Biochemical Reaction Networks Research Articles

An enzymatically activated AND-gate DNA logic circuit for tumor cells recognition via multi-microRNAs detection

The DNA-based logic circuit, constructed to mimic biochemical reaction networks, is highly significant in detecting biomarkers at the molecular level. The differences in the expression levels of microRNAs (miRNAs) within different types of cells provide hope for distinguishing cell subtypes. However, reliance on a single miRNA often leads to unreliable results. Herein, we constructed an enzyme-triggered cascade logic circuit based on the AND gate, which is capable of generating corresponding fluorescence signals in the presence of target miRNAs. The introduction of apurinic/apyrimidinic (AP) sites effectively reduces the likelihood of false signal generation. Amplification of the fluorescence signal relies on the catalytic hairpin assembly and the repetitive reuse of the multicomponent nucleic acid enzyme (MNAzyme). We demonstrated that the logic circuit can not only distinguish cancer cells from normal cells but also identify different types of cancer cells. The programmability of the logic circuits and the simplicity of the assay system allow us to modify the functional sequences to recognize different types of biomarkers, thus providing a reference for the identification of various cell subtypes.

Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

Tracing the Diverse Paths of One-Carbon Metabolism in Cancer and Beyond.

One-carbon (1C) metabolism is a network of biochemical reactions distributed across organelles that delivers folate-activated 1C units to support macromolecule synthesis, methylation, and reductive homeostasis. Fluxes through these pathways are up-regulated in highly proliferative cancer cells, and anti-folates, which target enzymes within the 1C pathway, have long been used in the treatment of cancer. In this work, we review fundamental aspects of 1C metabolism and place it in context with other biosynthetic and redox pathways, such that 1C metabolism acts to bridge pathways across compartments. We further discuss the importance of stable-isotope-tracing techniques combined with mass spectrometry analysis to study 1C metabolism and conclude by highlighting therapeutic approaches that could exploit cancer cells' dependency on 1C metabolism.

Analysis and design of antithetic proportional-integral-derivative biocontrol-systems with species dilution

The nonlinearity and non-separability of the antithetic PID (aPID) controller have provided greater flexibility in the design of biochemical reaction networks (BCRNs), resulting in significant impacts on biocontrol-systems. Nevertheless, the dilution of control species is disregarded in designs of aPID controllers, which would lead to the failure of inhibition mechanism in the controller and loss of robust perfect adaptation (RPA)—the biological counterpart of robust steady-state tracking. Here, the impact of dilution processes on the structure of aPID is investigated in this study. It is discovered that the proportional and low-pass filters are altered when the dilution processes is present in control species, which increases the coupling between the controller parameters. Moreover, additional integrations for the reference signal and control output generated by control species dilution further leads to the loss of RPA. Subsequently, a novel aPID controller represented by BCRNs, termed quasi-aPID, has been designed to eliminate the detrimental effects of the dilution processes. In an effort to ameliorate the interdependencies among controller parameters, a degradation inhibition mechanism is employed within this controller. Furthermore, this work establishes the limiting relationship between the controller’s reaction rates in order to guarantee RPA, while abstaining from the introduction of supplementary species and biochemical reactions. By using the quasi-aPID controller in both the Escherichia coli gene expression model and the whole-body cholesterol metabolism model, its effectiveness is confirmed. Simulation results demonstrate that, the quasi-aPID exhibits a smaller absolute steady-state error in both models and guarantees the RPA property.

Compartmental exchange regulates steady states and stochastic switching of a phosphorylation network

The phosphoregulation of proteins with multiple phosphorylation sites is governed by biochemical reaction networks that can exhibit multistable behavior. However, the behavior of such networks is typically studied in a single reaction volume, while cells are spatially organized into compartments that can exchange proteins. In this work, we use stochastic simulations to study the impact of compartmentalization on a two-site phosphorylation network. We characterize steady states and fluctuation-driven transitions between them as a function of the rate of protein exchange between two compartments. Surprisingly, the average time spent in a state before stochastically switching to another depends nonmonotonically on the protein exchange rate, with the most frequent switching occurring at intermediate exchange rates. At sufficiently small exchange rates, the state of the system and mean switching time are controlled largely by fluctuations in the balance of enzymes in each compartment. This leads to negatively correlated states in the compartments. For large exchange rates, the two compartments behave as a single effective compartment. However, when the compartmental volumes are unequal, the behavior differs from a single compartment with the same total volume. These results demonstrate that exchange of proteins between distinct compartments can regulate the emergent behavior of a common signaling motif.

