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

European patterns of local adaptation planning - a regional analysis

While European regions face a range of different climate hazards, little is known about how these differences affect local climate adaptation planning. We present an analytical framework for evaluating local climate adaptation plans (LCAPs) and apply it to 327 cities in 28 countries across different European regions. To do this, we use statistical methods to identify regional clusters based on overall plan quality, impacts, vulnerable population groups, and sectors addressed by LCAPs. By comparing both geographic and statistical clusters, we found (1) significant spatial heterogeneity across European cities but (2) higher average plan quality scores and more consistent strategies across cities in Central and Eastern Europe. Notably, we found no regional differences regarding (a) the climate impacts and vulnerable communities identified in plans: (b) the most commonly-addressed impacts, which were urban temperature and changing precipitation patterns; and (c) the residents that cities identified as most vulnerable, namely older people, women, infants, and the sick. Our study provides a spatial analysis of European LCAPs to uncover regional policy perspectives on local climate adaptation issues. Such approaches can effectively inform broader EU, national and regional strategies that aim to support local adaptation planning in a context of multilevel governance

Efficient derivation of NPCs, spinal motor neurons and midbrain dopaminergic neurons from hESCs at 3% oxygen

This protocol has been designed to generate neural precursor cells (NPCs) from human embryonic stem cells (hESCs) using a physiological oxygen (O(2)) level of 3% and chemically defined conditions. The first stage involves suspension culture of hESC colonies at 3% O(2), where they acquire a neuroepithelial identity over two weeks. This timescale is comparable to that at 20% O(2), but survival is enhanced. Sequential application of retinoic acid (RA) and purmorphamine (PM), from day 14 to 28, directs differentiation towards spinal motor neurons. Alternatively, addition of FGF-8 and PM generates midbrain dopaminergic neurons. OLIG2 induction in motor neuron precursors is 2-fold greater than at 20% O(2), whereas EN1 is 5-fold enhanced. 3% NPCs can be differentiated into all three neural lineages, and such cultures can be maintained long-term in the absence of neurotrophins. The ability to generate defined cell types at 3% O(2) should represent a significant advance for in vitro disease modelling and potentially cell-based therapies

A Patient-Specific in silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury

The development of personalized medicine is a primary objective of the medical community and increasingly also of funding and registration agencies. Modeling is generally perceived as a key enabling tool to target this goal. Agent-Based Models (ABMs) have previously been used to simulate inflammation at various scales up to the whole-organism level. We extended this approach to the case of a novel, patient-specific ABM that we generated for vocal fold inflammation, with the ultimate goal of identifying individually optimized treatments. ABM simulations reproduced trajectories of inflammatory mediators in laryngeal secretions of individuals subjected to experimental phonotrauma up to 4 hrs post-injury, and predicted the levels of inflammatory mediators 24 hrs post-injury. Subject-specific simulations also predicted different outcomes from behavioral treatment regimens to which subjects had not been exposed. We propose that this translational application of computational modeling could be used to design patient-specific therapies for the larynx, and will serve as a paradigm for future extension to other clinical domains

Construction of a large scale integrated map of macrophage pathogen recognition and effector systems

<p>Abstract</p> <p>Background</p> <p>In an effort to better understand the molecular networks that underpin macrophage activation we have been assembling a map of relevant pathways. Manual curation of the published literature was carried out in order to define the components of these pathways and the interactions between them. This information has been assembled into a large integrated directional network and represented graphically using the modified Edinburgh Pathway Notation (mEPN) scheme.</p> <p>Results</p> <p>The diagram includes detailed views of the toll-like receptor (TLR) pathways, other pathogen recognition systems, NF-kappa-B, apoptosis, interferon signalling, MAP-kinase cascades, MHC antigen presentation and proteasome assembly, as well as selected views of the transcriptional networks they regulate. The integrated pathway includes a total of 496 unique proteins, the complexes formed between them and the processes in which they are involved. This produces a network of 2,170 nodes connected by 2,553 edges.</p> <p>Conclusions</p> <p>The pathway diagram is a navigable visual aid for displaying a consensus view of the pathway information available for these systems. It is also a valuable resource for computational modelling and aid in the interpretation of functional genomics data. We envisage that this work will be of value to those interested in macrophage biology and also contribute to the ongoing Systems Biology community effort to develop a standard notation scheme for the graphical representation of biological pathways.</p

BowTieBuilder: modeling signal transduction pathways

<p>Abstract</p> <p>Background</p> <p>Sensory proteins react to changing environmental conditions by transducing signals into the cell. These signals are integrated into core proteins that activate downstream target proteins such as transcription factors (TFs). This structure is referred to as a bow tie, and allows cells to respond appropriately to complex environmental conditions. Understanding this cellular processing of information, from sensory proteins (e.g., cell-surface proteins) to target proteins (e.g., TFs) is important, yet for many processes the signaling pathways remain unknown.</p> <p>Results</p> <p>Here, we present BowTieBuilder for inferring signal transduction pathways from multiple source and target proteins. Given protein-protein interaction (PPI) data signaling pathways are assembled without knowledge of the intermediate signaling proteins while maximizing the overall probability of the pathway. To assess the inference quality, BowTieBuilder and three alternative heuristics are applied to several pathways, and the resulting pathways are compared to reference pathways taken from KEGG. In addition, BowTieBuilder is used to infer a signaling pathway of the innate immune response in humans and a signaling pathway that potentially regulates an underlying gene regulatory network.</p> <p>Conclusion</p> <p>We show that BowTieBuilder, given multiple source and/or target proteins, infers pathways with satisfactory recall and precision rates and detects the core proteins of each pathway.</p