Self-organization of heterogeneous topology and symmetry breaking in networks with adaptive thresholds and rewiring

We study an evolutionary algorithm that locally adapts thresholds and wiring in Random Threshold Networks, based on measurements of a dynamical order parameter. A control parameter $p$ determines the probability of threshold adaptations vs. link rewiring. For any $p < 1$, we find spontaneous symmetry breaking into a new class of self-organized networks, characterized by a much higher average connectivity $\bar{K}_{evo}$ than networks without threshold adaptation ($p =1$). While $\bar{K}_{evo}$ and evolved out-degree distributions are independent from $p$ for $p <1$, in-degree distributions become broader when $p \to 1$, approaching a power-law. In this limit, time scale separation between threshold adaptions and rewiring also leads to strong correlations between thresholds and in-degree. Finally, evidence is presented that networks converge to self-organized criticality for large $N$.Comment: 4 pages revtex, 6 figure

When correlations matter - response of dynamical networks to small perturbations

We systematically study and compare damage spreading for random Boolean and threshold networks under small external perturbations (damage), a problem which is relevant to many biological networks. We identify a new characteristic connectivity $K_s$, at which the average number of damaged nodes after a large number of dynamical updates is independent of the total number of nodes $N$. We estimate the critical connectivity for finite $N$ and show that it systematically deviates from the annealed approximation. Extending the approach followed in a previous study, we present new results indicating that internal dynamical correlations tend to increase not only the probability for small, but also for very large damage events, leading to a broad, fat-tailed distribution of damage sizes. These findings indicate that the descriptive and predictive value of averaged order parameters for finite size networks - even for biologically highly relevant sizes up to several thousand nodes - is limited.Comment: 4 pages, 4 figures. Accepted for the "Workshop on Computational Systems Biology", Leipzig 200

Critical Line in Random Threshold Networks with Inhomogeneous Thresholds

We calculate analytically the critical connectivity $K_c$ of Random Threshold Networks (RTN) for homogeneous and inhomogeneous thresholds, and confirm the results by numerical simulations. We find a super-linear increase of $K_c$ with the (average) absolute threshold $|h|$, which approaches $K_c(|h|) \sim h^2/(2\ln{|h|})$ for large $|h|$, and show that this asymptotic scaling is universal for RTN with Poissonian distributed connectivity and threshold distributions with a variance that grows slower than $h^2$. Interestingly, we find that inhomogeneous distribution of thresholds leads to increased propagation of perturbations for sparsely connected networks, while for densely connected networks damage is reduced; the cross-over point yields a novel, characteristic connectivity $K_d$, that has no counterpart in Boolean networks. Last, local correlations between node thresholds and in-degree are introduced. Here, numerical simulations show that even weak (anti-)correlations can lead to a transition from ordered to chaotic dynamics, and vice versa. It is shown that the naive mean-field assumption typical for the annealed approximation leads to false predictions in this case, since correlations between thresholds and out-degree that emerge as a side-effect strongly modify damage propagation behavior.Comment: 18 figures, 17 pages revte

Learning, Generalization, and Functional Entropy in Random Automata Networks

It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a subset of the learning examples is provided for learning. Here, we extend this body of work and show experimentally that random Boolean networks (RBNs), where both the interconnections and the Boolean transfer functions are chosen at random initially, can be evolved by using a state-topology evolution to solve simple tasks. We measure the learning and generalization performance, investigate the influence of the average node connectivity $K$, the system size $N$, and introduce a new measure that allows to better describe the network's learning and generalization behavior. We show that the connectivity of the maximum entropy networks scales as a power-law of the system size $N$. Our results show that networks with higher average connectivity $K$ (supercritical) achieve higher memorization and partial generalization. However, near critical connectivity, the networks show a higher perfect generalization on the even-odd task

Damage Spreading and Criticality in Finite Random Dynamical Networks

We systematically study and compare damage spreading at the sparse percolation (SP) limit for random boolean and threshold networks with perturbations that are independent of the network size $N$. This limit is relevant to information and damage propagation in many technological and natural networks. Using finite size scaling, we identify a new characteristic connectivity $K_s$, at which the average number of damaged nodes $\bar d$, after a large number of dynamical updates, is independent of $N$. Based on marginal damage spreading, we determine the critical connectivity $K_c^{sparse}(N)$ for finite $N$ at the SP limit and show that it systematically deviates from $K_c$, established by the annealed approximation, even for large system sizes. Our findings can potentially explain the results recently obtained for gene regulatory networks and have important implications for the evolution of dynamical networks that solve specific computational or functional tasks.Comment: 4 pages, 4 eps figure

Transcriptional memory emerges from cooperative histone modifications

Background&#xd;&#xa;Transcriptional regulation in cells makes use of diverse mechanisms to ensure that functional states can be maintained and adapted to variable environments; among them are chromatin-related mechanisms. While mathematical models of transcription factor networks controlling development are well established, models of transcriptional regulation by chromatin states are rather rare despite they appear to be a powerful regulatory mechanism.&#xd;&#xa;Results&#xd;&#xa;We here introduce a mathematical model of transcriptional regulation governed by histone modifications. This model describes binding of protein complexes to chromatin which are capable of reading and writing histone marks. Molecular interactions between these complexes and DNA or histones create a regulatory switch of transcriptional activity possessing a regulatory memory. The regulatory states of the switch depend on the activity of histone (de-) methylases, the structure of the DNA-binding regions of the complexes, and the number of histones contributing to binding. &#xd;&#xa;We apply our model to transcriptional regulation by trithorax- and polycomb- complex binding. By analyzing data on pluripotent and lineage-committed cells we verify basic model assumptions and provide evidence for a positive effect of the length of the modified regions on the stability of the induced regulatory states and thus on the transcriptional memory.&#xd;&#xa;Conclusions&#xd;&#xa;Our results provide new insights into epigenetic modes of transcriptional regulation. Moreover, they implicate well-founded hypotheses on cooperative histone modifications, proliferation induced epigenetic changes and higher order folding of chromatin which await experimental validation. Our approach represents a basic step towards multi-scale models of transcriptional control during development and lineage specification. &#xd;&#xa

Geometric morphometric analysis of grain shape and the identification of two-rowed barley (Hordeum vulgare subsp distichum L.) in southern France

Open Access funded by Natural Environment Research Council Under a Creative Commons license We would like to thank Michel Lemoine (CNRS, Muséum), for his invaluable help during the carbonization of the fresh caryopses. We are also most grateful to the society Secobra for providing the fresh caryopses used in this study, to Raphaël Cornette (UMR7205) for welcoming us into the morphometric platform of the National Museum of Paris, to prof. Jean- Frédéric Terral (University Montpellier 2) for his advice and to Elizabeth Kerr (UMR7209) and Nelly Gidaszewski (UMR7205) for language editing. A. Evin acknowledges financial support from the Natural Environment Research Council, UK (grant number NE/F003382/1). Finally, we would like to thank the UMR7209 (CNRS-MNHN), for financial support.Peer reviewedPublisher PD