Complex systems: Network thinking

Information plays a critical role in complex biological systems. Complex systems like immune systems and ant colonies co-ordinate heterogeneous components in a decentralized fashion. How do these distributed decentralized systems function? One key component is how these complex systems efficiently process information. These complex systems have an architecture for integrating and processing information coming in from various sources and points to the value of information in the functioning of different complex biological systems. This article proposes a role for information processing in questions around the origin of life and suggests how computational simulations may yield insights into questions related to the origin of life. Such a computational model of the origin of life would unify thermodynamics with information processing and we would gain an appreciation of why proteins and nucleotides evolved as the substrate of computation and information processing in living systems that we see on Earth. Answers to questions like these may give us insights into non-carbon based forms of life that we could search for outside Earth. We hypothesize that carbon-based life forms are only one amongst a continuum of systems in the universe. Investigations into the role of computational substrates that allow information processing is important and could yield insights into: 1) novel non-carbon based computational substrates that may have life-like properties, and 2) how life may have actually originated from non-life on Earth. Life may exist as a continuum between non-life and life and we may have to revise our notion of life and how common it is in the universe. Looking at life or phenomenon through the lens of information theory may yield a broader view of life.

Modem day network-centric computing can increasingly be viewed as a vast, extremely involved organism, of which the boundaries are not clear, and most of the constituent parts are unknown from any given viewpoint. It may even become impossible to ensure the security of computing systems in future with current approaches to computer security. On the other hand, nature has been successful in defending its complex biological systems from infection and damage for countless millennia by using highly specialized and evolved immune systems. It is therefore postulated that a highly effective defensive mechanism can be developed, to transparently enforce an acceptable level of security in very extensive and complex computer networks and systems, by building very basic, but specialized autonomous agents, that follow basic rules that can be deduced from biological immune systems. Key to this concept is the biological system’s ability to distinguish what belongs to it and what is foreign and therefore needs to be destroyed. This is done, inter alia, via genetic information contained in the DNA of each cell. Central to the proposed immune model is thus the concept of ‘DNA-proofing’

Despite significant effort on understanding complex biological systems, we lack a unified theory for modeling, inference, analysis, and efficient control of their dynamics in uncertain environments. These problems are made even more challenging when considering that only limited and noisy information is accessible for modeling, which can prove insufficient for explaining, and predicting the behavior of complex systems. For instance, missing information hampers the capabilities of analytical tools to uncover the true degrees of freedom and infer the model structure and parameters of complex biological systems. Toward this end, in this paper, we discuss several important mathematical challenges that could open new theoretical avenues in studying complex systems: (1) By understanding the universal laws characterizing the asymmetric statistics of magnitude increments and the complex space-time interdependency within one process and across many processes, we can develop a class of compact yet accurate mathematical models capable to potentially providing higher degree of predictability, and more efficient control strategies. (2) In order to better predict the onset of disease and their root cause, as well as potentially discover more efficient quality-of-life (QoL)-control strategies, we need to develop mathematical strategies that not only are capable to discover causal interactions and their corresponding mathematical expressions for space and time operators acting on biological processes, but also mathematical and algorithmic techniques to identify the number of unknown unknowns (UUs) and their interdependency with the observed variables. (3) Lastly, to improve the QoL of control strategies when facing intra- and inter-patient variability, the focus should not only be on specific values and ranges for biological processes, but also on optimizing/controlling knob variables that enforce a specific spatiotemporal multifractal behavior that corresponds to an initial healthy (patient specific) behavior. All in all, the modeling, analysis and control of complex biological collective systems requires a deeper understanding of the multifractal properties of high dimensional heterogeneous and noisy data streams and new algorithmic tools that exploit geometric, statistical physics, and information theoretic concepts to deal with these data challenges.

This article summarizes a network and complex systems science model for research on whole systems of complementary and alternative medicine (CAM) such as homeopathy and traditional Chinese medicine. The holistic concepts of networks and nonlinear dynamical complex systems are well matched to the global and interactive perspectives of whole systems of CAM, whereas the reductionistic science model is well matched to the isolated local organ, cell, and molecular mechanistic perspectives of pharmaceutically based biomedicine. Whole systems of CAM are not drugs with specific actions. The diagnostic and therapeutic approaches of whole systems of CAM produce effects that involve global and patterned shifts across multiple subsystems of the person as a whole. For homeopathy, several characteristics of complex systems, including the probabilistic nature of attractor patterns, variable sensitivity of complex systems to initial conditions, and emergent behaviors in the evolution of a system in its full environmental context over time, could help account for the mixed basic science and controlled clinical trial research findings, in contrast with the consistently positive outcomes of observational studies in the literature. Application of theories and methods from complex systems and network science can open a new era of advances in understanding factors that lead to good versus poor individual global outcome patterns and to rational triage of patients to one type of care over another. The growing reliance on complex systems thinking and systems biology for cancer research affords a unique opportunity to bridge between the CAM and conventional medical worlds with some common language and conceptual models.

