Weighted Evolving Networks

Many biological, ecological and economic systems are best described by weighted networks, as the nodes interact with each other with varying strength. However, most network models studied so far are binary, the link strength being either 0 or 1. In this paper we introduce and investigate the scaling properties of a class of models which assign weights to the links as the network evolves. The combined numerical and analytical approach indicates that asymptotically the total weight distribution converges to the scaling behavior of the connectivity distribution, but this convergence is hampered by strong logarithmic corrections.Comment: 5 pages, 3 figure

Degeneracy: a link between evolvability, robustness and complexity in biological systems

A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology. This paper offers a new perspective on the mechanics of evolution and the origins of complexity, robustness, and evolvability. Here we explore the hypothesis that degeneracy, a partial overlap in the functioning of multi-functional components, plays a central role in the evolution and robustness of complex forms. In support of this hypothesis, we present evidence that degeneracy is a fundamental source of robustness, it is intimately tied to multi-scaled complexity, and it establishes conditions that are necessary for system evolvability

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

Detecting the influence of initial pioneers on succession at deep-sea vents

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS One 7 (2012): e50015, doi:10.1371/journal.pone.0050015.Deep-sea hydrothermal vents are subject to major disturbances that alter the physical and chemical environment and eradicate the resident faunal communities. Vent fields are isolated by uninhabitable deep seafloor, so recolonization via dispersal of planktonic larvae is critical for persistence of populations. We monitored colonization near 9°50′N on the East Pacific Rise following a catastrophic eruption in order to address questions of the relative contributions of pioneer colonists and environmental change to variation in species composition, and the role of pioneers at the disturbed site in altering community structure elsewhere in the region. Pioneer colonists included two gastropod species: Ctenopelta porifera, which was new to the vent field, and Lepetodrilus tevnianus, which had been rare before the eruption but persisted in high abundance afterward, delaying and possibly out-competing the ubiquitous pre-eruption congener L. elevatus. A decrease in abundance of C. porifera over time, and the arrival of later species, corresponded to a decrease in vent fluid flow and in the sulfide to temperature ratio. For some species these successional changes were likely due to habitat requirements, but other species persisted (L. tevnianus) or arrived (L. elevatus) in patterns unrelated to their habitat preferences. After two years, disturbed communities had started to resemble pre-eruption ones, but were lower in diversity. When compared to a prior (1991) eruption, the succession of foundation species (tubeworms and mussels) appeared to be delayed, even though habitat chemistry became similar to the pre-eruption state more quickly. Surprisingly, a nearby community that had not been disturbed by the eruption was invaded by the pioneers, possibly after they became established in the disturbed vents. These results indicate that the post-eruption arrival of species from remote locales had a strong and persistent effect on communities at both disturbed and undisturbed vents.The authors received funding from National Science Foundation grant OCE-0424953, WHOI Deep Ocean Exploration Institute, WHOI Summer Student Fellow program, Woods Hole Partnership in Education Program, IFREMER and CNRS, Fondation TOTAL Chair Extreme Marine Environment, Biodiversity and Global change

Googling Food Webs: Can an Eigenvector Measure Species' Importance for Coextinctions?

A major challenge in ecology is forecasting the effects of species' extinctions, a pressing problem given current human impacts on the planet. Consequences of species losses such as secondary extinctions are difficult to forecast because species are not isolated, but interact instead in a complex network of ecological relationships. Because of their mutual dependence, the loss of a single species can cascade in multiple coextinctions. Here we show that an algorithm adapted from the one Google uses to rank web-pages can order species according to their importance for coextinctions, providing the sequence of losses that results in the fastest collapse of the network. Moreover, we use the algorithm to bridge the gap between qualitative (who eats whom) and quantitative (at what rate) descriptions of food webs. We show that our simple algorithm finds the best possible solution for the problem of assigning importance from the perspective of secondary extinctions in all analyzed networks. Our approach relies on network structure, but applies regardless of the specific dynamical model of species' interactions, because it identifies the subset of coextinctions common to all possible models, those that will happen with certainty given the complete loss of prey of a given predator. Results show that previous measures of importance based on the concept of “hubs” or number of connections, as well as centrality measures, do not identify the most effective extinction sequence. The proposed algorithm provides a basis for further developments in the analysis of extinction risk in ecosystems

The Effect of Consumers and Mutualists of Vaccinium membranaceum at Mount St. Helens: Dependence on Successional Context

In contrast to secondary succession, studies of terrestrial primary succession largely ignore the role of biotic interactions, other than plant facilitation and competition, despite the expectation that simplified interaction webs and propagule-dependent demographics may amplify the effects of consumers and mutualists. We investigated whether successional context determined the impact of consumers and mutualists by quantifying their effects on reproduction by the shrub Vaccinium membranaceum in primary and secondary successional sites at Mount St. Helens (Washington, USA), and used simulations to explore the effects of these interactions on colonization. Species interactions differed substantially between sites, and the combined effect of consumers and mutualists was much more strongly negative for primary successional plants. Because greater local control of propagule pressure is expected to increase successional rates, we evaluated the role of dispersal in the context of these interactions. Our simulations showed that even a small local seed source greatly increases population growth rates, thereby balancing strong consumer pressure. The prevalence of strong negative interactions in the primary successional site is a reminder that successional communities will not exhibit the distribution of interaction strengths characteristic of stable communities, and suggests the potential utility of modeling succession as the consequence of interaction strengths

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

BACKGROUND: Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments. METHODS: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. RESULTS: Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). CONCLUSIONS: These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy

Preclinical rationale for entinostat in embryonal rhabdomyosarcoma

BACKGROUND: Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in the pediatric cancer population. Survival among metastatic RMS patients has remained dismal yet unimproved for years. We previously identified the class I-specific histone deacetylase inhibitor, entinostat (ENT), as a pharmacological agent that transcriptionally suppresses the PAX3:FOXO1 tumor-initiating fusion gene found in alveolar rhabdomyosarcoma (aRMS), and we further investigated the mechanism by which ENT suppresses PAX3:FOXO1 oncogene and demonstrated the preclinical efficacy of ENT in RMS orthotopic allograft and patient-derived xenograft (PDX) models. In this study, we investigated whether ENT also has antitumor activity in fusion-negative eRMS orthotopic allografts and PDX models either as a single agent or in combination with vincristine (VCR). METHODS: We tested the efficacy of ENT and VCR as single agents and in combination in orthotopic allograft and PDX mouse models of eRMS. We then performed CRISPR screening to identify which HDAC among the class I HDACs is responsible for tumor growth inhibition in eRMS. To analyze whether ENT treatment as a single agent or in combination with VCR induces myogenic differentiation, we performed hematoxylin and eosin (H&E) staining in tumors. RESULTS: ENT in combination with the chemotherapy VCR has synergistic antitumor activity in a subset of fusion-negative eRMS in orthotopic "allografts," although PDX mouse models were too hypersensitive to the VCR dose used to detect synergy. Mechanistic studies involving CRISPR suggest that HDAC3 inhibition is the primary mechanism of cell-autonomous cytoreduction in eRMS. Following cytoreduction in vivo, residual tumor cells in the allograft models treated with chemotherapy undergo a dramatic, entinostat-induced (70-100%) conversion to non-proliferative rhabdomyoblasts. CONCLUSION: Our results suggest that the targeting class I HDACs may provide a therapeutic benefit for selected patients with eRMS. ENT's preclinical in vivo efficacy makes ENT a rational drug candidate in a phase II clinical trial for eRMS

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks