Introduction to Special Issue ECCS’10 in Theory in Biosciences

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,

Complexity at large

The standard assumptions that underlie many conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. This review introduces some of the basic principles of complex systems science, including complexity profiles, the tradeoff between efficiency and adaptability, the necessity of matching the complexity of systems to that of their environments, multiscale analysis, and evolutionary processes. Our focus is on the general properties of systems as opposed to the modeling of specific dynamics; rather than provide a comprehensive review, we pedagogically describe a conceptual and analytic approach for understanding and interacting with the complex systems of our world. This paper assumes only a high school mathematical and scientific background so that it may be accessible to academics in all fields, decision-makers in industry, government, and philanthropy, and anyone who is interested in systems and society.

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.

The digital economy is a complex system, but orthodox economic theory is unable to handle such complexity. For some decades economists have realized that the conventional theoretical models are not consistent with data on what people and institutions actually do, and a new behavioral economics is emerging. The science of complex systems developed over the last 50 years has developed many new ideas and methods for analyzing the non-linear non-equilibrium dynamics of complex systems. Responding to the failures of orthodox economics, those managing the global economy are willing and able to embrace and lead in this new way of looking at economic systems. Thus, economics is evolving and better able to inform decision making in the public and private sectors. This offers new ways of sthinking about the digital economy for entrepreneurs and policy makers which in turn will provide many new example of real-world complexity. The digital economy will drive the creation of future wealth and prosperity, co-evolving with the science of complex systems. We postulate that the research in complex systems will enable better understanding of the digital economy, augment existing economic models and improve their prediction powers.

Genomics, proteomics, and metabolomics have all vastly advanced our understanding of human biology and disease. But the functioning of even a simple system such as a single yeast cell or bacterium is much more complicated than the sum of its genes or proteins or metabolites; it’s the activity of all those components and their relationships to one another that add up to a living organism. Recognizing that complexity, the emerging field of systems biology attempts to harness the power of mathematics, engineering, and computer science to analyze and integrate data from all the “omics” and ultimately create working models of entire biological systems. “Traditionally, scientists—toxicologists included—have relied on a reductionist approach to biology,” says William Suk, director of the NIEHS Center for Risk and Integrated Sciences. Even now, many studies examine complex systems by looking at cellular components in isolation. For instance, a common experiment involves using DNA microarrays to observe the effect of a chemical exposure on thousands of genes at once. This technique can quickly tell a scientist which genes may be vulnerable to that exposure. But a systems biology approach would attempt to model not only the chemical’s effect on gene expression but also how that expression will affect protein function, and in turn how the exposure will affect cell signaling. “There’s nothing wrong with what we’ve been doing,” Suk says. “But systems biology is going to take it to another level.”

Change in a complex system—for example, to its technology, procedures, or information flows— no matter how small, has the potential to create large effects and ripples of disruption. A complex system’s dynamics cannot be fully known, and the effects and disruptions produced by change are difficult to predict. Nonetheless, complex systems can be at least partly understood in terms of patterns in their dynamics, generalizable principles, and mechanisms of control, balance, and adaptation. This panel will focus on complex systems research and what they suggest about how to introduce change into a complex system such that the work system resilience and health are disrupted minimally. Case studies may be discussed, as well; examples of changes to established complex work systems. These changes include introducing remotely piloted aircraft systems (RPAS) into the National Airspace System (NAS), additional automation into air traffic control, and new technology into military air combat training.

<i>Introduction to the Modeling and Analysis of Complex Systems.</i> H. Sayama (Ed.). (2015, Open SUNY Textbooks). Free open access PDF, 498 pp. ISBN 978-1-942341-06-2 (deluxe color edition). ISBN 978-1-942341-08-6 (print edition). ISBN 978-1-942341-09-3 (ebook).

EDITORIAL article Front. Comput. Neurosci., 10 September 2010 Volume 4 - 2010 | https://doi.org/10.3389/fncom.2010.00007

To explore the movement toward increased complex systems (CS) research in education, this special issue of the Journal of Experimental Education was developed to identify some under-examined places where CS approaches have advanced research. The articles provide empirical examples of research leveraging methods and analyses from complexity science. In this introduction article to the special issue, we discuss the authors’ contributions to defining and explicating concepts and methods that fall under the umbrella of CS perspectives and how these methods were used to investigate complex processes in their topics of study. The articles capture the goal of the special issue to demonstrate how the research questions framed by CS assumptions can change our expectations about the very nature of the processes under study in education. We then take a more global perspective and acknowledge the commonalities amongst the papers, including (a) the reliance on intensive data to answer research questions, and (b) the use of dynamic approaches that yield findings about the stability and change of these systems and phenomena.