Organoids are three-dimensional structures derived from stem cells that mimic the organization and function of specific organs, making them valuable tools for studying complex systems in biology. This paper explores the application of complex systems theory to understand and characterize organoids as exemplars of intricate biological systems. By identifying and analyzing common design principles observed across diverse natural, technological, and social complex systems, we can gain insights into the underlying mechanisms governing organoid behavior and function. This review outlines general design principles found in complex systems and demonstrates how these principles manifest within organoids. By acknowledging organoids as representations of complex systems, we can illuminate our understanding of their normal physiological behavior and gain valuable insights into the alterations that can lead to disease. Therefore, incorporating complex systems theory into the study of organoids may foster novel perspectives in biology and pave the way for new avenues of research and therapeutic interventions to improve human health and wellbeing.

Over the past five to ten years we have witnessed a revolution in biological research which is fueled by our increasing capacity to decipher biological information of three types: the digital information embedded in DNA with its four-letter alphabet; the three-dimensional information represented by proteins, the major executors of biological function; and the four-dimensional information of complex biological systems and networks representing the temporal and spatial interaction of multiple components. The analysis of complex systems and networks in their entirety is essential for scientists to understand the molecular basis of fascinating processes such as growth, development, and differentiation, and to extract the biological meaning of emergent properties such as consciousness, memory, and the ability to learn which evolved over billions of years. Not surprisingly, the linear nucleotide sequence in DNA has been the first type of information to be deciphered. Large-scale genetic mapping and DNA sequencing rapidly generate enormous amounts of biological information. In many ways, programs such as the human genome project represent some of the earliest attempts to understand biological complexity. Successful completion of the human genome and similar projects will pose the challenge of interpreting the information contained in billions of nucleotides and of explaining how the interplay of the products of perhaps 100,000 genes results in the myriad of biological phenotypes. Meeting these challenges will require new interdisciplinary strategies which draw from the expertise of scientists from disciplines as different as molecular biology, biochemistry, engineering, chemistry, applied mathematics, and computer science. Protein science is central to such integrated strategies. Separation of proteins from complex mixtures, followed by structural and functional analyses, control of protein function, post-translational processing, and modification and formation of macromolecular complexes of proteins and other biomolecules are but a few examples of topics which are essential for all those scientists who attempt to interpret the linear DNA sequence in terms of the three- and four-dimensional information of complex biological systems and networks. The following chapters detail the necessary protocols for this endeavor, provided by experts in experimental protein science. Two attractive features set this manual apart from other collections of research protocols. First, Current Protocols in Protein Science will be continuously updated and expanded by quarterly additions to the core edition. Secondly, this volume as well as the updates are also available on CD-ROM. Numerous cross-references within the manual by hypertext links, context based searching, and provisions for making individualized notebooks containing frequently used protocols are attractions of the CD-ROM version which are in tune with the increasing dependence on computers in biological research laboratories. Protein science is a diverse, experimentally challenging, and rapidly evolving discipline. This book provides expert guidance in experiment design and execution and promises to remain up-to-date, in contents and in format, for many years to come.

