Non‐linear feedback control of the p 53 protein–mdm 2 inhibitor system using the derivative‐free non‐linear Kalman filter

It is proven that the model of the p 53–mdm 2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p 53 protein at the desirable levels. To estimate the non‐measurable elements of the state vector describing the p 53–mdm 2 system dynamics, the derivative‐free non‐linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p 53–mdm 2 system, the derivative‐free non‐linear Kalman filter is re‐designed as a disturbance observer. The derivative‐free non‐linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non‐linear model. The proposed non‐linear feedback control and perturbations compensation method for the p 53–mdm 2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.Inspec keywords: proteins, molecular biophysics, biochemistry, Kalman filters, inverse problems, perturbation theoryOther keywords: nonlinear feedback control, p53 protein‐mdm2 inhibitor system, derivative‐free nonlinear Kalman filter, differential flatness theory, protein synthesis loop, diffeomorphism, protein synthesis model, feedback control law, nonmeasurable elements, modelling uncertainties, inverse transformation, nonlinear model, perturbation compensation method, chemotherapy schemes, medication infusion     

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.     

Time‐invariant biological networks with feedback loops: structural equation models and structural identifiability

Quantitative analyses of biological networks such as key biological parameter estimation necessarily call for the use of graphical models. While biological networks with feedback loops are common in reality, the development of graphical model methods and tools that are capable of dealing with feedback loops is still in its infancy. Particularly, inadequate attention has been paid to the parameter identifiability problem for biological networks with feedback loops such that unreliable or even misleading parameter estimates may be obtained. In this study, the structural identifiability analysis problem of time‐invariant linear structural equation models (SEMs) with feedback loops is addressed, resulting in a general and efficient solution. The key idea is to combine Mason''s gain with Wright''s path coefficient method to generate identifiability equations, from which identifiability matrices are then derived to examine the structural identifiability of every single unknown parameter. The proposed method does not involve symbolic or expensive numerical computations, and is applicable to a broad range of time‐invariant linear SEMs with or without explicit latent variables, presenting a remarkable breakthrough in terms of generality. Finally, a subnetwork structure of the C. elegans neural network is used to illustrate the application of the authors’ method in practice.Inspec keywords: matrix algebra, least squares approximations, statistical analysis, parameter estimation, biologyOther keywords: structural identifiability analysis problem, time‐invariant linear structural equation models, feedback loops, identifiability equations, time‐invariant linear SEMs, time‐invariant biological networks, graphical model methods, parameter identifiability problem, biological parameter estimation, subnetwork structure, C. elegans neural network     

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     

Development of a novel particle size control system for hammer milling

In this paper, a novel feedback control system based on a fuzzy logic has been developed for controlling particle size of products in the dry grinding process. The system measured the particle size distribution in real time and controlled the particle size to maintain a desired set-point value. The validity of on-line particle size measurement was confirmed. It was concluded that the developed feedback system controlled the median diameter at ±5% of a desired set-point value under various desired set-point values and experimental disturbances.     

Metabolic buffer analysis reveals the simultaneous,independent control of ATP and adenylate energy ratios

Determining the underlying principles behind biological regulation is important for understanding the principles of life, treating complex diseases and creating de novo synthetic biology. Buffering—the use of reservoirs of molecules to maintain molecular concentrations—is a widespread and important mechanism for biological regulation. However, a lack of theory has limited our understanding of its roles and quantified effects. Here, we study buffering in energy metabolism using control theory and novel buffer analysis. We find that buffering can enable the simultaneous, independent control of multiple coupled outputs. In metabolism, adenylate kinase and AMP deaminase enable simultaneous control of ATP and adenylate energy ratios, while feedback on metabolic pathways is fundamentally limited to controlling one of these outputs. We also quantify the regulatory effects of the phosphagen system—the above buffers and creatine kinase—revealing which mechanisms regulate which outputs. The results are supported by human muscle and mouse adipocyte data. Together, these results illustrate the synergy of feedback and buffering in molecular biology to simultaneously control multiple outputs.     

Deriving control parameter settings from process models to control capsule fillers integrated into continuous manufacturing

The aim of this work was to investigate the mean fill weight control of a continuous capsule-filling process, whether it is possible to derive controller settings from an appendant process model. To that end, a system composed out of fully automated capsule filler and an online gravimetric scale was used to control the filled weight. This setup allows to examine challenges associated with continuous manufacturing processes, such as variations in the amount of active pharmaceutical ingredient (API) in the mixture due to fluctuations of the feeders or due to altered excipient batch qualities. Two types of controllers were investigated: a feedback control and a combination of feedback and feedforward control. Although both of those are common in the industry, determining the optimal parameter settings remains an issue. In this study, we developed a method to derive the control parameters based on process models in order to obtain optimal control for each filled product. Determined via rapid automated process development (RAPD), this method is an effective and fast way of determining control parameters. The method allowed us to optimize the weight control for three pharmaceutical excipients. By conducting experiments, we verified the feasibility of the proposed method and studied the dynamics of the controlled system. Our work provides important basic data on how capsule filler can be implemented into continuous manufacturing systems.     

