Taming the Unknown Unknowns in Complex Systems: Challenges and Opportunities for Modeling, Analysis and Control of Complex (Biological) Collectives

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.

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.

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.

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.

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.”

Understanding the functioning of natural systems is not easy, although there is general agreement that understanding complex systems is an important goal for science education. Defining what makes a natural system complex will assist in identifying gaps in research on student reasoning about systems. The goal of this commentary is to propose a framework that explicitly defines the ways in which biological systems are complex and to discuss the potential relevance of these complexity dimensions to conducting research on student reasoning about complexity in biology classrooms. We use an engineering framework for dimensions of complexity and discuss how this framework may also be applied to biological systems, using gene expression as an example. We group dimensions of this framework into components, functional relationships among components, processes, manifestations, and interpretations within biological systems. We explain four steps that discipline-based education researchers can use to apply these dimensions to explore student reasoning about complex biological systems.

Metabolic networks: biology meets engineering sciences

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.

It is very difficult to use exact mathematical models to study complexity systems. As the units beside complex systems can interact to bring forth its complexity, it is completely true for the mammal body, a classic kind of biological complex system. As singular biological complexity systems, including the mammal body, are considered in a series of new research fields, a few passivity or biological control studies have been carried out up to now. In this research, the single-chamber model of the environmental hormone formaldehyde which is flowing in the mammal body has been set up according to the corresponding physiological rules with the model passivity described in detail. Under the strict passivity station, the feedback controller for this singular mammal body complexity system has been designed with a controller example also given as a model instantiation. Both passivity study and feedback controller design of the mammal body complex system can be applied to biological complexity systems and lay a useful foundation for singular system research.

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

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

Abstract: Mathematical modeling of complex systems provides a rigorous framework for representing, analyzing, and predicting the behaviors that emerge from interacting components across diverse domains. This book develops an integrated conceptual and methodological framework that unites systems theory, nonlinear dynamics, stochastic processes, and computational intelligence to address complexity in biology, economics, and related fields. The problem addressed is the persistent gap between theoretical models and real-world decision-making, particularly under uncertainty, multi-scale interactions, and emergent phenomena. Methodologically, the work combines analytical modeling, simulation-based approaches, and hybrid theory–data integration, incorporating techniques such as epidemiological compartment models, biochemical reaction networks, agent-based economic simulations, and machine learning–augmented forecasting. The analysis demonstrates that cross-domain modeling not only uncovers structural parallels—such as feedback loops, network interdependencies, and adaptive behaviors—but also enables transferable tools for prediction, risk assessment, and policy design. Key results include a unified perspective on domain-specific methodologies, a framework for model validation and uncertainty quantification, and best-practice guidelines for computational enhancement and ethical deployment. The implications extend to improved predictive reliability in high-stakes contexts, from pandemic response and environmental management to macroeconomic stability and financial regulation. By bridging mathematical theory, computational innovation, and governance principles, this work positions modeling as both a scientific and strategic instrument for navigating complexity in the 21st century. Keywords: mathematical modeling, complex systems, nonlinear dynamics, stochastic processes, systems theory, computational modeling, biology, economics, epidemiological models, agent-based modeling, network theory, uncertainty quantification, sensitivity analysis, policy modeling, interdisciplinary frameworks, simulation, machine learning, emergent behavior, validation, ethical modeling

Abrupt shifts, referred to as critical transitions, are frequently observed in complex biological systems, characterized by marked qualitative changes occurring from one stable state to another through a pre-transitional/critical state. Pinpointing such critical states, along with the signaling molecules, can provide valuable insights into the fundamental mechanisms of intricate biological processes. However, the identification and early warning of the critical state remains a challenge, particularly in model-free cases with high-dimensional single-cell data, where traditional statistical methods often prove inadequate due to the inherent sparsity, noise, and heterogeneity of the data. In this study, we propose a novel quantitative method, cell-specific causal network entropy (CCNE), to infer the specific causal network for each cell and quantify dynamic causal changes, thereby enabling the identification of critical states in complex biological processes at the single-cell level. We validated the accuracy and effectiveness of the proposed approach through numerical simulations and 5 distinct real-world single-cell datasets. Compared to existing methods for detecting critical states, the proposed CCNE exhibits enhanced effectiveness in identifying critical transition signals. Moreover, CCNE score is a computational tool for distinguishing temporal changes in cellular heterogeneity and demonstrates satisfactory performance in clustering cells over time. In addition, the reliability of CCNE is further emphasized through the functional enrichment and pathway analysis of signaling molecules.

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.

Modern X-ray scattering studies of complex biological systems