Gene networks inference using dynamic Bayesian networks

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.     

Phylogenomic networks

Phylogenomics is aimed at studying functional and evolutionary aspects of genome biology using phylogenetic analysis of whole genomes. Current approaches to genome phylogenies are commonly founded in terms of phylogenetic trees. However, several evolutionary processes are non tree-like in nature, including recombination and lateral gene transfer (LGT). Phylogenomic networks are a special type of phylogenetic network reconstructed from fully sequenced genomes. The network model, comprising genomes connected by pairwise evolutionary relations, enables the reconstruction of both vertical and LGT events. Modeling genome evolution in the form of a network enables the use of an extensive toolbox developed for network research. The structural properties of phylogenomic networks open up fundamentally new insights into genome evolution.     

Antiidiotypic networks

Jerne envisions the immune system as a web of V domains that constitutes an idiotypic network. He thinks that regulatory processes governed by idiotypic interactions can explain the generation of the various immune states. We discuss a few models that furnish information about the possible configuration of this immune network: closed or open ended. It appears that closed configurations only can generate stable immune states. Moreover, we cite some experimental data in favor of the network hypothesis. We show how they can lead to propose the structure of functional regulatory circuits whose cellular and molecular interactions are mediated by idiotypic recognition processes.     

Biological networks

Recent advances in high-throughput methods have provided us with a first glimpse of the overall structure of molecular interaction networks in biological systems. Ultimately, we expect that such information will change how we think about biological systems in a fundamental way. Instead of viewing the genetic parts list of an organism as a loose collection of biochemical activities, in the best case, we anticipate discrete networks of function to bridge the gap between genotype and phenotype, and to do so in a more profound way than the current qualitative classification of linked reactions into familiar pathways, such as glycolysis and the MAPK signal transduction cascades. At the present time, however, we are still far from a complete answer to the most basic question: what can we learn about biology by studying networks? Promising steps in this direction have come from such diverse approaches as mathematical analysis of global network structure, partitioning networks into functionally related modules and motifs, and even de novo design of networks. A complete picture will probably require integrating the data obtained from all of these approaches with modeling efforts at many different levels of detail.     

Kinematic networks

Motor control in primates relates to a system which is highly redundant from the mechanical point of view — redundancy coming from an imbalance between the set of independently controllable variables and the set of system variables. The consequence is the manifestation of a broad class of ill-posed problems, problems for which it is difficult to identify unique solutions. For example (i) the problem of determining the coordinated patterns of rotation of the arm joints for a planned trajectory of the hand; (ii) the problem of determining the distribution of muscle forces for a desired set of joint torques. Ill-posed problems, in general, require regularization methods which allow to spell acceptable, if not unique, solutions. In the case of the motor system, we propose that the basic regularization mechanism is provided by the potential fields generated by the elastic properties of muscles, according to an organizational principle that we call Passive Motion Paradigm. The physiological basis of this hypothesis is reviewed and a Kinematic Network (K-net) model is proposed that expresses the kinematic transformations and the causal relations implied by elasticity. Moreover, it is shown how K-nets can be obtained from a kinematic Body Model, in the context of a specific task. Two particularly significant results are: (i) the uniform treatment of closed as well as open kinematic chains, and (ii) the development of a new method for the automatic generation of kinematic equations with arbitrary topology. Moreover, the model is akin to the concept of motor equivalence in the sense that it provides families of motor equivalent trajectories parametrized by tunable motor impedances.     

Kernel-Kohonen networks

We investigate the combination of the Kohonen networks with the kernel methods in the context of classification. We use the idea of kernel functions to handle products of vectors of arbitrary dimension. We indicate how to build Kohonen networks with robust classification performance by transformation of the original data vectors into a possibly infinite dimensional space. The resulting Kohonen networks preserve a non-Euclidean neighborhood structure of the input space that fits the properties of the data. We show how to optimize the transformation of the data vectors in order to obtain higher classification performance. We compare the kernel-Kohonen networks with the regular Kohonen networks in the context of a classification task.     

Molecular networks as a sub-neural factor of neural networks

We describe a new approach in the research of neural networks. This research is based on molecular networks in the neuron. If we use molecular networks as a sub-neuron factor of neural networks, it is a more realistic approach than today's concepts in this new computer technology field, because the artificial neural activity profile is similar to the profile of the action potential in the natural neuron. The molecular networks approach can be used in three technologies: neurocomputer, neurochip and molecular chip. This means that molecular networks open new fields of science and engineering called molecular-like machines and molecular machines.     

Unraveling spurious properties of interaction networks with tailored random networks

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erd?s-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.     

Modelling hyphal networks

The indeterminate growth habit of fungal mycelial can produce massive organisms spanning kilometres, whereas the hypha, the modular building block of these structures, is only a few microns in diameter. The qualitative and quantitative relationship between these scales is difficult to establish using experimental methods alone and a large number of mathematical models have been constructed to assist in the investigation of the multi-scale form and function of filamentous fungi. Many such models operate at the colony-scale, representing the hyphal network as either a regular lattice or as a geometrically-unconstrained structure that changes according to a minimal set of specified rules focussed on the fundamental processes responsible for growth and function. In this review we discuss the historical development and recent applications of such models and suggest some future directions.     

Salmonella-induced tubular networks

Salmonella virulence relies on its capacity to replicate inside various cell types in a membrane-bound compartment, the Salmonella-containing vacuole (SCV). A unique feature of Salmonella-infected cells is the presence of tubular structures originating from and connected to the SCV, which often extend throughout the cell cytoplasm. These tubules include the well-studied Salmonella-induced filaments (SIFs), enriched in lysosomal membrane proteins. However, recent studies revealed that the Salmonella-induced tubular network is more extensive than previously thought and includes three types of tubules distinct from SIFs: sorting nexin tubules, Salmonella-induced secretory carrier membrane protein 3 (SCAMP3) tubules and lysosome-associated membrane protein 1 (LAMP1)-negative tubules. In this review, we examine the molecular mechanisms involved in the formation of Salmonella-induced tubular networks and discuss the importance of the tubules for Salmonella virulence and establishment of a Salmonella intracellular replicative niche.     

Dominating biological networks

Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC)" genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.