DNA Reaction System That Acquires Classical Conditioning.

Biochemical reaction networks can exhibit plastic adaptation to alter their functions in response to environmental changes. This capability is derived from the structure and dynamics of the reaction networks and the functionality of the biomolecule. This plastic adaptation in biochemical reaction systems is essentially related to memory and learning capabilities, which have been studied in DNA computing applications for the past decade. However, designing DNA reaction systems with memory and learning capabilities using the dynamic properties of biochemical reactions remains challenging. In this study, we propose a basic DNA reaction system design that acquires classical conditioning, a phenomenon underlying memory and learning, as a typical learning task. Our design is based on a simple mechanism of five DNA strand displacement reactions and two degradative reactions. The proposed DNA circuit can acquire or lose a new function under specific conditions, depending on the input history formed by repetitive stimuli, by exploiting the dynamic properties of biochemical reactions induced by different input timings.

A data-driven approach for timescale decomposition of biochemical reaction networks.

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for the timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor-driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications. IMPORTANCE Complex interactions within interconnected biochemical reaction networks enable cellular responses to a wide range of unpredictable environmental perturbations. Understanding how biological functions arise from these intricate interactions has been a long-standing problem in biology. Here, we introduce a computational approach to dissect complex biological systems' dynamics in evolving environments. This approach characterizes the timescale hierarchies of complex reaction networks, offering a system-level understanding at physiologically relevant timescales. Analyzing various hypothetical and biological pathways, we show how stoichiometric properties shape the way energy is dissipated throughout reaction networks. Notably, we establish that glycolysis operates as a cofactor-driven pathway, where the slowest dynamics are governed by a balance between high-energy phosphate bonds and redox trafficking. This approach enhances our understanding of network dynamics and facilitates the development of reduced-order kinetic models with biologically interpretable components.

Multimolecular Competition Effect as a Modulator of Protein Localization and Biochemical Networks in Cell-Size Space.

Cells are small, closed spaces filled with various types of macromolecules. Although it is shown that the characteristics of biochemical reactions in vitro are quite different from those in living cells, the role of the co-existence of various macromolecules in cell-size space remains still elusive. Here, using a constructive approach, it is demonstrated that the co-existence of various macromolecules themselves has the ability to tune protein localization for spatiotemporal regulation and a biochemical reaction system in a cell-size space. Both experimental and theoretical analyses reveal that enhancement of interfacial effects by a large surface-area-to-volume ratio facilitates membrane localization of molecules in the cell-size space, and the interfacial effects are alleviated by competitive binding to lipid membranes among multiple proteins even if their membrane affinities are weak. These results indicate that competition for membrane binding among various macromolecules in the cell-size space plays a role in regulating the spatiotemporal molecular organization and biochemical reaction networks. These findings shed light on the importance of surrounding molecules for biochemical reactions using purified elements in small spaces.

On Persistence of Some $\mathcal {W}_{I}$-Endotactic Chemical Reaction Networks

Motivated by the concept of the endotactic network, a kind of special geometric structure in chemical reaction networks developed for persistence analysis, we propose a new notion, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W}_{I}$</tex-math></inline-formula> -endotactic network. The corresponding network set is a larger class of set than the endotactic network, let alone the weakly reversible network. Based on an energy-like function, we prove that all 1-dimensional mass-action <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W}_{I}$</tex-math></inline-formula> -endotactic networks are persistent. Further, we prove some higher dimensional network systems also to support persistence if the system is composed of a series of 1-dimensional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W}_{I}$</tex-math></inline-formula> -endotactic networks. We discuss two cases for the sub-systems with and without intersecting species, and present the corresponding sufficient conditions to capture persistence. Finally, we use some examples including abstract and real biochemical reaction networks to illustrate our results.

