A feedback control principle common to several biological and engineered systems

Feedback control is used by many distributed systems to optimize behaviour. Traditional feedback control algorithms spend significant resources to constantly sense and stabilize a continuous control variable of interest, such as vehicle speed for implementing cruise control, or body temperature for maintaining homeostasis. By contrast, discrete-event feedback (e.g. a server acknowledging when data are successfully transmitted, or a brief antennal interaction when an ant returns to the nest after successful foraging) can reduce costs associated with monitoring a continuous variable; however, optimizing behaviour in this setting requires alternative strategies. Here, we studied parallels between discrete-event feedback control strategies in biological and engineered systems. We found that two common engineering rules—additive-increase, upon positive feedback, and multiplicative-decrease, upon negative feedback, and multiplicative-increase multiplicative-decrease—are used by diverse biological systems, including for regulating foraging by harvester ant colonies, for maintaining cell-size homeostasis, and for synaptic learning and adaptation in neural circuits. These rules support several goals of these systems, including optimizing efficiency (i.e. using all available resources); splitting resources fairly among cooperating agents, or conversely, acquiring resources quickly among competing agents; and minimizing the latency of responses, especially when conditions change. We hypothesize that theoretical frameworks from distributed computing may offer new ways to analyse adaptation behaviour of biology systems, and in return, biological strategies may inspire new algorithms for discrete-event feedback control in engineering.     

Dichotomous feedback: a signal sequestration-based feedback mechanism for biocontroller design

We introduce a new design framework for implementing negative feedback regulation in synthetic biology, which we term ‘dichotomous feedback’. Our approach is different from current methods, in that it sequesters existing fluxes in the process to be controlled, and in this way takes advantage of the process’s architecture to design the control law. This signal sequestration mechanism appears in many natural biological systems and can potentially be easier to realize than ‘molecular sequestration’ and other comparison motifs that are nowadays common in biomolecular feedback control design. The loop is closed by linking the strength of signal sequestration to the process output. Our feedback regulation mechanism is motivated by two-component signalling systems, where a second response regulator could be competing with the natural response regulator thus sequestering kinase activity. Here, dichotomous feedback is established by increasing the concentration of the second response regulator as the level of the output of the natural process increases. Extensive analysis demonstrates how this type of feedback shapes the signal response, attenuates intrinsic noise while increasing robustness and reducing crosstalk.     

Simplified mechanistic models of gene regulation for analysis and design

Simplified mechanistic models of gene regulation are fundamental to systems biology and essential for synthetic biology. However, conventional simplified models typically have outputs that are not directly measurable and are based on assumptions that do not often hold under experimental conditions. To resolve these issues, we propose a ‘model reduction’ methodology and simplified kinetic models of total mRNA and total protein concentration, which link measurements, models and biochemical mechanisms. The proposed approach is based on assumptions that hold generally and include typical cases in systems and synthetic biology where conventional models do not hold. We use novel assumptions regarding the ‘speed of reactions’, which are required for the methodology to be consistent with experimental data. We also apply the methodology to propose simplified models of gene regulation in the presence of multiple protein binding sites, providing both biological insights and an illustration of the generality of the methodology. Lastly, we show that modelling total protein concentration allows us to address key questions on gene regulation, such as efficiency, burden, competition and modularity.     

Small RNA‐mediated switch‐like regulation in bacterial quorum sensing

Quorum sensing (QS) is a signalling mechanism by which bacteria produce, release and then detect and respond to changes in their density and biosignals called autoinducers (AIs). There are multiple feedback loops in the QS system of Vibrio harveyi. However, how these feedback loops function to control signal processing remains unclear. In this study, the authors present a computational model for the switch‐like regulation of signal transduction by small regulatory RNA‐mediated QS based on intertwined network involving AIs, LuxO, LuxU, Qrr sRNAs and LuxR. In agreement with experimental observations, the model suggests that different feedbacks play critical roles in the switch‐like regulation. The authors results reveal that V. harveyi uses multiple feedbacks to precisely control signal transduction.Inspec keywords: biocommunications, biocontrol, biology computing, cellular biophysics, physiological models, RNAOther keywords: RNA‐mediated switch‐like regulation, bacterial quorum sensing, signaling mechanism, autoinducers, Vibrio harveyi, feedback loops function, signal processing control, switch‐like regulation     

