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We present a combined mean-field and simulation approach to different models describing the dynamics of classes formed by elements that can appear, disappear or copy themselves. These models, related to a paradigm duplication-innovation model known as Chinese Restaurant Process, are devised to reproduce the scaling behavior observed in the genome-wide repertoire of protein domains of all known species. In view of these data, we discuss the qualitative and quantitative differences of the alternative model formulations, focusing in particular on the roles of element loss and of the specificity of empirical domain classes., Comment: 10 Figures, 2 Tables

High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as realistic and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies., Comment: to appear on Mol. BioSyst. 2009

Analyzing the properties of duplicate genes during evolution is useful to understand the development of new cell functions. The yeast S. cerevisiae is a useful testing ground for this problem, because its duplicated genes with different evolutionary birth and destiny are well distinguishable. In particular, there is a clear detection for the occurrence of a Whole Genome Duplication (WGD) event in S. cerevisiae, and the genes derived from this event (WGD paralogs) are known. We studied WGD and non-WGD duplicates by two parallel analysis based on structural protein domains and on Gene Ontology annotation scheme respectively. The results show that while a large number of ``duplicable'' structural domains is shared in local and global duplications, WGD and non-WGD paralogs tend to have different functions. The reason for this is the existence of WGD and non-WGD specific domains with largely different functions. In agreement with the recent findings of Wapinski and collaborators (Nature 449, 2007), WGD paralogs often perform ``core'' cell functions, such as translation and DNA replication, while local duplications associate with ``peripheral'' functions such as response to stress. Our results also support the fact that domain architectures are a reliable tool to detect homology, as the domains of duplicates are largely invariant with date and nature of the duplication, while their sequences and also their functions might migrate., Comment: 19 pages, 5 figures, Supporting Information

We analyse a model of fixed in-degree Random Boolean Networks in which the fraction of input-receiving nodes is controlled by a parameter gamma. We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control, and as rich as the one of general Boolean networks. Biologically, it is justified on abstract grounds by the fact that all existing interactions play a dynamical role. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying gamma, this graph ensemble shows a phase transition that separates a tree-like graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact we provide analytical estimates of the maximum period starting from topological considerations.

Protein domains are found on genomes with notable statistical distributions, which bear a high degree of similarity. Previous work has shown how these distributions can be accounted for by simple models, where the main ingredients are probabilities of duplication, innovation, and loss of domains. However, no one so far has addressed the issue that these distributions follow definite trends depending on protein-coding genome size only. We present a stochastic duplication/innovation model, falling in the class of so-called Chinese Restaurant Processes, able to explain this feature of the data. Using only two universal parameters, related to a minimal number of domains and to the relative weight of innovation to duplication, the model reproduces two important aspects: (a) the populations of domain classes (the sets, related to homology classes, containing realizations of the same domain in different proteins) follow common power-laws whose cutoff is dictated by genome size, and (b) the number of domain families is universal and markedly sublinear in genome size. An important ingredient of the model is that the innovation probability decreases with genome size. We propose the possibility to interpret this as a global constraint given by the cost of expanding an increasingly complex interactome.

Swimming and pumping at low Reynolds numbers are subject to the "Scallop theorem", which states that there will be no net fluid flow for time reversible motions. Living organisms such as bacteria and cells are subject to this constraint, and so are existing and future artificial "nano-bots" or microfluidic pumps. We study a very simple mechanism to induce fluid pumping, based on the forced motion of three colloidal beads through a cycle that breaks time-reversal symmetry. Optical tweezers are used to vary the inter-bead distance. This model is inspired by a strut-based theoretical swimmer proposed by Najafi and Golestanian [Phys.Rev. E, 69, 062901, 2004], but in this work the relative softness of the optical trapping potential introduces a new control parameter. We show that this system is able to generate flow in a controlled fashion, characterizing the model experimentally and numerically., Comment: 14 pages, 6 figures, revised version, accepted for publication in Soft Matter, corrected typos

We present a comparative analysis of large-scale topological and evolutionary properties of transcription networks in three species, the two distant bacteria E. coli and B. subtilis, and the yeast S. cerevisiae. The study focuses on the global aspects of feedback and hierarchy in transcriptional regulatory pathways. While confirming that gene duplication has a significant impact on the shaping of all the analyzed transcription networks, our results point to distinct trends between the bacteria, where time constraints in the transcription of downstream genes might be important in shaping the hierarchical structure of the network, and yeast, which seems able to sustain a higher wiring complexity, that includes the more feedback, intricate hierarchy, and the combinatorial use of heterodimers made of duplicate transcription factors.

Transcription networks, and other directed networks can be characterized by some topological observables such as for example subgraph occurrence (network motifs). In order to perform such kind of analysis, it is necessary to be able to generate suitable randomized network ensembles. Typically, one considers null networks with the same degree sequences of the original ones. The commonly used algorithms sometimes have long convergence times, and sampling problems. We present here an alternative, based on a variant of the importance sampling Montecarlo developed by Chen et al. [1]., Comment: 6 pages and 1 figure, included supplementary mathematical notes

