Evolution of biological interaction networks: from models to real data

We are beginning to uncover common mechanisms leading to the evolution of biological networks. The driving force behind these advances is the increasing availability of comparative data in several species.     

A real time learning algorithm for recurrent analog neural networks

A new learning algorithm is described for a general class of recurrent analog neural networks which ultimately settle down to a steady state. Recently, Pineda (Pineda 1987; Almeida 1987; Ikeda et al. 1988) has introduced a learning rule for the recurrent net in which the connection weights are adjusted so that the distance between the stable outputs of the current system and the desired outputs will be maximally decreased. In this method, many cycles are needed in order to get a target system. In each cycle, the recurrent net is run until it reaches a stable state. After that, the weight change is calculated by using a linearized recurrent net which receives the current error of the system as a bias input. In the new algorithm the weights are changed so that the total error of neuron outputs over the entire trajectory is minimized. The weights are adjusted in real time when the network is running. In this method, the trajectory to the target system can be controlled, whereas Pineda's algorithm only controls the position of the fixed point. The relation to the back propagation method (Hinton et al. 1986) is also discussed.     

Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

Background  

This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics.     

Big roles for small RNAs in polyploidy, hybrid vigor, and hybrid incompatibility

Small RNAs, including microRNAs (miRNAs), small interfering RNAs (siRNAs), and trans-acting siRNAs (ta-siRNAs), mediate gene expression and epigenetic regulation. While siRNAs are highly diverged, miRNAs and ta-siRNAs are generally conserved but many are differentially expressed between related species and in interspecific hybrids and allopolyploids. On one hand, combination of diverged maternal and paternal siRNAs in the same nucleus may exert cis-acting and trans-acting effects on transposable elements (TEs) and TE-associated genes, leading to genomic instability and endosperm and embryo failures, constituting a bottleneck for the evolution of hybrids and polyploids. On the other hand, cis and trans-acting small RNAs induce quantitative and qualitative changes in epigenetic regulation, leading to morphological variation and hybrid vigor in F1 hybrids and stable allopolyploids as well as transgressive phenotypes in the progeny, increasing a potential for adaptive evolution.     

Creation of hybrid vigor through nuclear transplantation in Phytophthora

When isolated nuclei of a diploid oomycete, Phytophthora parasitica, were fused with protoplasts of another strain of the same species, the regenerated nuclear hybrids grew faster than the parental isolates. Such a phenomenon did not occur in hybrids regenerated from mitochondrion-protoplast or protoplast-protoplast fusion products between these two strains. These results indicate that hybrid vigor is the result of the interaction between two different kinds of nuclei, but not between mitochondria, and they suggest that the presence of mitochondria from nuclear donor cells represses the expression of increased vigor. The nuclear hybrids also expressed increased fungicide resistance and propagule production. Increased vigor in growth was also observed in the interspecific nuclear hybrids when isolated nuclei of P. parasitica were transferred into protoplasts of Phytophthora capsici, and vice versa. This phenomenon may have potential applications, such as the creation of superior fungal strains and plant cultivars with improved commercial traits for usage in industry and agriculture.     

Discrete time piecewise affine models of genetic regulatory networks

We introduce simple models of genetic regulatory networks and we proceed to the mathematical analysis of their dynamics. The models are discrete time dynamical systems generated by piecewise affine contracting mappings whose variables represent gene expression levels. These models reduce to boolean networks in one limiting case of a parameter, and their asymptotic dynamics approaches that of a differential equation in another limiting case of this parameter. For intermediate values, the model present an original phenomenology which is argued to be due to delay effects. This phenomenology is not limited to piecewise affine model but extends to smooth nonlinear discrete time models of regulatory networks. In a first step, our analysis concerns general properties of networks on arbitrary graphs (characterisation of the attractor, symbolic dynamics, Lyapunov stability, structural stability, symmetries, etc). In a second step, focus is made on simple circuits for which the attractor and its changes with parameters are described. In the negative circuit of 2 genes, a thorough study is presented which concern stable (quasi-)periodic oscillations governed by rotations on the unit circle – with a rotation number depending continuously and monotonically on threshold parameters. These regular oscillations exist in negative circuits with arbitrary number of genes where they are most likely to be observed in genetic systems with non-negligible delay effects.     

Hybrids in real time

We describe some new and recent results that allow for the analysis and representation of reticulate evolution by non-tree networks. In particular, we (1) present a simple result to show that, despite the presence of reticulation, there is always a well-defined underlying tree that corresponds to those parts of life that do not have a history of reticulation; (2) describe and apply new theory for determining the smallest number of hybridization events required to explain conflicting gene trees; and (3) present a new algorithm to determine whether an arbitrary rooted network can be realized by contemporaneous reticulation events. We illustrate these results with examples. [Directed acyclic graph; reticulate evolution; hybrid species; sub-tree prune and re-graft.].     

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.     

