A roadmap for a computational theory of the value of information in originof life questions

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.

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.

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.

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

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.

Complex systems: Network thinking

Given continuous improvements in industrial production and living standards, the analysis and detection of complex biological sample systems has become increasingly important. Common complex biological samples include blood, serum, saliva, and urine. At present, the main methods used to separate and recognize target analytes in complex biological systems are electrophoresis, spectroscopy, and chromatography. However, because biological samples consist of complex components, they suffer from the matrix effect, which seriously affects the accuracy, sensitivity, and reliability of the selected separation analysis technique. In addition to the matrix effect, the detection of trace components is challenging because the content of the analyte in the sample is usually very low. Moreover, reasonable strategies for sample enrichment and signal amplification for easy analysis are lacking. In response to the various issues described above, researchers have focused their attention on immuno-affinity technology with the aim of achieving efficient sample separation based on the specific recognition effect between antigens and antibodies. Following a long period of development, this technology is now widely used in fields such as disease diagnosis, bioimaging, food testing, and recombinant protein purification. Common immuno-affinity technologies include solid-phase extraction (SPE) magnetic beads, affinity chromatography columns, and enzyme linked immunosorbent assay (ELISA) kits. Immuno-affinity techniques can successfully reduce or eliminate the matrix effect; however, their applications are limited by a number of disadvantages, such as high costs, tedious fabrication procedures, harsh operating conditions, and ligand leakage. Thus, developing an effective and reliable method that can address the matrix effect remains a challenging endeavor. Similar to the interactions between antigens and antibodies as well as enzymes and substrates, biomimetic molecularly imprinted polymers (MIPs) exhibit high specificity and affinity. Furthermore, compared with many other biomacromolecules such as antigens and aptamers, MIPs demonstrate higher stability, lower cost, and easier fabrication strategies, all of which are advantageous to their application. Therefore, molecular imprinting technology (MIT) is frequently used in SPE, chromatographic separation, and many other fields. With the development of MIT, researchers have engineered different types of imprinting strategies that can specifically extract the target analyte in complex biological samples while simultaneously avoiding the matrix effect. Some traditional separation technologies based on MIP technology have also been studied in depth; the most common of these technologies include stationary phases used for chromatography and adsorbents for SPE. Analytical methods that combine MIT with highly sensitive detection technologies have received wide interest in fields such as disease diagnosis and bioimaging. In this review, we highlight the new MIP strategies developed in recent years, and describe the applications of MIT-based separation analysis methods in fields including chromatographic separation, SPE, diagnosis, bioimaging, and proteomics. The drawbacks of these techniques as well as their future development prospects are also discussed.

Metabolic networks: biology meets engineering sciences

Fluorescein diacetate (FDA) should not be used to study human carboxylesterase 2 (CES2) in complex biological systems without validation.

Task Force on Strategic Research Direction: Basic Science Subgroup key science topics report.

<p>This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian nonparametric inference to learn the statistical and syntactic properties of biological phenomena from sequence data. We show that the models learned by SYNACX offer performance comparable to that of standard neural network architectures. For complex biological systems or processes consisting of several heterogeneous components with spatio-temporal interdependencies across multiple scales, learning frameworks like SYNACX can become unwieldy due to the the resultant combinatorial complexity. Thus we also investigate ways to robustly reduce data dimensionality by introducing a new data abstraction. In particular, we extend traditional string and graph grammars in a new modeling formalism which we call Simplicial Grammar. This formalism integrates the topological properties of the simplicial complex with the expressive power of stochastic grammars in a computation abstraction with which we can decompose complex system behavior, into a finite set of modular grammar rules which parsimoniously describe the spatial/temporal structure and dynamics of patterns inferred from sequence data.</p>

In general biologists are not accustomed to formulating biological problems in the precise mathematical terms that are required to solve them analytically or numerically. Although many computational tools for systems biology have been developed recently, our observations indicate that many of these tools are powerful only in the hands of those who know a lot about how to use them. For most biologists, the tools have a protracted learning curve and unfriendly user interface that often diminish their likelihood of being used. Our long term goal is to build a knowledge system that allows biologists to synthesize complex biological systems via natural language interactions, and the system is able to generate the corresponding mathematical descriptions so that the often cumbersome communication process between biologists and mathematicians/engineers in formulating complex biological problems in mathematic terms can be performed more easily. To focus, the first goal in this research is to build a knowledge system prototype that focuses on transport related biological problems that occur from the cellular to tissue level. We address specifically two inter-related problems: (1) provision of an intelligent system that is capable of automatically synthesizing smaller components into more complex systems; (2) provision of a user-friendly and natural language interface.

Robustness and resilience are widely used in the biological sciences and related disciplines to describe how systems respond to change. Robustness is the ability to tolerate change without adapting or moving to another state. Resilience refers to the ability for a system to sustain a perturbation and maintain critical functions. Robustness and resilience transcend levels of biological organization, though they do not scale directly across levels. We live in an era of novel stressors and unprecedented change, including climate change, emerging environmental contaminants, and changes to the Earth's biogeochemical and hydrological cycles. We envision a common framework for developing models to predict the robustness and resilience of biological functions associated with complex systems that can transcend disciplinary boundaries. Conceptual and quantitative models of robustness and resilience must consider cross-scale interactions of potentially infinite complexity, but it is impossible to capture everything within a single model. Here, we discuss the need to balance accuracy and complexity when designing models, data collection, and downstream analyses to study robustness and resilience. We also consider the difficulties in defining the spatiotemporal domain when studying robustness and resilience as an emergent property of a complex system. We suggest a framework for implementing transdisciplinary research on robustness and resilience of biological systems that draws on participatory stakeholder engagement methods from the fields of conservation and natural resources management. Further, we suggest that a common, simplified model development framework for describing complex biological systems will provide new, broadly relevant educational tools. Efficient interdisciplinary collaboration to accurately develop a model of robustness and resilience would enable rapid, context-specific assessment of complex biological systems with benefits for a broad range of societally relevant problems.

The detection and characterization of low-level protein modifications in a complex system without a methodology for modification enrichment is a very challenging task. This study describes a high-resolution LC/MS-based background subtraction methodology for the unbiased detection and identification of acetaminophen-bound proteins formed in incubations with mouse liver microsomes. The microsomal incubations were conducted using both acetaminophen and [(13)C2,(15)N]acetaminophen at a drug concentration of 200 μM. After tryptic digestion and high-resolution LC/MS analysis, data from the two drug treatment groups were each background-subtracted against the other. Thus, peptide signals that were identical in both groups were effectively canceled out, and drug-bound peptide peaks, differing in masses between the groups because of the isotopic mass shift, were retained after background subtraction and became highlighted in the resultant base peak ion chromatograms. Follow-up MS/MS experiments with these drug-bound peptides led to the identification of three acetaminophen-bound proteins: microsomal glutathione S-transferase, oligosaccharyltransferase subunit ribophorin I, and argininosuccinate synthetase. These initial findings demonstrate the utility of the methodology and may shed new light on the mechanism of acetaminophen-induced hepatotoxicity. The approach is potentially applicable to similar tasks of identification of protein modifications in other complex biological systems.

On the Number of Experiments Required to Find the Causal Structure of Complex Systems