The E.coli transcription network has an essentially feedforward structure, with, however, abundant feedback at the level of self-regulations. Here, we investigate how these properties emerged during evolution. An assessment of the role of gene duplication based on protein domain architecture shows that (i) transcriptional autoregulators have mostly arisen through duplication, while (ii) the expected feedback loops stemming from their initial cross-regulation are strongly selected against. This requires a divergent coevolution of the transcription factor DNA-binding sites and their respective DNA cis-regulatory regions. Moreover, we find that the network tends to grow by expansion of the existing hierarchical layers of computation, rather than by addition of new layers. We also argue that rewiring of regulatory links due to mutation/selection of novel transcription factor/DNA binding interactions appears not to significantly affect the network global hierarchy, and that horizontally transferred genes are mainly added at the bottom, as new target nodes. These findings highlight the important evolutionary roles of both duplication and selective deletion of crosstalks between autoregulators in the emergence of the hierarchical transcription network of E.coli., Comment: to appear in PNAS

Having in mind the large-scale analysis of gene regulatory networks, we review a graph decimation algorithm, called "leaf-removal", which can be used to evaluate the feedback in a random graph ensemble. In doing this, we consider the possibility of analyzing networks where the diagonal of the adjacency matrix is structured, that is, has a fixed number of nonzero entries. We test these ideas on a network model with fixed degree, using both numerical and analytical calculations. Our results are the following. First, the leaf-removal behavior for large system size enables to distinguish between different regimes of feedback. We show their relations and the connection with the onset of complexity in the graph. Second, the influence of the diagonal structure on this behavior can be relevant., Comment: To appear in: Proceedings of the CMSB conference, Lecture Notes in Bioinformatics, Springer, 2006

In statistical mechanical investigations on complex networks, it is useful to employ random graphs ensembles as null models, to compare with experimental realizations. Motivated by transcription networks, we present here a simple way to generate an ensemble of random directed graphs with, asymptotically, scale-free outdegree and compact indegree. Entries in each row of the adjacency matrix are set to be zero or one according to the toss of a biased coin, with a chosen probability distribution for the biases. This defines a quick and simple algorithm, which yields good results already for graphs of size n ~ 100. Perhaps more importantly, many of the relevant observables are accessible analytically, improving upon previous estimates for similar graphs.

Identity, response to external stimuli, and spatial architecture of a living system are central topics of molecular biology. Presently, they are largely seen as a result of the interplay between a gene repertoire and the regulatory machinery of the cell. At the transcriptional level, the cis-regulatory regions establish sets of interdependencies between transcription factors and genes, including other transcription factors. These ``transcription networks'' are too large to be approached globally with a detailed dynamical model. In this paper, we describe an approach to this problem that focuses solely on the compatibility between gene expression patterns and signal integration functions, discussing calculations carried on the simplest, Boolean, realization of the model, and a first application to experimental data sets.

A great part of the effort in the study of coarse grained models of transcription networks is directed to the analysis of their dynamical features. In this letter, we consider the \emph{equilibrium} properties of such systems, showing that the logic backbone underlying all dynamic descriptions has the structure of a computational optimization problem. It involves variables, which correspond to gene expression levels, and constraints, which describe the effect of \emph{cis-}regulatory signal integration functions. In the simple paradigmatic case of Boolean variables and signal integration functions, we derive and discuss phase diagrams. Notably, the model exhibits a connectivity transition between a regime of simple, but uncertain, gene control, to a regime of complex combinatorial control., Comment: 11 pages, 4 figures final

We employ a model system, called rowers, as a generic physical framework to define the problem of the coordinated motion of cilia (the metachronal wave) as a far from equilibrium process. Rowers are active (two-state) oscillators interacting solely through forces of hydrodynamic origin. In this work, we consider the case of fully deterministic dynamics, find analytical solutions of the equation of motion in the long wavelength (continuum) limit, and investigate numerically the short wavelength limit. We prove the existence of metachronal waves below a characteristic wavelength. Such waves are unstable and become stable only if the sign of the coupling is reversed. We also find that with normal hydrodynamic interaction the metachronal pattern has the form of stable trains of traveling wave packets sustained by the onset of anti-coordinated beating of consecutive rowers., Comment: 11 pages, 7 figures

We introduce a model system of stochastic entities, called 'rowers' which include some essentialities of the behavior of real cilia. We introduce and discuss the problem of symmetry breaking for these objects and its connection with the onset of macroscopic, directed flow in the fluid. We perform a mean field-like calculation showing that hydrodynamic interaction may provide for the symmetry breaking mechanism and the onset of fluid flow. Finally, we discuss the problem of the metachronal wave in a stochastic context., Comment: Version 3

We propose a two-dimensional model for the organization of stabilized microtubules driven by molecular motors in an unconfined geometry. In this model two kinds of dynamics are competing. The first one is purely diffusive, with an interaction between the rotational degrees of freedom, the second one is a local drive, dependent on microtubule polarity. As a result, there is a configuration dependent driving field. Applying a molecular field approximation, we are able to derive continuum equations. A study on the solutions shows nonequilibrium steady states. The presence and stability of such self-organized states are investigated in terms of entropy production. Numerical simulations confirm analytical results., Comment: 23 pages, 10 figures, LaTeX, eps

A new model for lamellar surfaces formed by anisotropic molecules is proposed. The molecules have internal degrees of freedom, associated with their flexible section of length $N$ at zero temperature. We obtain a 2D non-standard six vertex model, which is exactly soluble and exhibits a finite order transition. The order and the character of the transition are determined by the dominant term in the $1 \over N$-expansion of the interaction energy. The dependence of the critical temperatures on $N$ is, instead, determined by the non-leading terms in the same expansion., Comment: 26 pages,plane tex, 5 figures not included, bassetti@mi.infn.it