Synthetic biology is an emerging field blending approaches and concepts derived from classic engineering disciplines with modern biological approaches. Concepts of modularity and orthogonality, i.e. the transfer of simple building blocks between unrelated chassis (host organisms), are guiding principles for the design and construction of artificial biological systems, which in their ultimate implementation can be artificial organisms. Synthetic biology is not only leading the way towards the engineering of useful organisms that serve human purposes, it is also a new way of approaching basic scientific questions to understand complex biological systems. The classic reductionist methodology by which scientists have dissected complex systems to understand their properties through understanding the functionality of isolated components, finds its counterpart in synthetic biology. If we can build complex biological processes, systems, and ultimately organisms from simple, fully understood functional modules using a set of defined rules, we must fully understand the system. At first this approach may sound almost naïve as with near certainty scientists will encounter spectacular 'failures' on the way to building complex biological systems. Undoubtedly, the result of synthetic biology efforts will be more than the sum of the individual components giving rise to complex systems with novel emergent properties, many of which are unexpected or even undesired. However, the process of learning from those 'failures' often through predictive modeling and simulation studies in parallel to the actual assembly and testing of artificial biological systems, will lead to novel insights into the function of complex biological systems in general. Plant and algal cells are complex with their extra organelle, the plastid, and are highly sophisticated in their metabolism enabling them to convert light, CO2 and minerals into the building blocks of cells, produce all oxygen in the atmosphere, thousands of specialized chemicals including drugs, and energy-rich compounds that fuel life on earth. While engineers have been dabbling for many years in the redesign of bacterial and yeast chassis with novel properties, the application of synthetic biology to photosynthetic organisms is just beginning. Therefore, it seems timely to provide an overview of the state of the art of 'Synthetic Biology for Basic and Applied Plant Research' in this special issue of The Plant Journal. Next Generation Sequencing has given us a nearly unlimited number of genomic blueprints for photosynthetic bacteria, algae and plants and this provides the raw material for synthetic biology. Tools for recombining of genes and introducing them into an increasing number of photosynthetic chassis including organelles such as chloroplasts, are available and no longer an impediment to the application of synthetic biology to plants. One revolutionary technique, the introduction of the CRISPR/CAS system for genome editing is now being applied to edit not only the plant genome, but also the transcriptome and epigenome as discussed by Puchta (2016). Bacterial microcompartments, first discovered as carboxysomes in cyanobacteria, provide an important platform for the engineering of synthetic modules. They can encapsulate enzymes, concentrate substrates, and help in the avoidance of toxic products as Gonzalez-Esquer et al. (2016) describe. Cyanobacteria address one key problem that all photosynthetic organisms encounter, the natural inefficiency of the carbon-fixing enzyme RubBisCO, by encapsulating this enzyme in carboxysomes, which increases the local concentration of CO2 around the enzyme. Plants do not have a carboxysome-based carbon concentration mechanism to overcome the limitation of photosynthesis through RubBisCO's inefficiency. The solution could be to introduce this bacterial microcompartment into chloroplasts of crop plants and synthetic biology efforts towards this aim are well under way as described by Hanson et al. (2016). A subset of plants has evolved their own way of overcoming this problem by prefixing carbon using a more efficient enzyme than RubBisCO. This carbon concentration mechanism requires the compartmentalization of different sets of enzymes in different cells of the leaf, and this overall approach is referred to as C4-syndrome of C4 plants, because the CO2 is first fixed into a four-carbon compound rather than the three-carbon compound produced first by RuBisCO in C3 plants. Some of the important crop plants that feed the world are C4 plants, such as maize, but many are not, including wheat and rice. The solution is to engineer C4 photosynthesis in a C3 chassis and as Schuler et al. (2016) describe, efforts are well underway by applying synthetic biology. Introduction of orthogonal biosynthetic pathways into photosynthetic organelles and bacteria to enhance their synthetic repertoires requires a deep knowledge of the regulation of photosynthesis, as the balance of ATP/and NADPH and the nature of the carbon sink are critical for the efficiency of photosynthesis. Nielson and coworkers describe how optimization of carbon flux and reductant are critical elements in engineering cyanobacteria and chloroplasts to sustainably produce novel chemicals (Nielsen et al., 2015). Plants are capable of making a seemingly unlimited number of specialized compounds to defend themselves against pathogens or herbivores and many of these compounds have been used by humans for thousands of years, e.g. as drugs. One particular compound class, the terpenoids, provides an example of the amazing natural combinatorial chemistry that plants are capable of. Applying synthetic biology principles of modularity and orthogonality, plant engineers are now capable of recombining different modules of terpenoid biosynthesis from different sources into new chassis to engineer plants that produce new-to-nature compounds as Arendt et al. (2016) describe. Another spectacular success in recombining modules of genes derived from different plants, algae, and fungi into a new chassis, the industrial crop Camelina, is the production of oils with a near natural composition of healthy oils found in fish as summarized by Haslam et al. (2016). With this accomplishment, important sustainability and human health questions can be addressed. These include improving the sustainability of the aquaculture industry for the production of fish rich in omega-3 oils with well-known health benefits when part of the human diet. Another example of addressing pressing problems for humankind is the generation of sustainable feed-stocks for energy production, independent of fossil fuels. For this reason, many scientists are currently pursuing the engineering of dedicated biofuel crops through the application of synthetic biology principles as summarized by Shih et al. (2016). Plant signaling pathways are highly interconnected and redundant, and hence often hard to dissect using the classical reductionistic approaches. Synthetic Biology offers a new way to explore individual signaling pathways by reassembling them bottom up from modules in non-interfering backgrounds of new chassis. Braguy and Zurbriggen (2016) describe this approach in detail. Ultimately, understanding how signaling pathways feed into programmable plant genetic circuits will be essential for the engineering of plants to be more efficient or to produce novel compounds. Medford and Prasad (2016) explain how genetic parts such as promoters and other regulatory elements can be tested and their assembly into genetic circuits simulated. The list of examples and approaches described in this special issue of The Plant Journal is comprehensive. Our intention is that this special issue will explain key principles and areas of plant synthetic biology to guide the reader and future contributors of The Plant Journal in embracing these approaches for both fundamental and applied plant science. Other areas of interest not covered here include synthetic consortia, the synthetic interaction of photosynthetic and heterotrophic organisms beyond naturally occurring symbioses. As we learn to understand how the microbiome affects plant growth, synthetic biology approaches may be key in learning more about these complex interactions, a topic that certainly falls with in the scope of The Plant Journal. With the expansion of the current field of plant synthetic biology, The Plant Journal welcomes the submission of basic research papers applying synthetic biology to further our understanding of the full biological complexity of photosynthetic organisms and their complex biotic and abiotic interaction with the environment.