A query-response causal analysis of reaction events in biochemical reaction networks

The stochastic kinetics of biochemical reaction networks is described by a chemical master equation (CME) and the underlying laws of mass action. Assuming network-free simulations of the rule-based models of biochemical reaction networks (BRNs), this paper departs from the usual analysis of network dynamics as the time-dependent distributions of chemical species counts, and instead considers statistically evaluating the sequences of reaction events generated from the stochastic simulations. The reaction event-time series can be used for reaction clustering, identifying rare events, and recognizing the periods of increased or steady-state activity. However, the main aim of this paper is to device an effective method for identifying causally and anti-causally related sub-sequences of reaction events using their empirical probabilities. This allows discovering some of the causal dynamics of BRNs as well as uncovering their short-term deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related. Moreover, since the time-ordering of reaction events is locally irrelevant, the reaction sub-sequences can be transformed into the reaction sets or multi-sets. The distance metrics can be then used to define the equivalences among the reaction events. The proposed method for identifying the causally related reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The method was evaluated for five models of genetic networks in seven defined numerical experiments. The models were simulated in BioNetGen using the open-source network-free simulator NFsim. This simulator had to be modified first to allow recording the traces of reaction events, and it is available in the Github repository, ploskot/nfsim_1.20. The generated event time-series were analyzed with Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. This demonstrates the opportunities for substantially increasing the research productivity by creating automated data generation and processing pipelines.

Basic pathway decomposition of biochemical reaction networks within growing cells

This contribution treats linear, steady-state dynamics for a metabolic network within a growing cell. Admissible steady-state reaction fluxes are assumed to form a pointed, convex, polyhedral, conical subset of the stoichiometric null-space. A solution of the problem is defined to consist of a linear basis for the stoichiometric null-space consisting of admissible fluxes called basic pathways. The algorithm used to construct the set of basic pathways scales as a polynomial of the system size in contrast to the NP-hard algorithms employed in the traditional notions of solution named extreme pathways, elementary flux modes, MEMos, and MinSpan, and that therefore suffer from the curse of dimensionality. The basic pathways approach is applied to a metabolic network consisting of a simplified version of the TCA cycle coupled to glycolysis highlighting that each basic pathway has a readily understood chemical interpretation. Generic admissible pathways are simply expressed in terms of basic pathways.

The asymptotic tails of limit distributions of continuous-time Markov chains

Abstract This paper investigates tail asymptotics of stationary distributions and quasi-stationary distributions (QSDs) of continuous-time Markov chains on subsets of the non-negative integers. Based on the so-called flux-balance equation, we establish identities for stationary measures and QSDs, which we use to derive tail asymptotics. In particular, for continuous-time Markov chains with asymptotic power law transition rates, tail asymptotics for stationary distributions and QSDs are classified into three types using three easily computable parameters: (i) super-exponential distributions, (ii) exponential-tailed distributions, and (iii) sub-exponential distributions. Our approach to establish tail asymptotics of stationary distributions is different from the classical semimartingale approach, and we do not impose ergodicity or moment bound conditions. In particular, the results also hold for explosive Markov chains, for which multiple stationary distributions may exist. Furthermore, our results on tail asymptotics of QSDs seem new. We apply our results to biochemical reaction networks, a general single-cell stochastic gene expression model, an extended class of branching processes, and stochastic population processes with bursty reproduction, none of which are birth–death processes. Our approach, together with the identities, easily extends to discrete-time Markov chains.

Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models.

Likelihood ratios are frequently utilized as basis for statistical tests, for model selection criteria and for assessing parameter and prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood ratios into p-values or confidence intervals requires the exact form of the test statistic's distribution. The lack of knowledge about this distribution for nonlinear ordinary differential equation (ODE) models requires an approximation which assumes the so-called asymptotic setting, i.e. a sufficiently large amount of data. Since the amount of data from quantitative molecular biology is typically limited in applications, this finite-sample case regularly occurs for mechanistic models of dynamical systems, e.g. biochemical reaction networks or infectious disease models. Thus, it is unclear whether the standard approach of using statistical thresholds derived for the asymptotic large-sample setting in realistic applications results in valid conclusions. In this study, empirical likelihood ratios for parameters from 19 published nonlinear ODE benchmark models are investigated using a resampling approach for the original data designs. Their distributions are compared to the asymptotic approximation and statistical thresholds are checked for conservativeness. It turns out, that corrections of the likelihood ratios in such finite-sample applications are required in order to avoid anti-conservative results.

