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We present a scenario for the origin of biological coding, a semiotic relationship between chemical information stored in one location that links to chemical information stored in a separate location. Coding originated from cooperation between two, originally separate, collectively autocatalytic sets (CASs), one for nucleic acids and one for peptides. Upon interaction, a series of RNA folding-directed processes led to their joint cooperativity. The aminoacyl adenylate was the first covalent association made by these two CASs and solidified their interdependence, and is a palimpsest of this era, a relic of the original semiotic relationship between RNA and proteins. Coding was driven by selection pressure to eliminate waste in CASs. Eventually a 1 : 1 relationship between single amino acids and short RNA pieces was established, i.e. the 'genetic code'. The two classes of aaRS enzymes are remnants of the complementary information in two RNA strands, as postulated by Rodin and Ohno. Every stage in the evolution of coding was driven by the downward selection on the components of a system to satisfy the Kantian whole. Coding was engendered because there were two chemically distinct classes of polymers needed for open-ended evolution; systems with only one polymer cannot exhibit this characteristic. Coding is thus synonymous with life as we know it.

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Despite myriads of possible gene expression profiles, cells tend to be found in a confined number of expression patterns. The dynamics of Boolean models of gene regulatory networks has proven to be a likely candidate for the description of such self-organisation phenomena. Because cells do not live in isolation, but they constantly shape their functions to adapt to signals from other cells, this raises the question of whether the cooperation among cells entails an expansion or a reduction of their possible steady states. Multi random Boolean networks are introduced here as a model for interaction among cells that might be suitable for the investigation of some generic properties regarding the influence of communication on the diversity of cell behaviours. In spite of its simplicity, the model exhibits a non-obvious phenomenon according to which a moderate exchange of products among adjacent cells fosters the variety of their possible behaviours, which on the other hand are more similar to one another. On the contrary, a more invasive coupling would lead cells towards homogeneity.

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The response to different kinds of perturbations of a discrete model of gene regulatory network, which is a generalization of the random Boolean network (RBN) model, is discussed. The model includes memory effects, and the analysis pays particular attention to the influence on the system stability of a parameter (i.e., the decay time of the gene products) that determines the duration of the memory effects. It is shown that this parameter deeply affects the overall behavior of the system, with special regard to the dynamical regimes and the sensitivity. Furthermore, a noteworthy divergence in the response of systems characterized by different memory lengths in the presence of either temporary or permanent damages is highlighted, as is the substantial difference, with respect to classical RBNs, between the specific dynamical regime and the landscape of the attractors.

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We discuss the complex dynamics of a nonlinear random networks model as a function of the connectivity k between the elements of the network. We show that this class of networks exhibits an order-chaos phase transition for a critical connectivity k{c}=2 . Also, we show that both pairwise correlation and complexity measures are maximized in dynamically critical networks. These results are in good agreement with the previously reported studies on random Boolean networks and random threshold networks, and show once again that critical networks provide an optimal coordination of diverse behavior.

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The asymptotic dynamics of random Boolean networks subject to random fluctuations is investigated. Under the influence of noise, the system can escape from the attractors of the deterministic model, and a thorough study of these transitions is presented. We show that the dynamics is more properly described by sets of attractors rather than single ones. We generalize here a previous notion of ergodic sets, and we show that the Threshold Ergodic Sets so defined are robust with respect to noise and, at the same time, that they do not suffer from a major drawback of ergodic sets. The system jumps from one attractor to another of the same Threshold Ergodic Set under the influence of noise, never leaving it. By interpreting random Boolean networks as models of genetic regulatory networks, we also propose to associate cell types to Threshold Ergodic Sets rather than to deterministic attractors or to ergodic sets, as it had been previously suggested. We also propose to associate cell differentiation to the process whereby a Threshold Ergodic Set composed by several attractors gives rise to another one composed by a smaller number of attractors. We show that this approach accounts for several interesting experimental facts about cell differentiation, including the possibility to obtain an induced pluripotent stem cell from a fully differentiated one by overexpressing some of its genes.