Complexity science is an emerging interdisciplinary research field that has been grabbing a great deal of attention over the past few decades. The field mainly studies collective dynamics of complex systems and networks emerging from interactions among their interconnected components. Such complex systems and networks are known to be ubiquitous in a huge variety of areas ranging from biology, neurology, health, and medicine to sociology, economics, and communication networks, etc. The rise of modern network sciences and the advances in studies on dynamical processes on complex networks and systems have provided the insightful understanding and novel approaches to computational sociology. Such studies pose interesting questions whose solutions may make impacts on economics, sociology, and politics, etc. By basing opinion dynamics on mathematical models, the existing studies, to a significant extent, have exhibited observations resembling real-life phenomena and revealed key factors playing important roles in the construction of social structures and the formation of public opinions. In this thesis, four problems on dynamics of opinion formation have been investigated which, to the best of our knowledge, have been largely missed in existing studies. The four problems include: 1. In the modern world, people may be active on multiple social networks such as Reddit, Twitter or Facebook. As a result, single social networks have evolved to multiplex networks. Understanding the dynamics of opinion formation on multiplex networks is of both research interest and application values. We considered the rules proposed in the bounded confidence opinion dynamics models and examine the effects of the interplay between layers of a multiplex network on the evolution of public opinions governed by such rules. The results show that the interactions of individuals on multiple different layers of a multiplex network may diminish or enhance the opinion diversity depending on the tolerance threshold of each layer. 2. Majority rule is one of the most popular tendencies in human behaviors in choosing either of two alternatives. Following the classical majority rule, one tends to adopt the opinion shared by the majority of the connections s/he has. Under such a regime, it is shown in most of the existing studies that a complete consensus is achieved across the population in relatively dense networks. However, we figure out that in sparse networks, the dynamics may be very different where multiple steady states of co-existence could emerge. Moreover, we examine a modified majority rule where different social influences of different individuals are taken into account. It is revealed that under such a rule, once again multi-steady state of coexistence could emerge in sparse networks, yet due to very different reasons from those for the case under the classic majority rule. 3. Classical bounded-confidence models mostly deal with pairwise interactions where the two involving agents have equal influences on each other. In real life, human contacts may be more complex and sophisticated. We examine opinion dynamics under the effects of bias in social interactions. Theoretical and simulation results show that the unbalance in interpersonal contacts may lead to macroscopic polarization and/or the emergence of extremism in the entire opinion system. Influences of a few other factors on the emergence and prevalence of extremism are also discussed. 4. Most of existing opinion dynamics models aim to reveal influences of a certain key factor in opinion formation. Dynamics of opinion formation under the interplay of multiple social rules and principles are largely unknown. We examine a simple model where individuals interact with each other under the influences of the two rules of interpersonal consensus making and majority orientation. We show that some interesting and complex system dynamics shall then emerge. Our contributions as listed above shall help provide deeper insights into opinion formation and evolution in human societies. A few directions for future research are also discussed.

Hospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Nevertheless, metrics of performance have to date focused on the performance of individual elements rather than the system as a whole. Ma- nipulation of individual elements of a complex system without an integrative understanding of its function is undesirable and may lead to counter-intuitive outcomes and a holistic metric of hospital function might help design more efficient services. We aimed to charac- terise the system of peri-operative care for emergency surgical admissions in our tertiary care hospital using network analysis. We used retrospective electronic health record data to construct a weighted directional network of the system. For this we selected all unplanned admissions during a 3.5 year period involving a surgical intervention during the inpatient stay and obtained a set of 16,500 individual inpatient episodes. We then constructed and analysed the structure of this network using established methods from network science such as degree distribution, betweenness centrality and small-world characteristics. The analysis showed the service to be a complex system with scale-free, small-world network properties. This finding has implications for the structure and resilience of the service as such networks, whilst being robust in general, may be vulnerable to outages at specific key nodes. We also identified such potential hubs and bottlenecks in the system based on a variety of network measures. It is hoped that such a holistic, system-wide description of a hospital service may provide better metrics for hospital strain and serve to help planners engineer systems that are as robust as possible to external shocks.

Despite its current popularity, “emergence” is a concept with a venerable history and an elusive, ambiguous standing in contemporary evolutionary theory. This paper briefly recounts the history of the term and details some of its current usages. Not only are there radically varying interpretations about what emergence means but “reductionist” and “holistic” theorists have very different views about the issue of causation. However, these two seemingly polar positions are not irreconcilable. Reductionism, or detailed analysis of the parts and their interactions, is essential for answering the “how” question in evolution -how does a complex living system work? But holism is equally necessary for answering the “why” question -why did a particular arrangement of parts evolve? In order to answer the “why” question, a broader, multi-leveled paradigm is required. The reductionist approach to explaining emergent complexity has entailed a search for underlying “laws of emergence.” Another alternative is the “Synergism Hypothesis,” which focuses on the “economics” – the functional effects produced by emergent wholes and their selective consequences. This theory, in a nutshell, proposes that the synergistic (co-operative) effects produced by various combinations of parts have played a major causal role in the evolution of biological complexity. It will also be argued that emergent phenomena represent, in effect, a subset of a much larger universe of combined effects in the natural world; there are many different kinds of synergy, but not all synergies represent emergent phenomena.