STARGATE-X: a Python package for statistical analysis on the REACTOME network.

Many important aspects of biological knowledge at the molecular level can be represented by pathways. Through their analysis, we gain mechanistic insights and interpret lists of interesting genes from experiments (usually omics and functional genomic experiments). As a result, pathways play a central role in the development of bioinformatics methods and tools for computing predictions from known molecular-level mechanisms. Qualitative as well as quantitative knowledge about pathways can be effectively represented through biochemical networks linking the biochemical reactions and the compounds (e.g., proteins) occurring in the considered pathways. So, repositories providing biochemical networks for known pathways play a central role in bioinformatics and in systems biology. Here we focus on Reactome, a free, comprehensive, and widely used repository for biochemical networks and pathways. In this paper, we: (1) introduce a tool StARGate-X (STatistical Analysis of the Reactome multi-GrAph Through nEtworkX) to carry out an automated analysis of the connectivity properties of Reactome biochemical reaction network and of its biological hierarchy (i.e., cell compartments, namely, the closed parts within the cytosol, usually surrounded by a membrane); the code is freely available at https://github.com/marinoandrea/stargate-x; (2) show the effectiveness of our tool by providing an analysis of the Reactome network, in terms of centrality measures, with respect to in- and out-degree. As an example of usage of StARGate-X, we provide a detailed automated analysis of the Reactome network, in terms of centrality measures. We focus both on the subgraphs induced by single compartments and on the graph whose nodes are the strongly connected components. To the best of our knowledge, this is the first freely available tool that enables automatic analysis of the large biochemical network within Reactome through easy-to-use APIs (Application Programming Interfaces).

Learning capacity and function of stochastic reaction networks

Biochemical reaction networks are expected to encode an efficient representation of the function of cells in a variable environment. It is thus important to see how these networks do learn and implement such representations. The first step in this direction is to characterize the function and learning capabilities of basic artificial reaction networks. In this study, we consider multilayer networks of reversible reactions that connect two layers of signal and response species through an intermediate layer of hidden species. We introduce a stochastic learning algorithm that updates the reaction rates based on the correlation values between reaction products and responses. Our findings indicate that the function of networks with random reaction rates, as well as their learning capacity for random signal-response activities, are critically determined by the number of reactants and reaction products. Moreover, the stored patterns exhibit different levels of robustness and qualities as the reaction rates deviate from their optimal values in a stochastic model of defect evolution. These findings can help suggest network modules that are better suited to specific functions, such as amplifiers or dampeners, or to the learning of biologically relevant signal-response activities.

The membrane surface as a platform that organizes cellular and biochemical processes.

Membranes are essential for life. They act as semi-permeable boundaries that define cells and organelles. In addition, their surfaces actively participate in biochemical reaction networks, where they confine proteins, align reaction partners, and directly control enzymatic activities. Membrane-localized reactions shape cellular membranes, define the identity of organelles, compartmentalize biochemical processes, and can even be the source of signaling gradients that originate at the plasma membrane and reach into the cytoplasm and nucleus. The membrane surface is, therefore, an essential platform upon which myriad cellular processes are scaffolded. In this review, we summarize our current understanding of the biophysics and biochemistry of membrane-localized reactions with particular focus on insights derived from reconstituted and cellular systems. We discuss how the interplay of cellular factors results in their self-organization, condensation, assembly, and activity, and the emergent properties derived from them.

Thermoring-based heat activation switches in the TRPV1 biothermometer

Non-covalent interactions in bio-macromolecules are individually weak but collectively important. How they take a concerted action in a complex biochemical reaction network to realize their thermal stability and activity is still challenging to study. Here graph theory was used to investigate how the temperature-dependent non-covalent interactions as identified in the 3D structures of the thermo-gated capsaicin receptor TRPV1 could form a systemic fluidic grid-like mesh network with topological grids constrained as the thermo-rings to govern heat-sensing. The results showed that the heat-evoked melting of the biggest grid initiated a matched temperature threshold to release the lipid from the active vanilloid site for channel activation. Meanwhile, smaller grids were required to stabilize heat efficacy. Altogether, the change in the total grid sizes upon the change in the total noncovalent interactions along the lipid-dependent gating pathway was necessary for the matched temperature sensitivity. Therefore, this grid thermodynamic model may be broadly significant for the structural thermostability and the functional thermoactivity of bio-macromolecules.

Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth.

The physiology of biological cells evolved under physical and chemical constraints, such as mass conservation across the network of biochemical reactions, nonlinear reaction kinetics, and limits on cell density. For unicellular organisms, the fitness that governs this evolution is mainly determined by the balanced cellular growth rate. We previously introduced growth balance analysis (GBA) as a general framework to model and analyze such nonlinear systems, revealing important analytical properties of optimal balanced growth states. It has been shown that at optimality, only a minimal subset of reactions can have nonzero flux. However, no general principles have been established to determine if a specific reaction is active at optimality. Here, we extend the GBA framework to study the optimality of each biochemical reaction, and we identify the mathematical conditions determining whether a reaction is active or not at optimal growth in a given environment. We reformulate the mathematical problem in terms of a minimal number of dimensionless variables and use the Karush-Kuhn-Tucker (KKT) conditions to identify fundamental principles of optimal resource allocation in GBA models of any size and complexity. Our approach helps to identify from first principles the economic values of biochemical reactions, expressed as marginal changes in cellular growth rate; these economic values can be related to the costs and benefits of proteome allocation into the reactions' catalysts. Our formulation also generalizes the concepts of Metabolic Control Analysis to models of growing cells. We show how the extended GBA framework unifies and extends previous approaches of cellular modeling and analysis, putting forward a program to analyze cellular growth through the stationarity conditions of a Lagrangian function. GBA thereby provides a general theoretical toolbox for the study of fundamental mathematical properties of balanced cellular growth.

Quantifying Information of Dynamical Biochemical Reaction Networks.