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Random Threshold Networks (RTNs) are an idealized model of diluted, non-symmetric spin glasses, neural networks or gene regulatory networks. RTNs also serve as an interesting general example of any coordinated causal system. Here we study the conditions for maximal information transfer and behavior diversity in RTNs. These conditions are likely to play a major role in physical and biological systems, perhaps serving as important selective traits in biological systems. We show that the pairwise mutual information is maximized in dynamically critical networks. Also, we show that the correlated behavior diversity is maximized for slightly chaotic networks, close to the critical region. Importantly, critical networks maximize coordinated, diverse dynamical behavior across the network and across time: the information transmission between source and receiver nodes and the diversity of dynamical behaviors, when measured with a time delay between the source and receiver, are maximized for critical networks.

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Determining the structure of the gene regulatory network using the information in genomewide profiles of mRNA abundance, such as microarray data, poses several challenges. Typically, "static" rather than dynamical profile measurements, such as those taken from steady state tissues in various conditions, are the starting point. This makes the inference of causal relationships between genes difficult. Moreover, the paucity of samples relative to the gene number leads to problems such as overfitting and underconstrained regression analysis. Here we present a novel method for the sparse approximation of gene regulatory networks that addresses these issues. It is formulated as a sparse combinatorial optimization problem which has a globally optimal solution in terms of l(0) norm error. In order to seek an approximate solution of the l(0) optimization problem, we consider a heuristic approach based on iterative greedy algorithms. We apply our method to a set of gene expression profiles comprising of 24,102 genes measured over 79 human tissues. The inferred network is a signed directed graph, hence predicts causal relationships. It exhibits typical characteristics of regulatory networks organism with partially known network topology, such as the average number of inputs per gene as well as the in-degree and out-degree distribution.

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We discuss a heuristic method for the sparse reconstruction of gene networks. The method is based on iterative greedy algorithms, and uses gene expression data from microarray experiments. Also, we show numerically that the greedy algorithms are able to give good approximative solutions to the sparse reconstruction problem even in the presence of significant levels of noise.

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Monte Carlo simulations of a genetic toggle switch show that its behavior can be more complex than analytic models would suggest. We show here that as a result of the interplay between frequent and infrequent reaction events, such a switch can have more stable states than an analytic model would predict, and that the number and character of these states depend to a large extent on the propensity of transcription factors to bind to and dissociate from promoters. The effects of gene duplications differ even more; in analytic models, these seem to result in the disappearance of bi-stability and thus a loss of the switching function, but a Monte Carlo simulation shows that they can result in the appearance of new stable states without the loss of old ones, and thus in an increase of the complexity of the switch's behavior which may facilitate the evolution of new cellular functions. These differences are of interest with respect to the evolution of gene networks, particularly in clonal lines of cancer cells, where the duplication of active genes is an extremely common event, and often seems to result in the appearance of viable new cellular phenotypes.

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We show that there is a physical analogy between a stochastic model of a genetic toggle switch system and a thermostated particle moving in a potential field, derived from the probability distribution of the toggle switch. This result suggests that one can actually simulate the dynamics of a more complex gene network by considering an ensemble of thermostated particles moving in a potential field, derived from the stationary distribution of the chemical stochastic model describing the gene network.

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In a previous study it was shown that a simple random Boolean network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1.

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We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with the innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as the parameters are varied, including the broadening of the in-degree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

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Random Boolean networks, originally invented as models of genetic regulatory networks, are simple models for a broad class of complex systems that show rich dynamical structures. From a biological perspective, the most interesting networks lie at or near a critical point in parameter space that divides "ordered" from "chaotic" attractor dynamics. We study the scaling of the average number of dynamically relevant nodes and the median number of distinct attractors in such networks. Our calculations indicate that the correct asymptotic scalings emerge only for very large systems.