Data based identification and prediction of nonlinear and complex dynamical systems

Complex networks have been used in several applications in the past few decades. Complex systems can be observed in applications such as transportation, energy systems, internet, biology, and logistics, among other possible *implementations*. In such structures, there may be agents exploring nodes and identifying new concepts and discovering the network; this kind of exploration is known as Knowledge Acquisition and it has been deeply researched for the past decades. When exploring a network, i.e., acquiring knowledge in it, the path explored by an agent can be seen as a sequence of visited nodes. In this context, this thesis allowed us to observe the behavior of different network dynamics and topologies when acquiring knowledge. Moreover, using machine learning techniques, we have proposed a framework that showed it is possible to recover the generating structures that constructed an unknown sequence. Finally, we have evaluated how global properties of a network are reflected in structures generated by sequences. Thus, we have presented an analysis whether local information of a network are biased or it is indeed a real picture of the network as a whole; this analysis allowed us to measure the impact of the sequences size while identifying networks properties. Hence, the results presented in this thesis have shown the behavior of different structures during the knowledge acquisition process. Lastly, we can highlight the framework built in this work, which allowed to classify which are the original topology and dynamics that generated a given sequence. Such results may enable several applications in network science, and pave the knowledge in this area. Among the main results, this work has allowed the proper identification of sequences generating structures from the properties obtained during the reconstruction of such sequences as a complex network; and, moreover, it was possible to observe that small sequences allow the identification of the structures with high accuracyComplex networks have been used in several applications in the past few decades. Complex systems can be observed in applications such as transportation, energy systems, internet, biology, and logistics, among other possible *implementations*. In such structures, there may be agents exploring nodes and identifying new concepts and discovering the network; this kind of exploration is known as Knowledge Acquisition and it has been deeply researched for the past decades. When exploring a network, i.e., acquiring knowledge in it, the path explored by an agent can be seen as a sequence of visited nodes. In this context, this thesis allowed us to observe the behavior of different network dynamics and topologies when acquiring knowledge. Moreover, using machine learning techniques, we have proposed a framework that showed it is possible to recover the generating structures that constructed an unknown sequence. Finally, we have evaluated how global properties of a network are reflected in structures generated by sequences. Thus, we have presented an analysis whether local information of a network are biased or it is indeed a real picture of the network as a whole; this analysis allowed us to measure the impact of the sequences size while identifying networks properties. Hence, the results presented in this thesis have shown the behavior of different structures during the knowledge acquisition process. Lastly, we can highlight the framework built in this work, which allowed to classify which are the original topology and dynamics that generated a given sequence. Such results may enable several applications in network science, and pave the knowledge in this area. Among the main results, this work has allowed the proper identification of sequences generating structures from the properties obtained during the reconstruction of such sequences as a complex network; and, moreover, it was possible to observe that small sequences allow the identification of the structures with high accuracy

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.

The role of diet and gut microbiota in the pathophysiology of neurodegenerative diseases, such as Alzheimer's, has recently come under intense investigation. Studies suggest that human gut microbiota may contribute to the modulation of several neurochemical and neurometabolic pathways, through complex systems that interact and interconnect with the central nervous system. The brain and intestine form a bidirectional communication axis, or vice versa, they form an axis through bi-directional communication between endocrine and complex immune systems, involving neurotransmitters and hormones. Above all, studies suggest that dysbiotic and poorly diversified microbiota may interfere with the synthesis and secretion of neurotrophic factors, such as brain-derived neurotrophic factor, gammaaminobutyric acid and N-methyl D-Aspartate receptors, widely associated with cognitive decline and dementia. In this context, the present article provides a review of the literature on the role of the gutbrain axis in Alzheimer's disease.