Identification of intermittent control in man and machine

Regulation by negative feedback is fundamental to engineering and biological processes. Biological regulation is usually explained using continuous feedback models from both classical and modern control theory. An alternative control paradigm, intermittent control, has also been suggested as a model for biological control systems, particularly those involving the central nervous system. However, at present, there is no identification method explicitly formulated to distinguish intermittent from continuous control; here, we present such a method. The identification experiment uses a special paired-step set-point sequence. The corresponding data analysis use a conventional ARMA model to relate a theoretically derived equivalent set-point to control signal; the novelty lies in sequentially and iteratively adjusting the timing of the steps of this equivalent set-point to optimize the linear time-invariant fit. The method was verified using realistic simulation data and was found to robustly distinguish not only between continuous and intermittent control but also between event-driven intermittent and clock-driven intermittent control. When applied to human pursuit tracking, event-driven intermittent control was identified, with an intermittent interval of 260–310 ms (n = 6, p < 0.05). This new identification method is applicable for machine and biological applications.     

基于增益调度的结构振动分布式反馈控制

使用多传感器和驱动器控制简支板的振动.传感器和驱动器局部连接组成独立的闭环.在仿真中分别采用了点力驱动器和压电陶瓷驱动器.以极小化系统的总动能为目标,使用同步扰动方法对结构振动的分布式反馈增益进行最优调度.仿真结果表明,所提出的方法可以有效地抑制结构振动.     

Knowing one's place: a free-energy approach to pattern regulation

Understanding how organisms establish their form during embryogenesis and regeneration represents a major knowledge gap in biological pattern formation. It has been recently suggested that morphogenesis could be understood in terms of cellular information processing and the ability of cell groups to model shape. Here, we offer a proof of principle that self-assembly is an emergent property of cells that share a common (genetic and epigenetic) model of organismal form. This behaviour is formulated in terms of variational free-energy minimization—of the sort that has been used to explain action and perception in neuroscience. In brief, casting the minimization of thermodynamic free energy in terms of variational free energy allows one to interpret (the dynamics of) a system as inferring the causes of its inputs—and acting to resolve uncertainty about those causes. This novel perspective on the coordination of migration and differentiation of cells suggests an interpretation of genetic codes as parametrizing a generative model—predicting the signals sensed by cells in the target morphology—and epigenetic processes as the subsequent inversion of that model. This theoretical formulation may complement bottom-up strategies—that currently focus on molecular pathways—with (constructivist) top-down approaches that have proved themselves in neuroscience and cybernetics.     

Biological pest control using a model‐based robust feedback

Biological control is the artificial manipulation of natural enemies of a pest for its regulation to densities below a threshold for economic damage. The authors address the biological control of a class of pest population models using a model‐based robust feedback approach. The proposed control framework is based on a recursive cascade control scheme exploiting the chained form of pest population models and the use of virtual inputs. The robust feedback is formulated considering the non‐linear model uncertainties via a simple and intuitive control design. Numerical results on three pest biological control problems show that the proposed model‐based robust feedback can regulate the pest population at the desired reference via the manipulation of a biological control action despite model uncertainties.Inspec keywords: cascade control, control system synthesis, nonlinear control systems, feedback, robust control, pest control, manipulatorsOther keywords: biological pest control, artificial manipulation, natural enemies, pest population models, robust feedback approach, recursive cascade control scheme, nonlinear model uncertainties, simple control design, intuitive control design, pest biological control problems, biological control action     

Crosstalk between pathways enhances the controllability of signalling networks

The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow‐tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross‐pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors’ work provides a comprehensive understanding of the impact of network structures and properties on controllability.Inspec keywords: genetics, numerical analysis, control theoryOther keywords: signalling network controllability, interconnected pathways, bow‐tie architecture, structure controllability, biological mechanisms, cross‐pathway interactions, numerical simulations, biological networks, control theory, gene regulatory network     