A large number of complex biochemical reaction networks are included in the gene expression, cell development, and cell differentiation of in vivo cells, among other processes. Biochemical reaction-underlying processes are the ones transmitting information from cellular internal or external signaling. However, how this information is measured remains an open question. In this paper, we apply the method of information length, based on the combination of Fisher information and information geometry, to study linear and nonlinear biochemical reaction chains, respectively. Through a lot of random simulations, we find that the amount of information does not always increase with the length of the linear reaction chain; instead, the amount of information varies significantly when this length is not very large. When the length of the linear reaction chain reaches a certain value, the amount of information hardly changes. For nonlinear reaction chains, the amount of information changes not only with the length of this chain, but also with reaction coefficients and rates, and this amount also increases with the length of the nonlinear reaction chain. Our results will help to understand the role of the biochemical reaction networks in cells.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Since its dawn, systems-and-control theory has been strongly and deeply interlaced with the life sciences, as is emphasized by the work of some of its greatest pioneers. Norbert Wiener presented Cybernetics in his book as Control and Communication in the Animal and the Machine, thus stressing the remarkable similarities between engineering mechanisms and regulation in living beings. Ludwig von Bertalanffy, the founder of General Systems Theory, advocated the use of mathematical approaches and system-level thinking in the study of biology, and was a biologist himself. The approaches developed within the realm of systems-and-control theory can be successfully employed to better understand the functioning of systems in the life sciences at all scales, from biochemical reaction networks to biological systems, from biomedical systems to ecological networks and epidemic phenomena. Novel rigorous methods can guide the control, optimization, and bottom-up design of systems in synthetic biology, as well as the development of approaches for biomedicine and of strategies for the control of epidemics. Systems in the life sciences are characterized by a high level of complexity, steep nonlinearities, and largely uncertain dynamics: their features pose many challenges that can inspire the development of new theory in robust and nonlinear control. This special issue has collected recent advances and new exciting ideas at the frontiers between systems-and-control theory and the life sciences, presented by renowned research teams worldwide. The scope of the special issue includes several complementary aspects, ranging from systems biology and synthetic biology (modeling, analysis, control, and design of biochemical and biological systems at all scales) to the analysis and management of ecological networks, from the study of biomedical systems (including disease management and the design and analysis of novel treatments) to the analysis and the control of epidemiological systems (embracing the study of their dynamics and approaches to suppress or mitigate the spread of contagion). Attention is devoted to modeling, identification, parameter estimation, as well as to analysis, control, and optimization. The special issue embraces both work with novel theoretical approaches and work that advances theoretical contributions with meaningful applications. The COVID-19 pandemic has highlighted the importance of mathematical and systems-and-control approaches to understand, manage, and control health emergencies, to predict the trend of epidemics and to plan effective strategies that curb the contagion. Indeed, several contributions of the special issue revolve around models for epidemics and infectious diseases and tackle their analysis and their control to suppress the contagion and eradicate infections, resorting to nonpharmaceutical interventions as well as vaccination. Eduardo D. Sontag proposes “An explicit formula for minimizing the infected peak in an SIR epidemic model when using a fixed number of complete lockdowns”; his work deals with the careful timing of nonpharmaceutical interventions, such as physical distancing, with the aim of minimizing the peak in the number of infected individuals, and provides an insightful, explicit, and easily computable rule for the timing of a fixed number of complete lockdowns of prespecified lengths. In their work “Change time estimation uncertainty in nonlinear dynamical systems with applications to COVID-19,” Rijad Alisic, Philip E. Paré, and Henrik Sandberg analyze the detectability of sudden parameter changes for nonlinear dynamical systems in the presence of measurement noise, so as to assess the impact of individual nonpharmaceutical interventions or viral mutations on the epidemic spread, and look for the most informative data sample allowing to discriminate at best between different output trajectories, revealing, for example, that monitoring the number of infected (instead of recovered/deceased) is preferable. “Semi-Markov models of epidemics over networks with time delays” are proposed by Mohammad Ghousein, Emmanuel Moulay, and Patrick Coirault, who modify Markov models for epidemics by including minimum viral incubation period and recovery period as time delays affecting the states, leading to a more general class of systems that loses the Markovian property; sufficient conditions for the global exponential stability of the resulting time-delay probabilistic dynamical models are offered via Lyapunov theory. “A time-varying network model for sexually transmitted infections accounting for behavior and control actions” is proposed by Kathinka Frieswijk, Lorenzo Zino, and Ming Cao, whose stochastic network model for the spread of sexually transmitted infections includes asymptomatic infections, as well as behavioral choices related to the use of protective measures and their time evolution depending on the perceived risk and on persuasion due to exchange of opinions; different control actions (awareness campaigns, routine screening, and