Organoids as complex (bio)systems

Complex systems theory is concerned with identifying and characterizing common design elements that are observed across diverse natural, technological and social complex systems. Systems biology, a more holistic approach to study molecules and cells in biology, has advanced rapidly in the past two decades. However, not much appreciation has been granted to the realization that the human cell is an exemplary complex system. Here, I outline general design principles identified in many complex systems, and then describe the human cell as a prototypical complex system. Considering concepts of complex systems theory in systems biology can illuminate our overall understanding of normal cell physiology and the alterations that lead to human disease.

Contracts as Systems

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.

Study on the formation of chemical wave patterns for the Belousov–Zhabotinsky reaction system

I General Aspects of Complex Systems.- Synergetics as a Strategy to Cope with Complex Systems.- Evolution, Creativity and Intelligence in Complex Systems.- Philosophical Foundations of Nonlinear Complex Systems.- II Mathematical Models of Populations and Societies.- Diversity and Collective Action.- On the Application of Synergetics to Social Systems.- Emergent Behavior in Insect Societies: Global Oscillations, Chaos and Computation.- Collective Dynamics in Models of Communicating Populations.- III Complex Systems in Social Sciences and Psychology.- From Social Engineering to Synergetics On Metaphors Models and Reality.- Social Order. From Individual Activity to Functional Cooperation.- The Significance of Nonlinear Phenomena for the Investigation of Cognitive Systems.- Pattern Formation in Complex Cognitive Processes.- IV Complex Systems in Biology, Physiology and Ecology.- Modelling Pattern Formation in Ecological Systems.- Characterization of Temporal and Spatio-temporal Chaos.- Attractor-Ruled Dynamics in Neurobiology: Does it Exist? Can it be Measured?.- Synergetics of Blood Movement Through Microvascular Networks: Causes and Consequences of Nonlinear Pressure-Flow Relationships.- Index of Contributors.

The study of human properties in complex systems is a central problem in the development of 'Complex system theory'. Due to the complexity of this problem, there has been no a general theory to deal with it. In this paper, we develop a general framework to link all the complicated human properties in complex systems. The basic idea of this approach is as follow, first, human properties in complex systems are classified according to the three-level theory of human behaviors then, the detailed theories for each level behaviors are developed finally, human properties in complex systems are studied by these detailed theories. In the first part of this paper, the three-level theory of human behaviors is reviewed briefly. Then human properties In a concrete complex system -- C system -- are analysed very briefly. In the second part, a new theory - an Information model for human activities in complex systems -- is developed in some details which can be used to study the rate-based and knowledge-based behaviors of human beings In complex systems. Finally, a practical system -- the Combat Planning Division In Tactical Air Control Center -- is represented in the framework of this information model.

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.

A longstanding philosophical premise perceives simplicity as a desirable attribute of scientific theories. One of several raised justifications for this notion is that simple theories are more likely to indicate the true makeup of natural systems. Qualitatively parsimonious hypotheses and theories keep to a minimum the number of different postulated entities within a system. Formulation of such ontologically simple working hypotheses proved to be useful in the experimental probing of narrowly defined bio systems. It is less certain, however, whether qualitatively parsimonious theories are effective indicators of the true nature of complex biological systems. This paper assesses the success of ontologically simple theories in envisaging the makeup of three complex systems in bacteriology, immunology, and molecular biology. Evidence shows that parsimonious theories completely misconstrued the actual ontologically complex constitutions of the three examined systems. Since evolution and selective pressures typically produce ontologically intricate rather than simple bio systems, qualitatively parsimonious theories are mostly inapt indicators of the true nature of complex biological systems.

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 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.

The Law of Requisite Variety is a mathematical theorem relating the number of control states of a system to the number of variations in control that is necessary for effective response. The Law of Requisite Variety does not consider the components of a system and how they must act together to respond effectively. Here we consider the additional requirement of scale of response and the effect of coordinated versus uncoordinated response as a key attribute of complex systems. The components of a system perform a task, with a number of such components needed to act in concert to perform subtasks. We apply the resulting generalization—a Multiscale Law of Requisite Variety—to understanding effective function of complex biological and social systems. This allows us to formalize an understanding of the limitations of hierarchical control structures and the inadequacy of central control and planning in the solution of many complex social problems and the functioning of complex social organizations, e.g., the military, healthcare, and education systems. © 2004 Wiley Periodicals, Inc. Complexity 9: 37–45, 2004

Chemistry in Taiwan and Academia Sinica.

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.

Distributed redundancy and robustness in complex systems

In popular dialogues, describing a system as "complex" is often the point of resignation, inferring that the system cannot be sufficiently described, predicted nor managed. Transport networks, management infrastructure and supply chain logistics are all often described in this way. Academic dialogues have begun to explore the collective behaviors of complex systems to define a complex system specifically as an adaptive one; i.e. a system that demonstrates 'self organising' principles and 'emergent' properties. Based upon the key principles of interaction and emergence in relation to adaptive and self organising systems in cultural artifacts and processes, this paper will argue that complex systems are cultural systems. By introducing generic principles of complex systems, and looking at the exploration of such principles in art, design and media research, this paper argues that a science of cultural systems as part of complex systems theory is the post modern science for the digital age. Furthermore, that such a science was predicated by post structuralism and has been manifest in art, design and media practice since the late 1960s.