes during cell fate commitment to create highly complex RT networks composed of thousands of interactions that form specific functional subnetwork communities. We also constructed directional regulatory networks based on the order of RT changes within cell lineages, and identified master regulators of differentiation pathways. Finally, we explored relationships between RT networks and transcriptional regulatory networks (TRNs) by combining them into more complex circuitries of composite and bipartite networks. Results identified novel trans interactions linking transcription factors that are core to the regulatory circuitry of each cell type to RT changes occurring in those cell types. These core transcription factors were found to bind cooperatively to sites in the affected replication domains, providing provocative evidence that they constitute biologically significant directional interactions. Our findings suggest a regulatory link between the establishment of cell-type-specific TRNs and RT control during lineage specification.

BackgroundStudying biological networks is of extreme importance in understanding cellular functions. These networks model interactions between molecules in each cell. A large volume of research has been done to uncover different characteristics of biological networks, such as large-scale organization, node centrality and network robustness. Nevertheless, the vast majority of research done in this area assume that biological networks have deterministic topologies. Biological interactions are however probabilistic events that may or may not appear at different cells or even in the same cell at different times.ResultsIn this paper, we present novel methods for characterizing probabilistic signaling networks. Our methods do this by computing the probability that a signal propagates successfully from receptor to reporter genes through interactions in the network. We characterize such networks with respect to (i) centrality of individual nodes, (ii) stability of the entire network, and (iii) important functions served by the network. We use these methods to characterize major H. sapiens signaling networks including Wnt, ErbB and MAPK.

Discovering signaling pathways in protein interaction networks is a key ingredient in understanding how proteins carry out cellular functions. These interactions however can be uncertain events that may or may not take place depending on many factors including the internal factors, such as the size and abundance of the proteins, or the external factors, such as mutations, disorders and drug intake. In this paper, we consider the problem of finding causal orderings of nodes in such protein interaction networks to discover signaling pathways. We adopt color coding technique to address this problem. Color coding method may fail with some probability. By allowing it to run for sufficient time, however, its confidence in the optimality of the result can converge close to 100%. Our key contribution in this paper is elimination of the key conservative assumptions made by the traditional color coding methods while computing its success probability. We do this by carefully establishing the relationship between node colors, network topology and success probability. As a result our method converges to any confidence value much faster than the traditional methods. Thus, it is scalable to larger protein interaction networks and longer signaling pathways than existing methods. We demonstrate, both theoretically and experimentally that our method outperforms existing methods.Keywords: protein interaction networks; signaling pathways; color coding; chromatic polynomial IntroductionStudying interactions between proteins has been of utmost importance in understanding how proteins work collectively to govern cellular functions. 1,2 Such collection of interactions among proteins is called a protein interaction network. The interactions are uncertain events. They may or may not take place depending on the internal factors, such as the size and abundance of the proteins, or the external factors, such as mutations, disorders and drug intake. Mathematically, a protein interaction network is often modeled as an edge-weighted undirected graph where each node denotes a protein and each edge represents an interaction between a pair of proteins. The weight of an edge denotes the level of confidence that this interaction truly exists.Computational analysis of protein interaction networks has been essential in identification of signaling pathways. A signaling pathway is a series of proteins in which each protein participates in transmitting biological information by modifying its successor through an interaction. Thus, signaling pathways can be viewed as simple paths in protein interaction networks. 3 One outcome of the uncertainty of the interactions is that the pathway that transmits signals between two specific sets of proteins (e.g., from membrane receptors to transcription factors) may differ as the set of interactions change. Finding possible pathways in the presence of such uncertainty has great potential in numerous applications including identification of drug targets, studying complex diseases, drug-drug interaction and metabolic en...

Motivation: Major disorders, such as leukemia, have been shown to alter the transcription of genes. Understanding how gene regulation is affected by such aberrations is of utmost importance. One promising strategy toward this objective is to compute whether signals can reach to the transcription factors through the transcription regulatory network (TRN). Due to the uncertainty of the regulatory interactions, this is a #P-complete problem and thus solving it for very large TRNs remains to be a challenge.Results: We develop a novel and scalable method to compute the probability that a signal originating at any given set of source genes can arrive at any given set of target genes (i.e., transcription factors) when the topology of the underlying signaling network is uncertain. Our method tackles this problem for large networks while providing a provably accurate result. Our method follows a divide-and-conquer strategy. We break down the given network into a sequence of non-overlapping subnetworks such that reachability can be computed autonomously and sequentially on each subnetwork. We represent each interaction using a small polynomial. The product of these polynomials express different scenarios when a signal can or cannot reach to target genes from the source genes. We introduce polynomial collapsing operators for each subnetwork. These operators reduce the size of the resulting polynomial and thus the computational complexity dramatically. We show that our method scales to entire human regulatory networks in only seconds, while the existing methods fail beyond a few tens of genes and interactions. We demonstrate that our method can successfully characterize key reachability characteristics of the entire transcriptions regulatory networks of patients affected by eight different subtypes of leukemia, as well as those from healthy control samples.Availability: All the datasets and code used in this article are available at bioinformatics.cise.ufl.edu/PReach/scalable.htm.Contact: atodor@cise.ufl.eduSupplementary information: Supplementary data are available at Bioinformatics online.