partner notification) are investigated along with their effectiveness. Francesco Parino, Lorenzo Zino, Giuseppe C. Calafiore, and Alessandro Rizzo, in their article “A model predictive control approach to optimally devise a two-dose vaccination rollout: A case study on COVID-19 in Italy,” face the challenge of optimally planning massive two-dose vaccination rollouts by leveraging a nonlinear model predictive control strategy that accounts for both healthcare and socioeconomic costs, which they apply to the 2021 COVID-19 vaccination campaign in Italy by considering a tailored epidemic model with parameters inferred from real data. Moving from the between-host scale of epidemiological models that describe contagion spreading in a population to the in-host treatment of diseases, the paper “Scheduling collateral sensitivity-based cycling therapies toward eradication of drug-resistant infections,” by Josephine N. A. Tetteh, Sorin Olaru, Hans Crauel, and Esteban A. Hernandez-Vargas, explores the control-theoretic aspects and implications of facing drug-resistant pathogens through the sequential use of drugs where resistance to the former drug induces sensitivity to the next (collateral sensitivity); a switched system formulation allows to optimally tailor the order and time of cycling between drugs to the pathogen population present in the host. Systems-and-control theory offers powerful tools to gain insight into the behavior of biological systems, including their response to perturbations, their stability properties, and their ability to give rise to sustained and synchronized oscillations. In his paper “Sign-sensitivity of metabolic networks: Which structures determine the sign of the responses,” Nicola Vassena follows a structural approach to investigate the sign of the responses of metabolic networks to external perturbations, affecting both reaction rates and metabolite concentrations at the equilibrium, exclusively based on the network stoichiometry, without any information about the parameter values; subnetworks that have a crucial role in determining the sign sensitivity are identified and shown to be associated with kernel vectors of the stoichiometric matrix and thus independent of the chosen kinetics. Nicolas Augier, Madalena Chaves, and Jean-Luc Gouzé deal with “Weak synchronization and convergence in coupled genetic regulatory networks: Applications to damped oscillators and multistable circuits”: they consider a general model of genetic networks, compare the equilibria and stability properties of different interconnections based on either homogeneous or heterogeneous coupling, and find conditions for weak synchronization, applied to the synchronization of damped oscillators and to the control of multistable systems. Control-theoretic approaches allow us to govern, enhance, and improve biological phenomena at various scales, ranging from gene regulation and metabolism to the control of cell populations and their growth, and enables the design of biological mechanisms with the desired behavior. In a biological context, control strategies can aim at optimality, but most often need to face huge uncertainty and environmental variability, which requires fundamental robustness properties. Virginia Fusco, Davide Salzano, Davide Fiore, and Mario di Bernardo present a feedback strategy for the “Embedded control of cell growth using tuneable genetic systems”: they control the density of a microbial population, where cells self-regulate their growth rate, by tuning the production of a growth inhibitor protein based on the information on the population density obtained from a quorum sensing mechanism, they show that the set-point can be flexibly changed online, and they test the robustness of the designed controller with respect to disturbances and parameter variations due, for example, to cell-to-cell variability. “Self-regulation in a stochastic model of chemical self-replication” is investigated by Alessandro Borri, Massimiliano d'Angelo, and Pasquale Palumbo, who analyze burst noise propagation in the context of gene expression and investigate the impact of feedback on noise attenuation, both within a computational numerical framework based on a stochastic simulation algorithm and within an approximated analytical framework that replaces nonlinear terms with linear ones to enable closed-loop solutions for first- and second-order moments. Debojyoti Biswas, Sayak Bhattacharya, and Pablo A. Iglesias investigate “Enhanced chemotaxis through spatially regulated absolute concentration robustness” and introduce a control mechanism to enhance the efficiency of chemotaxis (the directional motility of cells in response to spatial chemical gradients) in amoeboid cells, whose movement is enabled by protrusions driven by the stochastic threshold crossing of an underlying excitable system, by suppressing undesirable protrusions in the wrong directions; the control action couples an absolute concentration robustness mechanism to the cellular signaling machinery. The work “Optimal control of probabilistic Boolean control networks: A scalable infinite horizon approach” by Sonam Kharade, Sarang Sutavani, Sushama Wagh, Amol Yerudkar, Carmen Del Vecchio, and Navdeep Singh deals with the optimal control of large-scale gene regulatory networks, modeled as probabilistic Boolean control networks based on Markov decision processes, with the aim of developing therapeutic intervention strategies that alter gene regulation dynamics so as to avoid diseased states. Nicolas Augier and Agustín G. Yabo deal with the “Time-optimal control of piecewise affine bistable gene-regulatory networks,” where the goal is to minimize the time that a piecewise affine bistable genetic toggle switch needs to transition between its two stable steady states, and show that time-optimal transitions pass through an undifferentiated state, which has fundamental importance in cell biology in relation to fate differentiation of cells and has applications in synthetic biology, biotechnology, and gene therapy. The paper “Optimal periodic resource allocation in reactive dynamical systems: application to Microalgal production” by Olivier Bernard, Liu-Di Lu, and Julien Salomon focuses on a periodic resource allocation problem applied to a dynamical system, which is inspired by the optimization of a