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.

Blocked autophagy sensitizes resistant carcinoma cells to radiation therapy.

Autophagy or "self eating" is frequently activated in tumor cells treated with chemotherapy or irradiation. Whether autophagy represents a survival mechanism or rather contributes to cell death remains controversial. To address this issue, the role of autophagy in radiosensitive and radioresistant human cancer cell lines in response to gamma-irradiation was examined. We found irradiation-induced accumulation of autophagosomes accompanied by strong mRNA induction of the autophagy-related genes beclin 1, atg3, atg4b, atg4c, atg5, and atg12 in each cell line. Transduction of specific target-siRNAs led to down-regulation of these genes for up to 8 days as shown by reverse transcription-PCR and Western blot analysis. Blockade of each autophagy-related gene was associated with strongly diminished accumulation of autophagosomes after irradiation. As shown by clonogenic survival, the majority of inhibited autophagy-related genes, each alone or combined, resulted in sensitization of resistant carcinoma cells to radiation, whereas untreated resistant cells but not sensitive cells survived better when autophagy was inhibited. Similarly, radiosensitization or the opposite was observed in different sensitive carcinoma cells and upon inhibition of different autophagy genes. Mutant p53 had no effect on accumulation of autophagosomes but slightly increased clonogenic survival, as expected, because mutated p53 protects cells by conferring resistance to apoptosis. In our system, short-time inhibition of autophagy along with radiotherapy lead to enhanced cytotoxicity of radiotherapy in resistant cancer cells.

A general model to predict individual exposure to solar UV by using ambient irradiance data

Excessive exposure to solar ultraviolet (UV) is the main cause of skin cancer. Specific prevention should be further developed to target overexposed or highly vulnerable populations. A better characterisation of anatomical UV exposure patterns is however needed for specific prevention. To develop a regression model for predicting the UV exposure ratio (ER, ratio between the anatomical dose and the corresponding ground level dose) for each body site without requiring individual measurements. A 3D numeric model (SimUVEx) was used to compute ER for various body sites and postures. A multiple fractional polynomial regression analysis was performed to identify predictors of ER. The regression model used simulation data and its performance was tested on an independent data set. Two input variables were sufficient to explain ER: the cosine of the maximal daily solar zenith angle and the fraction of the sky visible from the body site. The regression model was in good agreement with the simulated data ER (R(2)=0.988). Relative errors up to +20% and -10% were found in daily doses predictions, whereas an average relative error of only 2.4% (-0.03% to 5.4%) was found in yearly dose predictions. The regression model predicts accurately ER and UV doses on the basis of readily available data such as global UV erythemal irradiance measured at ground surface stations or inferred from satellite information. It renders the development of exposure data on a wide temporal and geographical scale possible and opens broad perspectives for epidemiological studies and skin cancer prevention.

Dormant and self-renewing hematopoietic stem cells and their niches.

In the mouse, over the last 20 years, a set of cell-surface markers and activities have been identified, enabling the isolation of bone marrow (BM) populations highly enriched in hematopoietic stem cells (HSCs). These HSCs have the ability to generate multiple lineages and are capable of long-term self-renewal activity such that they are able to reconstitute and maintain a functional hematopoietic system after transplantation into lethally irradiated recipients. Using single-cell reconstitution assays, various marker combinations can be used to achieve a functional HSC purity of almost 50%. Here we have used the differential expression of six of these markers (Sca1, c-Kit, CD135, CD48, CD150, and CD34) on lineage-depleted BM to refine cell hierarchies within the HSC population. At the top of the hierarchy, we propose a dormant HSC population (Lin(-)Sca1(+)c-Kit(+) CD48(-)CD150(+)CD34(-)) that gives rise to an active self-renewing CD34(+) HSC population. HSC dormancy, as well as the balance between self-renewal and differentiation activity, is at least, in part, controlled by the stem cell niches individual HSCs are attached to. Here we review the current knowledge about HSC niches and propose that dormant HSCs are located in niches at the endosteum, whereas activated HSCs are in close contact to sinusoids of the BM microvasculature.

Spatial and temporal dynamics of jasmonate synthesis and accumulation in Arabidopsis in response to wounding.

A new metabolite profiling approach combined with an ultrarapid sample preparation procedure was used to study the temporal and spatial dynamics of the wound-induced accumulation of jasmonic acid (JA) and its oxygenated derivatives in Arabidopsis thaliana. In addition to well known jasmonates, including hydroxyjasmonates (HOJAs), jasmonoyl-isoleucine (JA-Ile), and its 12-hydroxy derivative (12-HOJA-Ile), a new wound-induced dicarboxyjasmonate, 12-carboxyjasmonoyl-l-isoleucine (12-HOOCJA-Ile) was discovered. HOJAs and 12-HOOCJA-Ile were enriched in the midveins of wounded leaves, strongly differentiating them from the other jasmonate metabolites studied. The polarity of these oxylipins at physiological pH correlated with their appearance in midveins. When the time points of accumulation of different jasmonates were determined, JA levels were found to increase within 2-5 min of wounding. Remarkably, these changes occurred throughout the plant and were not restricted to wounded leaves. The speed of the stimulus leading to JA accumulation in leaves distal to a wound is at least 3 cm/min. The data give new insights into the spatial and temporal accumulation of jasmonates and have implications in the understanding of long-distance wound signaling in plants.