Impact of negative feedback in metabolic noise propagation

Synthetic biology combines different branches of biology and engineering aimed at designing synthetic biological circuits able to replicate emergent properties useful for the biotechnology industry, human health and environment. The role of negative feedback in noise propagation for a basic enzymatic reaction scheme is investigated. Two feedback control schemes on enzyme expression are considered: one from the final product of the pathway activity, the other from the enzyme accumulation. Both schemes are designed to provide the same steady‐state average values of the involved players, in order to evaluate the feedback performances according to the same working mode. Computations are carried out numerically and analytically, the latter allowing to infer information on which model parameter setting leads to a more efficient noise attenuation, according to the chosen scheme. In addition to highlighting the role of the feedback in providing a substantial noise reduction, our investigation concludes that the effect of feedback is enhanced by increasing the promoter sensitivity for both schemes. A further interesting biological insight is that an increase in the promoter sensitivity provides more benefits to the feedback from the product with respect to the feedback from the enzyme, in terms of enlarging the parameter design space.Inspec keywords: biotechnology, enzymes, biological techniquesOther keywords: negative feedback impact, metabolic noise propagation, mathematical modelling, synthetic biological circuit, biotechnology industry, human health, environment, enzymatic reaction scheme, feedback control scheme, enzyme expression, enzyme accumulation, negative autoregulation, steady‐state average value, feedback performance, stochastic simulation algorithm, stochastic hybrid system modelling, noise attenuation, substantial noise reduction, feedback effect, parameter design space     

Anatomy of a negative feedback loop: the case of IκBα

The magnitude, duration and oscillation of cellular signalling pathway responses are often limited by negative feedback loops, defined as an ‘activator-induced inhibitor’ regulatory motif. Within the NFκB signalling pathway, a key negative feedback regulator is IκBα. We show here that, contrary to current understanding, NFκB-inducible expression is not sufficient for providing effective negative feedback. We then employ computational simulations of NFκB signalling to identify IκBα molecular properties that are critical for proper negative feedback control and test the resulting predictions in biochemical and single-cell live-imaging studies. We identified nuclear import and nuclear export of IκBα and the IκBα–NFκB complex, as well as the free IκBα half-life, as key determinants of post-induction repression of NFκB and the potential for subsequent reactivation. Our work emphasizes that negative feedback is an emergent systems property determined by multiple molecular and biophysical properties in addition to the required ‘activator-induced inhibitor’ relationship.     

Systems biology approaches to understanding stem cell fate choice

Stem cells have the capability to self-renew and maintain their undifferentiated state or to differentiate into one or more specialised cell types. Stem cell expansion and manipulation ex vivo is a promising approach for engineering cell replacement therapies, and endogenous stem cells represent potential drugable targets for tissue repair. Before we can harness stem cells' therapeutic potential, we must first understand the intracellular mechanisms controlling their fate choices. These mechanisms involve complex signal transduction and gene regulation networks that feature, for example, intricate feed-forward loops, feedback loops and cross-talk between multiple signalling pathways. Systems biology applies computational and experimental approaches to investigate the emergent behaviour of collections of molecules and strives to explain how these numerous components interact to regulate molecular, cellular and organismal behaviour. Here we review systems biology, and in particular computational, efforts to understand the intracellular mechanisms of stem cell fate choice. We first discuss deterministic and stochastic models that synthesise molecular knowledge into mathematical formalism, enable simulation of important system behaviours and stimulate further experimentation. In addition, statistical analyses such as Bayesian networks and principal components analysis (PCA)/partial least squares (PLS) regression can distill large datasets into more readily managed networks and principal components that provide insights into the critical aspects and components of regulatory networks. Collectively, integrating modelling with experimentation has strong potential for enabling a deeper understanding of stem cell fate choice and thereby aiding the development of therapies to harness stem cells' therapeutic potential.     