The following news item is taken in part from the November 26, 2010 issue of Science titled “From the Connectome to the Synaptome: An Epic Love Story,” by Javier DeFelipe. A major challenge in neuroscience is to decipher the structural layout of the brain. The term “connectome” has recently been proposed to refer to the highly organized connection matrix of the human brain. However, defining how information flows through such a complex system represents [an extremely] difficult (…) task (…). Circuit diagrams of the nervous system can be considered at different levels, although they are surely impossible to complete at the synaptic level. Nevertheless, advances in our capacity to marry macroscopic and microscopic data may help establish a realistic statistical model that could describe connectivity at the ultrastructural level, the “synaptome,” giving us cause for optimism. A link to this article can be found at http://dx.doi.org/10.1126/science.1193378. The following news item is taken in part from the October, 2010 issue of PLoS ONE titled “Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?,” by Konstantinos V. Katsikopoulos and Andrew J. King. Using a set of simple models, we present theoretical conditions (involving group size and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions—where individuals are able to consider the success of previous decision outcomes—the collective's aggregated information is almost always superior. A link to this article can be found at http://dx.doi.org/10.1371/journal.pone.0015505. The following news item is taken in part from the November 27, 2010 issue of arXiv titled “Networks and the Epidemiology of Infectious Disease,” by Leon Danon, Ashley P. Ford, Thomas House, Chris P. Jewell, Matt J. Keeling, Gareth O. Roberts, Joshua V. Ross, and Matthew C. Vernon. The science of networks has revolutionized research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here, we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterized; the statistical methods that can be applied to infer the epidemiological parameters on a realized network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. A link to this article can be found at http://arXiv.org/abs/1011.5950. The following news item is taken in part from the November 4, 2010 issue of PLoS Comput Biol titled “Infectious Disease Modeling of Social Contagion in Networks,” by Alison L. Hill, David G. Rand, Martin A. Nowak, and Nicholas A. Christakis. Information, trends, behaviors, and even health states may spread between contacts in a social network, similar to disease transmission. However, a major difference is that as well as being spread infectiously, it is possible to acquire this state spontaneously. For example, you can gain knowledge of a particular piece of information either by being told about it, or by discovering it yourself. In this article, we introduce a mathematical modeling framework that allows us to compare the dynamics of these social contagions to traditional infectious diseases. As an example, we study the spread of obesity. A link to this article can be found at http://dx.doi.org/10.1371/journal.pcbi.1000968. The following news item is taken in part from the November 18, 2010 issue of PLoS Comput Biol titled “Network Analysis of Global Influenza Spread,” by Joseph Chan, Antony Holmes, and Raul Rabadan. As evidenced by several historic vaccine failures, the design and implementation of the influenza vaccine remains an imperfect science. On a local scale, our technique can output the most likely origins of a virus circulating in a given location. On a global scale, we can pinpoint regions of the world that would maximally disrupt viral transmission with an increase in vaccine implementation. We demonstrate our method on seasonal H3N2 and H1N1 and foresee similar application to other seasonal viruses, including swine-origin H1N1, once more seasonal data are collected. A link to this article can be found at http://dx.doi.org/10.1371/journal.pcbi.1001005. The following news item is taken in part from the first issue of Cliodynamics: The Journal of Theoretical and Mathematical History titled “Cycling in the Complexity of Early Societies,” by Sergey Gavrilets, David G. Anderson, and Peter Turchin. Warfare is commonly viewed as a driving force of the process of aggregation of initially independent villages into larger and more complex political units that started several 1000 years ago and quickly lead to the appearance of chiefdoms, states, and empires. Here, we build on extensions and generalizations of Carneiro's (1970) argument to develop a spatially explicit agent-based model of the emergence of early complex societies via warfare. A general prediction of our model is continuous stochastic cycling in which the growth of individual polities in size, wealth/power, and complexity is interrupted by their quick collapse. A link to this article can be found at http://escholarship.org/uc/item/5536t55r. The following news item is taken in part from the November 19, 2010 issue of arXiv titled “Hierarchy and information in feedforward networks,” by Bernat Corominas-Murtra, Joaquín Goñi, Carlos Rodríguez-Caso, and Ricard Solé. In this article, we define a hierarchical index for feedforward structures taking, as the starting point, three fundamental concepts underlying hierarchy: order, predictability, and pyramidal structure. Our definition applies to the so-called causal graphs, that is, connected, directed acyclic graphs in which the arrows depict a direct causal relation between two elements defining the nodes. The estimator of hierarchy is obtained by evaluating the complexity of causal paths against the uncertainty in recovering them from a given end point. This naturally leads us to a definition of mutual information which, properly normalized and weighted through the layered structure of the graph, results in suitable index of hierarchy with strong theoretical grounds. A link to this article can be found at http://arXiv.org/abs/1011.4394. The following news item is taken in part from the December 13, 2010 issue of PNAS titled “There's Plenty of Time for Evolution,” by Herbert S. Wilf and Warren J. Ewens. Objections to Darwinian evolution are often based on the time required to carry out the necessary mutations. Seemingly, exponential numbers of