NS2 protein of hepatitis C virus interacts with structural and non-structural proteins towards virus assembly.

Growing experimental evidence indicates that, in addition to the physical virion components, the non-structural proteins of hepatitis C virus (HCV) are intimately involved in orchestrating morphogenesis. Since it is dispensable for HCV RNA replication, the non-structural viral protein NS2 is suggested to play a central role in HCV particle assembly. However, despite genetic evidences, we have almost no understanding about NS2 protein-protein interactions and their role in the production of infectious particles. Here, we used co-immunoprecipitation and/or fluorescence resonance energy transfer with fluorescence lifetime imaging microscopy analyses to study the interactions between NS2 and the viroporin p7 and the HCV glycoprotein E2. In addition, we used alanine scanning insertion mutagenesis as well as other mutations in the context of an infectious virus to investigate the functional role of NS2 in HCV assembly. Finally, the subcellular localization of NS2 and several mutants was analyzed by confocal microscopy. Our data demonstrate molecular interactions between NS2 and p7 and E2. Furthermore, we show that, in the context of an infectious virus, NS2 accumulates over time in endoplasmic reticulum-derived dotted structures and colocalizes with both the envelope glycoproteins and components of the replication complex in close proximity to the HCV core protein and lipid droplets, a location that has been shown to be essential for virus assembly. We show that NS2 transmembrane region is crucial for both E2 interaction and subcellular localization. Moreover, specific mutations in core, envelope proteins, p7 and NS5A reported to abolish viral assembly changed the subcellular localization of NS2 protein. Together, these observations indicate that NS2 protein attracts the envelope proteins at the assembly site and it crosstalks with non-structural proteins for virus assembly.

The IFN gamma receptor: a paradigm for cytokine receptor signaling.

During the last several years, the mechanism of IFN gamma-dependent signal transduction has been the focus of intense investigation. This research has recently culminated in the elucidation of a comprehensive molecular understanding of the events that underlie IFN gamma-induced cellular responses. The structure and function of the IFN gamma receptor have been defined. The mechanism of IFN gamma signal transduction has been largely elucidated, and the physiologic relevance of this process validated. Most recently, the molecular events that link receptor ligation to signal transduction have been established. Together these insights have produced a model of IFN gamma signaling that is nearly complete and that serves as a paradigm for signaling by other members of the cytokine receptor superfamily.

MAMOT: hidden Markov modeling tool.

Hidden Markov models (HMMs) are probabilistic models that are well adapted to many tasks in bioinformatics, for example, for predicting the occurrence of specific motifs in biological sequences. MAMOT is a command-line program for Unix-like operating systems, including MacOS X, that we developed to allow scientists to apply HMMs more easily in their research. One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding. AVAILABILITY: MAMOT is implemented in C++, and is distributed under the GNU General Public Licence (GPL). The software, documentation, and example model files can be found at http://bcf.isb-sib.ch/mamot

Geometry and physics of catenanes applied to the study of DNA replication.

The concept of ideal geometric configurations was recently applied to the classification and characterization of various knots. Different knots in their ideal form (i.e., the one requiring the shortest length of a constant-diameter tube to form a given knot) were shown to have an overall compactness proportional to the time-averaged compactness of thermally agitated knotted polymers forming corresponding knots. This was useful for predicting the relative speed of electrophoretic migration of different DNA knots. Here we characterize the ideal geometric configurations of catenanes (called links by mathematicians), i.e., closed curves in space that are topologically linked to each other. We demonstrate that the ideal configurations of different catenanes show interrelations very similar to those observed in the ideal configurations of knots. By analyzing literature data on electrophoretic separations of the torus-type of DNA catenanes with increasing complexity, we observed that their electrophoretic migration is roughly proportional to the overall compactness of ideal representations of the corresponding catenanes. This correlation does not apply, however, to electrophoretic migration of certain replication intermediates, believed up to now to represent the simplest torus-type catenanes. We propose, therefore, that freshly replicated circular DNA molecules, in addition to forming regular catenanes, may also form hemicatenanes.

Coevolution of adaptive technology, maladaptive culture and population size in a producer-scrounger game.