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This chapter is a slightly modified version of Chapter 1 of my book, Investigations. I discuss "autonomous agents," the origin of life, autocatalytic sets of polymers, work cycles, puzzles-about evolutionary theory, and the birth of a general biology dealing with biospheres anywhere in the universe.

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The theory of autocatalytic binary ligation is reviewed within the context of a consistently applied Michaelis-Menten quasi-steady-state approximation to obtain explicit analytical results describing time-course data from experiments. A detailed protocol for the step-wise elucidation of a minimal set of experimental parameters is outlined. The kinetic equations are then generalized to cases of self- and cross-catalysis among an arbitrary number of different templates and applied to experiments involving just two templates. Depending on the values of various kinetic parameters such systems can display exclusionary Darwinian selection corresponding to an exponential growth law, selective coexistence or coexistence of all species characteristic of a parabolic growth law; the intermediate behaviour arises as a property of the full mechanism analysed here. Our results are applicable to the classical case of self-replicating nucleic acids and their analogues as well as to newly discovered self-replicating peptides.

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The purpose of this study was to describe construction workers' use of hearing protection devices (HPDs) and determine their perceptions of noise exposure and hearing loss. Operating engineers, carpenters, and plumbers/pipe fitters in the Midwest (n = 400) completed a written questionnaire regarding their use of HPDs and their perceptions of noise exposure and hearing loss. Subjects were recruited through their trade union groups. Mean reported use of HPDs and mean perceived noise exposure were compared across trade groups. Bivariate and multivariate analysis techniques were used to assess relationships between use of HPDs and trade category, education, age, years of employment, noise exposure, and hearing loss. Bivariate analyses identified significant differences in mean use of HPDs by age, years of employment, and trade group. Multivariate logistic regression assessing the independent effects of these variables found significant differences only by trade group. Results indicate a need for significant improvement in all three trade groups' use of HPDs, and suggest a need to consider use and exposure levels, demographics, and trade group membership in designing hearing conservation programs.

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Local search methods constitute one of the most successful approaches to solving large-scale combinatorial optimization problems. As these methods are increasingly parallelized, optimization performance initially improves but then abruptly degrades to no better than that of random search beyond a certain point. The existence of this transition is demonstrated for a family of generalized spin-glass models and the traveling salesman problem. Finite-size scaling is used to characterize size-dependent effects near the transition, and analytical insight is obtained through a mean-field approximation.

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Libraries of random-sequence polypeptides have been shown to be valuable sources of novel molecules possessing a variety of useful biologic-like activities, some of which may hold promise as potential vaccines and therapeutics. Previous random peptide expression systems were limited to low levels of peptide production and often to short sequences. Here we describe a series of libraries designed for increased polypeptide length. Cloned as carboxy-terminal extensions of ubiquitin, the fusions were produced in E. coli at high levels, and were purified to homogeneity. The majority of the extension proteins examined could be cleaved from ubiquitin by treatment with a ubiquitin-fusion hydrolase. The libraries described here are appropriate sources of novel polypeptides with desired binding or catalytic function, as well as tools with which to examine inherent properties of proteins as a whole. Toward the latter goal, we have examined structural properties of random-sequence proteins purified from these libraries. Quite surprisingly, fluorescence emission spectra of intrinsic tryptophan residues in several purified fusion proteins, under native-like and denaturing conditions, often resemble those expected for folded and unfolded states, respectively. The results presented here detail an important expansion in the range of potential uses for random-sequence polypeptide libraries.

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A new approach to drug discovery is based on the generation of high diversity libraries of DNA, RNA, peptides or small molecules. Search of such libraries for useful molecules is an optimization problem on high-dimensional molecular fitness landscapes. We utilize a spin-glass-like model, the NK model, to analyze search strategies based on pooling, mutation, recombination and selective hill-climbing. Our results suggest that pooling followed by recombination and/or hill-climbing finds better candidate molecules than pooling alone on most molecular landscapes. Our results point to new experiments to assess the structure of molecular fitness landscapes and improve current models.

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