mutations are needed. We show that such estimates ignore the effects of natural selection, and that the numbers of necessary mutations are thereby reduced to about K log L, rather than KL, where L is the length of the genomic “word,” and K is the number of possible “letters” that can occupy any position in the word. The required theory makes contact with the theory of radix-exchange sorting in theoretical computer science and the asymptotic analysis of certain sums that occur there. A link to this article can be found at http://dx.doi.org/10.1073/pnas.1016207107. The following news item is taken in part from the Online First articles of Theory in Biosciences titled “Mathematical Modeling of Evolution. Solved and Open Problems,” by Peter Schuster. Evolution is a highly complex multilevel process and mathematical modeling of evolutionary phenomenon requires proper abstraction and radical reduction to essential features. Examples are natural selection, Mendel's laws of inheritance, optimization by mutation and selection, and neutral evolution. An attempt is made to describe the roots of evolutionary theory in mathematical terms. A link to this article can be found at http://dx.doi.org/10.1007/s12064-010-0110-z. The following news item is taken in part from the December 10, 2010 issue of arXiv titled “Are biological systems poised at criticality?,” by Thierry Mora and William Bialek. Many of life's most fascinating phenomena emerge from interactions among many elements—many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this “inverse” approach, using examples form families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised at a very special point in their parameter space—a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems. A link to this article can be found at http://arXiv.org/abs/1012.2242. The following news item is taken in part from the January 14, 2011 issue of Science titled “Quantitative Analysis of Culture Using Millions of Digitized Books,” by Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K. Gray, The Google Books Team, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, and Erez Lieberman Aiden. We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of “culturomics,” focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities. A link to this article can be found at http://dx.doi.org/10.1126/science.1199644. The following news item is taken in part from the December 19, 2010 issue of arXiv titled “BioLogistics and the Struggle for Efficiency: Concepts and Perspectives,” by Dirk Helbing, Andreas Deutsch, Stefan Diez, Karsten Peters, Yannis Kalaidzidis, Kathrin Padberg, Stefan Lammer, Anders Johansson, Georg Breier, Frank Schulze, and Marino Zerial. The growth of world population, limitation of resources, economic problems, and environmental issues force engineers to develop increasingly efficient solutions for logistic systems. Pure optimization for efficiency, however, has often led to technical solutions that are vulnerable to variations in supply and demand, and to perturbations. In contrast, nature already provides a large variety of efficient, flexible and robust logistic solutions. Can we utilize biological principles to design systems, which can flexibly adapt to hardly predictable, fluctuating conditions? We propose a bioinspired “BioLogistics” approach to deduce dynamic organization processes and principles of adaptive self-control from biological systems, and to transfer them to man-made logistics (including nanologistics), using principles of modularity, self-assembly, self-organization, and decentralized coordination. Conversely, logistic models can help revealing the logic of biological processes at the systems level. A link to this article can be found at http://arXiv.org/abs/1012.4189. The following news item is taken in part from the February 1, 2010 issue of PNAS titled “Continuous-time Model of Structural Balance,” by Seth A. Marvel, Jon Kleinberg, Robert D. Kleinberg, and Steven H. Strogatz. It is not uncommon for certain social networks to divide into two opposing camps in response to stress. This happens, for example, in networks of political parties during winner-takes-all elections, in networks of companies competing to establish technical standards, and in networks of nations faced with mounting threats of war. A simple model for these two-sided separations is the dynamical system dX/dt = X2, where X is a matrix of the friendliness or unfriendliness between pairs of nodes in the network. A link to this article can be found at http://dx.doi.org/10.1073/pnas.1013213108. The following news item is taken in part from the January 10, 2011 issue of arXiv titled “Modular Random Boolean Networks,” by Rodrigo Poblanno-Balp and Carlos Gershenson. Random Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with regular topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs are closer to criticality than regular RBNs. A link to this article can be found at http://arXiv.org/abs/1101.1893. The following news item is taken in part from the January 19, 2011 issue of Nature titled “Systemic risk in banking ecosystems,” by Andrew G. Haldane and Robert M. May. In the run-up to the recent financial crisis, an increasingly elaborate set of financial instruments emerged, intended to optimize returns to individual institutions with seemingly minimal risk. Essentially no attention was given to their possible effects on the stability of the system as a whole. Drawing analogies with the dynamics of ecological food webs and with networks within which infectious diseases spread, we explore the interplay between complexity and stability in deliberately simplified models of financial networks. We suggest some policy lessons that can be drawn from such models, with the explicit aim of minimizing systemic risk. A link to this article can be found at http://dx.doi.org/10.1038/nature09659. The following news item is taken in part from the January 28, 2011 issue of Science titled “The Newest Synthesis: Understanding the Interplay of Evolutionary and Ecological Dynamics,” by Thomas W. Schoener. The effect of ecological change on evolution has long been a focus of scientific research. The reverse—how evolutionary dynamics affect ecological traits—has only recently captured our attention, however, with the realization that