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     

Multi‐bit Boolean model for chemotactic drift of Escherichia coli

Dynamic biological systems can be modelled to an equivalent modular structure using Boolean networks (BNs) due to their simple construction and relative ease of integration. The chemotaxis network of the bacterium Escherichia coli (E. coli ) is one of the most investigated biological systems. In this study, the authors developed a multi‐bit Boolean approach to model the drifting behaviour of the E. coli chemotaxis system. Their approach, which is slightly different than the conventional BNs, is designed to provide finer resolution to mimic high‐level functional behaviour. Using this approach, they simulated the transient and steady‐state responses of the chemoreceptor sensory module. Furthermore, they estimated the drift velocity under conditions of the exponential nutrient gradient. Their predictions on chemotactic drifting are in good agreement with the experimental measurements under similar input conditions. Taken together, by simulating chemotactic drifting, they propose that multi‐bit Boolean methodology can be used for modelling complex biological networks. Application of the method towards designing bio‐inspired systems such as nano‐bots is discussed.Inspec keywords: cell motility, microorganisms, Boolean functionsOther keywords: multibit Boolean approach, conventional BNs, high‐level functional behaviour, steady‐state responses, chemoreceptor sensory module, drift velocity, chemotactic drifting, multibit Boolean methodology, complex biological networks, bio‐inspired systems, multibit Boolean model, chemotactic drift, dynamic biological systems, equivalent modular structure, Boolean networks, simple construction, chemotaxis network, bacterium Escherichia coli, biological systems     

主动柔性耦合隔振系统的直接速度反馈实验研究

详细地介绍了柔性耦合隔振系统的组成和工作原理,然后通过系统辨识方法建立了柔性耦合系统的输入-输出动态耦合数学模型。在该模型基础上,应用直接速度反馈控制方法对柔性耦合系统进行控制,最后通过实验证明了直接速度反馈控制的有效性。     

Rolling balance board of adjustable geometry as a tool to assess balancing skill and to estimate reaction time delay

The relation between balancing performance and reaction time is investigated for human subjects balancing on rolling balance board of adjustable physical parameters: adjustable rolling radius R and adjustable board elevation h. A well-defined measure of balancing performance is whether a subject can or cannot balance on balance board with a given geometry (R, h). The balancing ability is linked to the stabilizability of the underlying two-degree-of-freedom mechanical model subject to a delayed proportional–derivative feedback control. Although different sensory perceptions involve different reaction times at different hierarchical feedback loops, their effect is modelled as a single lumped reaction time delay. Stabilizability is investigated in terms of the time delay in the mechanical model: if the delay is larger than a critical value (critical delay), then no stabilizing feedback control exists. Series of balancing trials by 15 human subjects show that it is more difficult to balance on balance board configuration associated with smaller critical delay, than on balance boards associated with larger critical delay. Experiments verify the feature of the mechanical model that a change in the rolling radius R results in larger change in the difficulty of the task than the same change in the board elevation h does. The rolling balance board characterized by the two well-defined parameters R and h can therefore be a useful device to assess human balancing skill and to estimate the corresponding lumped reaction time delay.     

采用滑动模态控制器的实时子结构试验等效力控制方法

等效力控制方法用反馈控制代替数学迭代过程求解实时子结构试验隐式逐步积分方法的非线性方程。对于非线性结构实时子结构试验的等效力控制方法,采用滑动模态控制器代替ＰＤ和ＰＩ控制器。滑动模态控制是一种非线性控制器,适合线性和非线性系统的控制。线性弹簧试件的实时子结构试验模拟和试验验证了这种控制方法的可行性.防屈曲支撑试件试验模拟和试验的结果表明,非线性系统的等效力控制方法试验,滑动模态控制有较好的控制效果。     

A Synthesis of Feedback and Feedforward Control for Process Improvement Under Stationary and Nonstationary Time Series Disturbance Models

Process adjustment strategy is an important part of the process improvement methods. The feedback control technique is used to compensate for the deviation of the output, and it has been intensively investigated. For continuous improvement and proactive strategies, feedback control has a delay and thus is not the ideal solution. In this article, motivated by a realistic manufacturing example, we propose the periodic shift disturbance models and investigate the feedforward control application from a new disturbance decomposition framework. We combine feedforward control with feedback control for maintaining the stability of the process and delivering products at target values. Then, we evaluate the performance of different control strategies for various disturbance models by using the output mean square error criterion. Sensitivity analysis of these control methods is made on different model parameter spaces, and robustness analysis for both model parameter and model structure misspecifications is presented. Two simulated examples show that the proposed control strategies can significantly reduce the variation of an evolving disturbance process. Copyright © 2014 John Wiley & Sons, Ltd.     