mixing strategy to enhance the growth rate in a microalgal raceway system; the control is represented by a permutation applied on the system to reallocate the resources to the different activities. Estimating the state of a biological system based on experimentally measured quantities is fundamental. Alex dos Reis de Souza, Denis Efimov, Andrey Polyakov, Jean-Luc Gouzé, and Eugenio Cinquemani discuss “State observation in microbial consortia: A case study on a synthetic producer-cleaner consortium”; they frame an observability analysis and propose state estimators for each component of a system describing a two-strain microbial consortium that involves both producers and cleaners; they deal with observer design and they assess the advantage of measuring a fluorescent reporter in addition to the total biomass, even though measuring total biomass alone is enough to make the system detectable. System identification and the generation of reduced order models are of crucial importance in systems and synthetic biology. Francesco Montefusco, Anna Procopio, Declan G. Bates, Francesco Amato, and Carlo Cosentino tackle the “Scalable reverse-engineering of gene regulatory networks from time-course measurements”: they propose a computationally efficient method for topological inference of biological interaction networks from experimental data, which combines system identification based on instrumental variables with a regularization strategy to deal with over-parameterized systems, and can simultaneously exploit multiple time series from multiple experiments and be applied to large-scale networks with thousands of nodes. Gemma Massonis, Julio R. Banga, and Alejandro F. Villaverde propose “AutoRepar: a method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights”; their approach automatically re-parameterizes nonlinear ODE models to guarantee their structural identifiability and observability, so that the values of the model parameters and state variables can be inferred from data without ambiguities and the models can provide reliable predictions and insight. “Robustness guarantees for structured model reduction of dynamical systems with applications to biomolecular models” are offered by Ayush Pandey and Richard M. Murray, who focus on the robustness properties of the error in model reduction in the presence of uncertainties; they compute robustness guarantee metrics for a class of reduced models of engineered biological systems in uncertain environments, and propose an automated model reduction method to find the best possible reduced model, which is applied to gene expression systems with limited resources and to circuits for bacterial population control. Developing parameter identification, state estimation, and control approaches for biomedical systems is key to provide robust and theoretically grounded support to healthcare workers in the clinical practice. “Parameter identification and state estimation for a diabetic glucose-insulin model via an adaptive observer” are considered by Roberto Franco, Héctor Ríos, Alejandra Ferreira de Loza, Louis Cassany, David Gucik-Derigny, Jérôme Cieslak, and David Henry, who design an LMI-based adaptive observer for patients with type 1 diabetes mellitus, using Bergman's minimal model, to simultaneously estimate states and parameters corresponding to the insulin-dependent glucose disappearance rate in the presence of parameter uncertainties and food intake regarded as an external disturbance; the estimates are based on intravenous glucose measurements and the approach is validated in the UVA/Padova metabolic simulator. Luca Ranghetti, Daniel E. Rivera, Penghong Guo, Antonio Visioli, Jennifer Savage Williams, and Danielle Symons Downs discuss “A control-based observer approach for estimating energy intake during pregnancy” aimed at regulation of gestational weight so as to prevent risks for both the mother and her unborn child; an energy balance model predicts gestational weight based on physical activity and unmeasured disturbances associated with energy intake, and two different control-based observer formulations, relying on internal model control and model predictive control, are proposed and tested on real data. “Nonlinear dynamic modelling and model predictive control of thrombin generation to treat trauma-induced coagulopathy” are investigated by Damon E. Ghetmiri and Amor A. Menezes: they propose a nonlinear dynamic coagulation model including the complex biochemical reactions of clotting and demonstrate the effectiveness of a model predictive control scheme that administers blood proteins as system inputs to robustly automate clinical interventions for trauma-induced coagulopathy, even in the presence of experimentally observed uncertainties involving nonlinearities and delays. Two contributions deal with decision-making in populations and with the control of ecological systems. Leonardo Stella and Dario Bauso investigate “The impact of irrational behaviors in the optional prisoner's dilemma with game-environment feedback”, where players can choose to cooperate, defect or abstain, their behavior can be irrational according to prospect theory (capturing, e.g., reference dependence or loss aversion), and environmental effects exert a feedback over the population dynamics; the system is analyzed in the context of evolutionary games, with applications to opinion dynamics in society (elections) and collective decision-making in honeybee swarms. “Cyclic control equilibria for switched systems with applications to ecological systems” are discussed by Alejandro L. Anderson, Pablo Abuin, Antonio Ferramosca, Esteban A. Hernandez-Vargas, and Alejandro H. Gonzalez, who investigate permanence regions for switched systems under arbitrary waiting-time constraints and their (un)suitability as control targets, and propose a case study concerning population control and the stabilization of communities in an ecological system. We are deeply grateful to all the authors who contributed to this special issue by entrusting their valuable work and offering a rich and varied range of perspectives on “Control-Theoretic Approaches to Systems in the Life Sciences.” We also have heartful thanks for all the anonymous reviewers for their thorough assessment of submissions and for the insightful suggestions and comments they offered.