Phosphorylation and sulfation share a common biosynthetic pathway,but extend biochemical and evolutionary diversity of biological macromolecules in distinct ways

Phosphate and sulfate groups are integral to energy metabolism and introduce negative charges into biological macromolecules. One purpose of such modifications is to elicit precise binding/activation of protein partners. The physico-chemical properties of the two groups, while superficially similar, differ in one important respect—the valency of the central (phosphorus or sulfur) atom. This dictates the distinct properties of their respective esters, di-esters and hence their charges, interactions with metal ions and their solubility. These, in turn, determine the contrasting roles for which each group has evolved in biological systems. Biosynthetic links exist between the two modifications; the sulfate donor 3′-phosphoadenosine-5′-phosphosulfate being formed from adenosine triphosphate (ATP) and adenosine phosphosulfate, while the latter is generated from sulfate anions and ATP. Furthermore, phosphorylation, by a xylosyl kinase (Fam20B, glycosaminoglycan xylosylkinase) of the xylose residue of the tetrasaccharide linker region that connects nascent glycosaminoglycan (GAG) chains to their parent proteoglycans, substantially accelerates their biosynthesis. Following observations that GAG chains can enter the cell nucleus, it is hypothesized that sulfated GAGs could influence events in the nucleus, which would complete a feedback loop uniting the complementary anionic modifications of phosphorylation and sulfation through complex, inter-connected signalling networks and warrants further exploration.     

Nanotransducers for Near‐Infrared Photoregulation in Biomedicine

Photoregulation, which utilizes light to remotely control biological events, provides a precise way to decipher biology and innovate in medicine; however, its potential is limited by the shallow tissue penetration and/or phototoxicity of ultraviolet (UV)/visible light that are required to match the optical responses of endogenous photosensitive substances. Thereby, biologically friendly near‐infrared (NIR) light with improved tissue penetration is desired for photoregulation. Since there are a few endogenous biomolecules absorbing or emitting light in the NIR region, the development of molecular transducers is essential to convert NIR light into the cues for regulation of biological events. In this regard, optical nanomaterials able to convert NIR light into UV/visible light, heat, or free radicals are suitable for this task. Here, the recent developments of optical nanotransducers for NIR‐light‐mediated photoregulation in medicine are summarized. The emerging applications, including photoregulation of neural activity, gene expression, and visual systems, as well as photochemical tissue bonding, are highlighted, along with the design principles of nanotransducers. Moreover, the current challenges and perspectives in this field are discussed.     

Future climates: Markov blankets and active inference in the biosphere

We formalize the Gaia hypothesis about the Earth climate system using advances in theoretical biology based on the minimization of variational free energy. This amounts to the claim that non-equilibrium steady-state dynamics—that underwrite our climate—depend on the Earth system possessing a Markov blanket. Our formalization rests on how the metabolic rates of the biosphere (understood as Markov blanket''s internal states) change with respect to solar radiation at the Earth''s surface (i.e. external states), through the changes in greenhouse and albedo effects (i.e. active states) and ocean-driven global temperature changes (i.e. sensory states). Describing the interaction between the metabolic rates and solar radiation as climatic states—in a Markov blanket—amounts to describing the dynamics of the internal states as actively inferring external states. This underwrites climatic non-equilibrium steady-state through free energy minimization and thus a form of planetary autopoiesis.     

A composite computational model of liver glucose homeostasis. I. Building the composite model

A computational model of the glucagon/insulin-driven liver glucohomeostasis function, focusing on the buffering of glucose into glycogen, has been developed. The model exemplifies an ‘engineering’ approach to modelling in systems biology, and was produced by linking together seven component models of separate aspects of the physiology. The component models use a variety of modelling paradigms and degrees of simplification. Model parameters were determined by an iterative hybrid of fitting to high-scale physiological data, and determination from small-scale in vitro experiments or molecular biological techniques. The component models were not originally designed for inclusion within such a composite model, but were integrated, with modification, using our published modelling software and computational frameworks. This approach facilitates the development of large and complex composite models, although, inevitably, some compromises must be made when composing the individual models. Composite models of this form have not previously been demonstrated.