evolution can occur over ecological time scales. This newly highlighted causal direction and the implied feedback loop—ecoevolutionary dynamics—is invigorating both ecologists and evolutionists and blurring the distinction between them. A link to this article can be found at http://dx.doi.org/10.1126/science.1193954. The following news item is taken in part from the December, 2010 issue of Trends in Ecology & Evolution titled “Swarm intelligence in plant roots,” by František Baluška, Simcha Lev-Yadun, and Stefano Mancuso. Swarm intelligence occurs when two or more individuals independently, or at least partly independently, acquire information that is processed through social interactions and is used to solve a cognitive problem in a way that would be impossible for isolated individuals. We propose at least one example of swarm intelligence in plants: coordination of individual roots in complex root systems. A link to this article can be found at http://dx.doi.org/10.1016/j.tree.2010.09.003. The following news item is taken in part from the January 19, 2011 issue of Nature titled “Primitive agriculture in a social amoeba,” by Debra A. Brock, Tracy E. Douglas, David C. Queller, and Joan E. Strassmann. Here, we show that the social amoeba Dictyostelium discoideum has a primitive farming symbiosis that includes dispersal and prudent harvesting of the crop. About one-third of wild-collected clones engage in husbandry of bacteria. Instead of consuming all bacteria in their patch, they stop feeding early and incorporate bacteria into their fruiting bodies. They then carry bacteria during spore dispersal and can seed a new food crop, which is a major advantage if edible bacteria are lacking at the new site. A link to this article can be found at http://dx.doi.org/10.1038/nature09668. The following news item is taken in part from the January 21, 2011 issue of arXiv titled “Evolutionary Mechanics: New Engineering Principles for the Emergence of Flexibility in a Dynamic and Uncertain World,” by James M. Whitacre, Philipp Rohlfshagen, and Axel Bender. Engineered systems are designed to deftly operate under predetermined conditions yet are notoriously fragile when unexpected perturbations arise. In contrast, biological systems operate in a highly flexible manner, learn quickly adequate responses to novel conditions, and evolve new routines/traits to remain competitive under persistent environmental change. A recent theory on the origins of biological flexibility has proposed that degeneracy—the existence of multifunctional components with partially overlapping functions—is a primary determinant of the robustness and adaptability found in evolved systems. While degeneracy's contribution to biological flexibility is well documented, there has been little investigation of degeneracy design principles for achieving flexibility in systems engineering. A link to this article can be found at http://arXiv.org/abs/1101.4103. The following news item is taken in part from the January 25, 2011 issue of PNAS titled “Morphological Change in Machines Accelerates the Evolution of Robust Behavior,” by Josh Bongard. Most animals exhibit significant neurological and morphological change throughout their lifetime. No robots to date, however, grow new morphological structure while behaving. This is due to technological limitations but also because it is unclear that morphological change provides a benefit to the acquisition of robust behavior in machines. Here, I show that in evolving populations of simulated robots, if robots grow from anguilliform into legged robots during their lifetime in the early stages of evolution, and the anguilliform body plan is gradually lost during later stages of evolution, gaits are evolved for the final, legged form of the robot more rapidly—and the evolved gaits are more robust—compared to evolving populations of legged robots that do not transition through the anguilliform body plan. A link to this article can be found at http://dx.doi.org/10.1073/pnas.1015390108. The following news item is taken in part from the January, 2011 issue of Entropy titled “Complexity through Recombination: From Chemistry to Biology,” by Niles Lehman, Carolina Díaz Arenas, Wesley A. White, and Francis J. Schmidt. Recombination is a common event in nature, with examples in physics, chemistry, and biology. This process is characterized by the spontaneous reorganization of structural units to form new entities. On reorganization, the complexity of the overall system can change. In particular, the components of the system can now experience a new response to externally applied selection criteria, such that the evolutionary trajectory of the system is altered. The link between chemical and biological forms of recombination is explored. The results underscore the importance of recombination in the origins of life on the Earth and its subsequent evolutionary divergence. A link to this article can be found at http://www.mdpi.com/1099-4300/13/1/17/. The following news item is taken in part from the January 19, 2011 issue of Knowledge@Wharton, titled “Gross Domestic Happiness: What Is the Relationship between Money and Well-being?” What exactly is the relationship between money and happiness? It is a difficult question to pin down, experts say. While more money may make us happier, other considerations—such as whether you live in an economically advanced country and how you think about your time—also play into the equation. An increasing number of economists, sociologists, and psychologists are now working in the field, and most agree that there is a strong link between a country's level of economic development and the happiness of its people. A link to this article can be found at http://knowledge.wharton.upenn.edu/article/2675.cfm. International Conference on Swarm Intelligence (ICSI 2011), Cergy, France, 2011/06/14-15 http://icsi11.eisti.fr/ International Conference on Complex Systems (ICCS 2011), Boston, MA, 2011/06/26-07/01 http://www.necsi.edu/events/iccs2011/ GECCO 2011: Genetic and Evolutionary Computation Conference, Dublin, Ireland, 2011/07/12-16 http://www.sigevo.org/gecco-2011/ IJCAI 2011, The 22nd International Conference on International on and International on Theoretical Conference on France, The 2011 International Conference on & Conference on Complex Systems 2011,