Technology (i.e. tools, methods of cultivation and domestication, systems of construction and appropriation, machines) has increased the vital rates of humans, and is one of the defining features of the transition from Malthusian ecological stagnation to a potentially perpetual rising population growth. Maladaptations, on the other hand, encompass behaviours, customs and practices that decrease the vital rates of individuals. Technology and maladaptations are part of the total stock of culture carried by the individuals in a population. Here, we develop a quantitative model for the coevolution of cumulative adaptive technology and maladaptive culture in a 'producer-scrounger' game, which can also usefully be interpreted as an 'individual-social' learner interaction. Producers (individual learners) are assumed to invent new adaptations and maladaptations by trial-and-error learning, insight or deduction, and they pay the cost of innovation. Scroungers (social learners) are assumed to copy or imitate (cultural transmission) both the adaptations and maladaptations generated by producers. We show that the coevolutionary dynamics of producers and scroungers in the presence of cultural transmission can have a variety of effects on population carrying capacity. From stable polymorphism, where scroungers bring an advantage to the population (increase in carrying capacity), to periodic cycling, where scroungers decrease carrying capacity, we find that selection-driven cultural innovation and transmission may send a population on the path of indefinite growth or to extinction.

Dipeptidyl peptidase IV and its inhibitors: therapeutics for type 2 diabetes and what else?

The proline-specific dipeptidyl aminopeptidase IV (DPP IV, DPP-4, CD26), widely expressed in mammalians, releases X-Pro/Ala dipeptides from the N-terminus of peptides. DPP IV is responsible of the degradation of the incretin peptide hormones regulating blood glucose levels. Several families of DPP IV inhibitors have been synthesized and evaluated. Their positive effects on the degradation of the incretins and the control of blood glucose levels have been demonstrated in biological models and in clinical trials. Presently, several DPP IV inhibitors, the "gliptins", are approved for type 2 diabetes or are under clinical evaluation. However, the gliptins may also be of therapeutic interest for other diseases beyond the inhibition of incretin degradation. In this Perspective, the biological functions and potential substrates of DPP IV enzymes are reviewed and the characteristics of the DPP IV inhibitors are discussed in view of type 2 diabetes and further therapeutic interest.

Chromatin domain boundaries delimited by a histone-binding protein in yeast.

When located next to chromosomal elements such as telomeres, genes can be subjected to epigenetic silencing. In yeast, this is mediated by the propagation of the SIR proteins from telomeres toward more centromeric regions. Particular transcription factors can protect downstream genes from silencing when tethered between the gene and the telomere, and they may thus act as chromatin domain boundaries. Here we have studied one such transcription factor, CTF-1, that binds directly histone H3. A deletion mutagenesis localized the barrier activity to the CTF-1 histone-binding domain. A saturating point mutagenesis of this domain identified several amino acid substitutions that similarly inhibited the boundary and histone binding activities. Chromatin immunoprecipitation experiments indicated that the barrier protein efficiently prevents the spreading of SIR proteins, and that it separates domains of hypoacetylated and hyperacetylated histones. Together, these results suggest a mechanism by which proteins such as CTF-1 may interact directly with histone H3 to prevent the propagation of a silent chromatin structure, thereby defining boundaries of permissive and silent chromatin domains.

Stochastic stability and the evolution of coordination in spatially structured populations.

Animals can often coordinate their actions to achieve mutually beneficial outcomes. However, this can result in a social dilemma when uncertainty about the behavior of partners creates multiple fitness peaks. Strategies that minimize risk ("risk dominant") instead of maximizing reward ("payoff dominant") are favored in economic models when individuals learn behaviors that increase their payoffs. Specifically, such strategies are shown to be "stochastically stable" (a refinement of evolutionary stability). Here, we extend the notion of stochastic stability to biological models of continuous phenotypes at a mutation-selection-drift balance. This allows us to make a unique prediction for long-term evolution in games with multiple equilibria. We show how genetic relatedness due to limited dispersal and scaled to account for local competition can crucially affect the stochastically-stable outcome of coordination games. We find that positive relatedness (weak local competition) increases the chance the payoff dominant strategy is stochastically stable, even when it is not risk dominant. Conversely, negative relatedness (strong local competition) increases the chance that strategies evolve that are neither payoff nor risk dominant. Extending our results to large multiplayer coordination games we find that negative relatedness can create competition so extreme that the game effectively changes to a hawk-dove game and a stochastically stable polymorphism between the alternative strategies evolves. These results demonstrate the usefulness of stochastic stability in characterizing long-term evolution of continuous phenotypes: the outcomes of multiplayer games can be reduced to the generic equilibria of two-player games and the effect of spatial structure can be analyzed readily.

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.

Estimating the basic reproductive number from viral sequence data.

Epidemiological processes leave a fingerprint in the pattern of genetic structure of virus populations. Here, we provide a new method to infer epidemiological parameters directly from viral sequence data. The method is based on phylogenetic analysis using a birth-death model (BDM) rather than the commonly used coalescent as the model for the epidemiological transmission of the pathogen. Using the BDM has the advantage that transmission and death rates are estimated independently and therefore enables for the first time the estimation of the basic reproductive number of the pathogen using only sequence data, without further assumptions like the average duration of infection. We apply the method to genetic data of the HIV-1 epidemic in Switzerland.