Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

A coiled coil trigger site is essential for rapid binding of synaptobrevin to the SNARE acceptor complex.

Exocytosis from synaptic vesicles is driven by stepwise formation of a tight alpha-helical complex between the fusing membranes. The complex is composed of the three SNAREs: synaptobrevin 2, SNAP-25, and syntaxin 1a. An important step in complex formation is fast binding of vesicular synaptobrevin to the preformed syntaxin 1.SNAP-25 dimer. Exactly how this step relates to neurotransmitter release is not well understood. Here, we combined different approaches to gain insights into this reaction. Using computational methods, we identified a stretch in synaptobrevin 2 that may function as a coiled coil "trigger site." This site is also present in many synaptobrevin homologs functioning in other trafficking steps. Point mutations in this stretch inhibited binding to the syntaxin 1.SNAP-25 dimer and slowed fusion of liposomes. Moreover, the point mutations severely inhibited secretion from chromaffin cells. Altogether, this demonstrates that the trigger site in synaptobrevin is crucial for productive SNARE zippering.

A neuropsychological instrument measuring age-related cerebral decline in older drivers: development, reliability, and validity of MedDrive.

When facing age-related cerebral decline, older adults are unequally affected by cognitive impairment without us knowing why. To explore underlying mechanisms and find possible solutions to maintain life-space mobility, there is a need for a standardized behavioral test that relates to behaviors in natural environments. The aim of the project described in this paper was therefore to provide a free, reliable, transparent, computer-based instrument capable of detecting age-related changes on visual processing and cortical functions for the purposes of research into human behavior in computational transportation science. After obtaining content validity, exploring psychometric properties of the developed tasks, we derived (Study 1) the scoring method for measuring cerebral decline on 106 older drivers aged ≥70 years attending a driving refresher course organized by the Swiss Automobile Association to test the instrument's validity against on-road driving performance (106 older drivers). We then validated the derived method on a new sample of 182 drivers (Study 2). We then measured the instrument's reliability having 17 healthy, young volunteers repeat all tests included in the instrument five times (Study 3) and explored the instrument's psychophysical underlying functions on 47 older drivers (Study 4). Finally, we tested the instrument's responsiveness to alcohol and effects on performance on a driving simulator in a randomized, double-blinded, placebo, crossover, dose-response, validation trial including 20 healthy, young volunteers (Study 5). The developed instrument revealed good psychometric properties related to processing speed. It was reliable (ICC = 0.853) and showed reasonable association to driving performance (R (2) = 0.053), and responded to blood alcohol concentrations of 0.5 g/L (p = 0.008). Our results suggest that MedDrive is capable of detecting age-related changes that affect processing speed. These changes nevertheless do not necessarily affect driving behavior.

Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.

Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from diverse sources poses computational challenges for such process reconstruction. We introduce a scalable Bayesian inference framework that properly accounts for population heterogeneity. The method allows inference of inaccessible molecular states and kinetic parameters; computation of Bayes factors for model selection; and dissection of intrinsic, extrinsic and technical noise. We show how additional single-cell readouts such as morphological features can be included in the analysis. We use the method to reconstruct the expression dynamics of a gene under an inducible promoter in yeast from time-lapse microscopy data.

Identifying protein-coding genes in genomic sequences.

The vast majority of the biology of a newly sequenced genome is inferred from the set of encoded proteins. Predicting this set is therefore invariably the first step after the completion of the genome DNA sequence. Here we review the main computational pipelines used to generate the human reference protein-coding gene sets.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Processos de relativização e marcadores relativos em crioulo de Cabo Verde

Segundo Comrie (1981:148), «a given language may have more than one type of relative clause construction in its over-all battery of relative clause formation possibilities. (…) The distribution of types within a language, however, is not completely arbitrary (…)». Dado que o Crioulo de Cabo Verde (CCV), variante de Santiago, se comporta desta maneira relativamente àquela área da gramática, a análise das orações relativas nesta língua de base lexical portuguesa assume uma importância acrescida quer descritivamente, quer tipologicamente. Deste modo, o artigo que se apresenta foca os seguintes tópicos da sintaxe de relativização do CCV: (i) Tipos de orações relativas em Crioulo de Cabo Verde (cf. secção 2.); (ii) Estratégias de relativização em CCV (cf. secção 2.); (iii) Natureza dos marcadores relativos em CCV (cf. secção 3.). As orações relativas diferem entre si relativamente a vários aspectos, sendo o resultado da interacção de factores sintácticos e semânticos. Cada tipo de oração relativa pode envolver uma ou mais estratégias de relativização, obedecendo a determinadas condições sintácticas, como se expõe no Quadro

Upper Guinea Creole Evidence in favor of a Santiago birth

This paper addresses the debate on the place of origin of the Upper Guinea branch of Portuguese Creole (UGPC) as spoken in Guinea-Bissau and Casamance (GBC)1 and on the Santiago Island of Cape Verde (SCV). The hypothesis that UGPC emerged on Santiago rather than on the mainland is underpinned both historically and linguistically. First, a historical framework is presented that accounts for the linguistic transfer from Santiago to Cacheu. Secondly, Parkvall’s (2000) lexical evidence in favor of a Santiago birth will be analyzed and corroborated. Thirdly, a phonological trait that separates GBC from SCV is highlighted and shown to favor a Santiago origin. Finally, lexical and phonological features typical of 15th–16th century Portuguese shared by GBC and SCV are combined with historical data to further strengthen the Santiago birth hypothesis.

A Cross-linguistic comparison of copular predication

This paper explores the syntax of copular predication withn and across the varletles of cape verdean creole briging new insights about the morpho-synactic properies of the copula with respect to funcional and lexical categories

The European dimension for the mouse genome mutagenesis program.

The European Mouse Mutagenesis Consortium is the European initiative contributing to the international effort on functional annotation of the mouse genome. Its objectives are to establish and integrate mutagenesis platforms, gene expression resources, phenotyping units, storage and distribution centers and bioinformatics resources. The combined efforts will accelerate our understanding of gene function and of human health and disease.

Os crioulos da Alta Guiné e do Golfo da Guiné

Este artigo incide sobre os crioulos de base lexical portuguesa da Alta Guiné e do Golfo da Guiné, que constituem duas famílias linguísticas distintas, de formação independente, tendo por objectivo mostrar algumas tendências de convergência e de divergência a nível da estrutura sintáctica.

A polynomial algorithm for special case of the one-machine scheduling problem with time-lags

The standard one-machine scheduling problem consists in schedulinga set of jobs in one machine which can handle only one job at atime, minimizing the maximum lateness. Each job is available forprocessing at its release date, requires a known processing timeand after finishing the processing, it is delivery after a certaintime. There also can exists precedence constraints between pairsof jobs, requiring that the first jobs must be completed beforethe second job can start. An extension of this problem consistsin assigning a time interval between the processing of the jobsassociated with the precedence constrains, known by finish-starttime-lags. In presence of this constraints, the problem is NP-hardeven if preemption is allowed. In this work, we consider a specialcase of the one-machine preemption scheduling problem with time-lags, where the time-lags have a chain form, and propose apolynomial algorithm to solve it. The algorithm consist in apolynomial number of calls of the preemption version of the LongestTail Heuristic. One of the applicability of the method is to obtainlower bounds for NP-hard one-machine and job-shop schedulingproblems. We present some computational results of thisapplication, followed by some conclusions.