The locust olfactory system as a case study for modeling dynamics of neurobiological networks: from discrete time neurons to continuous time neurons

Both chaotic and periodic activities are observed in networks of the central nervous systems. We choose the locust olfactory system as a good case study to analyze the relationships between networks' structure and the types of dynamics involved in coding mechanisms. In our modeling approach, we first build a fully connected recurrent network of synchronously updated McCulloch and Pitts neurons (MC-P type). In order to measure the use of the temporal dimension in the complex spatio-temporal patterns produced by the networks, we have defined an index the Normalized Euclidian Distance NED. We find that for appropriate parameters of input and connectivity, when adding some strong connections to the initial random synaptic matrices, it was easy to get the emergence of both robust oscillations and distributed synchrony in the spatiotemporal patterns. Then, in order to validate the MC-P model as a tool for analysis for network properties, we examine the dynamic behavior of networks of continuous time model neuron (Izhikevitch Integrate and Fire model -IFI-), implementing the same network characteristics. In both models, similarly to biological PN, the activity of excitatory neurons are phase-locked to different cycles of oscillations which remind the ones of the local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes.     

Chromosome condensation: DNA compaction in real time

Mitotic chromosomes must be organised into a highly ordered and compacted form to allow proper segregation of DNA during each round of cell division. Two new studies report observations of DNA compaction by eukaryotic and bacterial condensin molecules in real time using magnetic and optical trapping micromanipulation techniques.     

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away") and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.     

Early maternal investment in mice: no evidence for compatible-genes sexual selection despite hybrid vigor

Confronting a recently mated female with a strange male can induce a pregnancy block ('Bruce effect'). The physiology of this effect is well studied, but its functional significance is still not fully understood. The 'anticipated infanticide hypothesis' suggests that the pregnancy block serves to avoid the cost of embryogenesis and giving birth to offspring that are likely to be killed by a new territory holder. Some 'compatible-genes sexual selection hypotheses' suggest that the likelihood of a pregnancy block is also dependent on the female's perception of the stud's and the stimulus male's genetic quality. We used two inbred strains of mice (C57BL/6 and BALB/c) to test all possible combinations of female strain, stud strain, and stimulus strain under experimental conditions (N(total) = 241 mated females). As predicted from previous studies, we found increased rates of pregnancy blocks if stud and stimulus strains differed, and we found evidence for hybrid vigour in offspring of between-strain mating. Despite the observed heterosis, pregnancies of within-strain matings were not more likely to be blocked than pregnancies of between-strain matings. A power analysis revealed that if we missed an existing effect (type-II error), the effect must be very small. If a female gave birth, the number and weight of newborns were not significantly influenced by the stimulus males. In conclusion, we found no support for the 'compatible-genes sexual selection hypotheses'.     

Neural coding: hybrid analog and digital signalling in axons

Mammalian axons are thought to act as digital signaling devices, conveying information only by the timing and rate of all-or-none action potentials. Two recent studies now show that synaptic potentials can also spread far down the axon and influence action potential-triggered transmitter release in a graded, 'analog' manner. Axons thus encode information both about subthreshold and suprathreshold synaptic activity.     

Neural networks with constrained inputs as models for pattern formation in primate visual cortex

We present a neural network model for the formation of ocular dominance stripes on primate visual cortex and examine the generic phase behavior and dynamics of the model. The dynamical equation of ocular dominance development can be identified with a class of Langevin equations with a nonconserved order parameter. We first set up and examine an Ising model with long-range interactions in an external field, which is equivalent to the model described by the Langevin equation. We use both mean-field theory and Monte-Carlo simulations to study the equilibrium phase diagram of this equivalent Ising model. The phase diagram comprises three phases: a striped phase, a hexagonal bubble phase, and a uniform paramagnetic phase. We then examine the dynamics of the striped phase by solving the Langevin equation both numerically and by singular perturbation theory. Finally, we compare the results of the model with physiological data. The typical striped structure of the ocular dominance columns corresponds to the zero-field configurations of the model. Monocular deprivation can be simulated by allowing the system to evolve in the absence of an external field at early times and then continuing the simulation in the presence of an external field. The physical and physiological applications of our model are discussed in the conclusion.     

Koala retrovirus: a genome invasion in real time

Koalas are currently undergoing a wave of germline infections by the retrovirus KoRV. Study of this phenomenon not only provides an opportunity for understanding the processes regulating retrovirus endogenization but may also be essential to preventing the extinction of the species.     

Neural networks in intestinal immunoregulation

Key physiological functions of the intestine are governed by nerves and neurotransmitters. This complex control relies on two neuronal systems: an extrinsic innervation supplied by the two branches of the autonomic nervous system and an intrinsic innervation provided by the enteric nervous system. As a result of constant exposure to commensal and pathogenic microflora, the intestine developed a tightly regulated immune system. In this review, we cover the current knowledge on the interactions between the gut innervation and the intestinal immune system. The relations between extrinsic and intrinsic neuronal inputs are highlighted with regards to the intestinal immune response. Moreover, we discuss the latest findings on mechanisms underlying inflammatory neural reflexes and examine their relevance in the context of the intestinal inflammation. Finally, we discuss some of the recent data on the identification of the gut microbiota as an emerging player influencing the brain function.     

Neural networks and REM sleep

The nature of certain forms of memory is discussed in relation to neural networks and REM sleep.