Distributed redundancy and robustness in complex systems

Abstract: This chapter delves into the advanced mathematical approaches used in precision modeling to analyze and understand complex systems in both science and economics. It explores the foundational principles of nonlinear dynamics, chaos theory, stochastic processes, and agent-based modeling, which are essential for capturing the intricate behaviors of these systems. The chapter further examines the application of these models in various scientific fields, including biology, ecology, physics, and climate science, as well as in economic contexts such as macroeconomic modeling, financial markets, and behavioral economics. It also addresses the challenges associated with precision modeling, including model uncertainty, data quality, and ethical considerations, while highlighting the emerging trends and innovations, such as artificial intelligence integration and hybrid models. The chapter concludes by emphasizing the importance of interdisciplinary collaboration and the transformative potential of precision modeling in driving scientific discovery, informing policy decisions, and addressing global challenges. Keywords: Keywords: Precision Modeling, Complex Systems, Nonlinear Dynamics, Chaos Theory, Stochastic Processes, Agent-Based Modeling, Macroeconomic Modeling, Financial Markets, Behavioral Economics, Interdisciplinary Collaboration, Artificial Intelligence, Hybrid Models, Scientific Discovery, Policy Decision-Making, Global Challenges.

Metabolic networks: biology meets engineering sciences

The name of this panel session called for a discussion on the progress in human-system interaction. It appears that the progress in that area is invariably associated with intelligent agents, a subject which reoccurred in each panel presentation. An increased power of computing machinery together with an advancement of technology in other areas opened a new frontier for human factors research in complex human machine systems employing high degree of automation as well as humans. Intelligent agents entered various stages of human interaction with complex system, such as the design of automated systems, operation and maintenance of complex systems, and training of humans which interact with complex systems. Agents, by the earliest definition are artifacts that have a very specialized function, usually quite complex. In the human-machine systems intelligent agents often serve as computer interface agents, systems that can serve as go-betweens because they posses some specialized skills. There are agents that serve as assistants to humans interacting and controlling complex systems, and agents that serve as tutors training the humans to operate systems. The set of tasks and applications where intelligent agents could be employed is virtually unlimited.

The subsurface environment, which encompasses the vadose and saturated zones, is a heterogeneous, geologically complex domain. Believed to contain a large percentage of Earth's biomass in the form of microorganisms, the subsurface is a dynamic zone where important biogeochemical cycles work to sustain life. Actively linked to the atmosphere and biosphere through the hydrologic and carbon cycles, the subsurface serves as a storage location for much of Earth's fresh water. Coupled hydrological, microbiological, and geochemical processes occurring within the subsurface environment cause the local and regional natural chemical fluxes that govern water quality. These processes play a vital role in the formation of soil, economically important fossil fuels, mineral deposits, and other natural resources. Cleaning up Department of Energy (DOE) lands impacted by legacy wastes and using the subsurface for carbon sequestration or nuclear waste isolation require a firm understanding of these processes and the documented means to characterize the vertical and spatial distribution of subsurface properties directing water, nutrient, and contaminant flows. This information, along with credible, predictive models that integrate hydrological, microbiological, and geochemical knowledge over a range of scales, is needed to forecast the sustainability of subsurface water systems and to devise ways to manage and manipulate dynamic in situ processes for beneficial outcomes. Predictive models provide the context for knowledge integration. They are the primary tools for forecasting the evolving geochemistry or microbial ecology of groundwater under various scenarios and for assessing and optimizing the potential effectiveness of proposed approaches to carbon sequestration, waste isolation, or environmental remediation. An iterative approach of modeling and experimentation can reveal powerful insights into the behavior of subsurface systems. State-of-science understanding codified in models can provide a basis for testing hypotheses, guiding experiment design, integrating scientific knowledge on multiple environmental systems into a common framework, and translating this information to support informed decision making and policies. Subsurface behavior typically has been investigated using reductionist, or bottom-up approaches. In these approaches, mechanisms of small-scale processes are quantified, and key aspects of their behaviors are moved up to the prediction scale using scaling laws and models. Reductionism has and will continue to yield essential and comprehensive understanding of the molecular and microscopic underpinnings of component processes. However, system-scale predictions cannot always be made with bottom-up approaches because the behaviors of subsurface environments often simply do not result from the sum of smaller-scale process interactions. Systems exhibiting such behavior are termed complex and can range from the molecular to field scale in size. Complex systems contain many interactive parts and display collective behavior including emergence, feedback, and adaptive mechanisms. Microorganisms - key moderators of subsurface chemical processes - further challenge system understanding and prediction because they are adaptive life forms existing in an environment difficult to observe and measure. A new scientific approach termed complex systems science has evolved from the critical need to understand and model these systems, whose distinguishing features increasingly are found to be common in the natural world. In contrast to reductionist approaches, complexity methods often use a top-down approach to identify key interactions controlling diagnostic variables at the prediction scale; general macroscopic laws controlling system-scale behavior; and essential, simplified models of subsystem interactions that enable prediction. This approach is analogous to systems biology, which emphasizes the tight coupling between experimentation and modeling and is defined, in the context of Biological Systems Science research programs under DOE's Office of Biological and Environmental Research (BER), as ''the holistic, multidisciplinary study of complex interactions that specify the function of an entire biological system - whether single cells or a multicellular organism - rather than the reductionist study of individual components.'' In August 2009, BER held the Subsurface Complex System Science Relevant to Contaminant Fate and Transport workshop to assess the merits and limitations of complex systems science approaches to subsurface systems controlled by coupled hydrological, microbiological, and geochemical processes.