Biosynthesis of Silver Nanoparticles by Streptomyces griseorubens isolated from Soil and Their Antioxidant Activity

Microbial mediated biological synthesis of metallic nanoparticles was carried out ecofriendly in the present study. Silver nanoparticles (AgNPs) were extracellularly biosynthesised from Streptomyces griseorubens AU2 and extensively characterised by ultraviolet–visible (UV–vis) and Fourier transform infrared spectroscopy, high‐resolution transmission electron microscopy, scanning electron microscopy and X‐ray diffraction analysis. Elemental analysis of nanoparticles was also carried out using energy dispersive X‐ray spectroscopy. The biosynthesised AgNPs showed the characteristic absorption spectra in UV–vis at 422 nm which confirmed the presence of metallic AgNPs. According to the further characterisation analysis, the biosynthesised AgNPs were found to be spherical and crystalline particles with 5–20 nm average size. Antioxidant properties of the biosynthesised AgNPs were determined by 2,2‐diphenyl‐1‐picrylhydrazyl free radical scavenging assay and was found to increase in a dose‐dependent matter. The identification of the strain was determined by molecular characterisation method using 16s rDNA sequencing. The present study is the first report on the microbial biosynthesis of AgNPs using S. griseorubens isolated from soil and provides that the active biological components found in the cell‐free culture supernatant of S. griseorubens AU2 enable the synthesis of AgNPs.Inspec keywords: silver, microorganisms, nanoparticles, nanofabrication, DNA, molecular biophysics, ultraviolet spectra, visible spectra, scanning electron microscopy, Fourier transform infrared spectra, transmission electron microscopy, X‐ray diffraction, X‐ray chemical analysis, absorption coefficients, cellular biophysicsOther keywords: silver nanoparticles, Streptomyces griseorubens AU2, soil, antioxidant activity, microbial mediated biological synthesis, ultraviolet‐visible spectroscopy, Fourier transform infrared spectroscopy, UV‐vis spectroscopy, high‐resolution transmission electron microscopy, scanning electron microscopy, X‐ray diffraction, elemental analysis, energy dispersive X‐ray spectroscopy, absorption spectra, spherical particles, crystalline particles, 2,2‐diphenyl‐1‐picrylhydrazyl free radical scavenging assay, strain identification, molecular characterisation method, rDNA sequencing, active biological components, cell‐free culture supernatant, wavelength 422 nm, size 5 nm to 20 nm, Ag     

Enzyme-sharing as a cause of multi-stationarity in signalling systems

Multi-stationarity in biological systems is a mechanism of cellular decision-making. In particular, signalling pathways regulated by protein phosphorylation display features that facilitate a variety of responses to different biological inputs. The features that lead to multi-stationarity are of particular interest to determine, as well as the stability, properties of the steady states. In this paper, we determine conditions for the emergence of multi-stationarity in small motifs without feedback that repeatedly occur in signalling pathways. We derive an explicit mathematical relationship φ between the concentration of a chemical species at steady state and a conserved quantity of the system such as the total amount of substrate available. We show that φ determines the number of steady states and provides a necessary condition for a steady state to be stable—that is, to be biologically attainable. Further, we identify characteristics of the motifs that lead to multi-stationarity, and extend the view that multi-stationarity in signalling pathways arises from multi-site phosphorylation. Our approach relies on mass-action kinetics, and the conclusions are drawn in full generality without resorting to simulations or random generation of parameters. The approach is extensible to other systems.     

Theoretical cross‐comparative analysis on dynamics of small intestine and colon crypts during cancer initiation

Epigenetics is emerging as a fundamentally important area of biological and medical research that has implications for our understanding of human diseases including cancer, autoimmune and neuropsychiatric disorders. In the context of recent efforts on personalised medicine, a novel research direction is concerned with identification of intra‐individual epigenetic variation linked to disease predisposition and development, i.e. epigenome‐wide association studies. A computational model has been developed to describe the dynamics and structure of human intestinal crypts and to perform a comparative analysis on aberrant DNA methylation level induced in these during cancer initiation. The crypt framework, AgentCrypt, is an agent‐based model of crypt dynamics, which handles intra‐ and inter‐dependencies. In addition, the AgentCrypt model is used to investigate the effect of a set of potential inhibitors with respect to methylation modification in intestinal tissue during initiation of disease. Methylation level decrease over a relatively short period of 90 days is marked for the colon compared to the small intestine, although similar alterations are induced in both tissues. In addition, inhibitor effect is notable for abnormal crypt groups, with largest average methylation differences observed ≈0.75% lower in the colon and ≈0.79% lower in the small intestine with inhibitor present.Inspec keywords: cancer, tumours, biological organs, medical disorders, genetics, DNA, molecular biophysics, biochemistry, genomics, medical computing, cellular biophysicsOther keywords: theoretical cross‐comparative analysis, small intestine, colon crypts, cancer initiation, inherited genome regulation, biological research, medical research, human diseases, autoimmune disorders, neuropsychiatric disorders, personalised medicine, targeted treatments, intraindividual epigenetic variation, disease predisposition, epigenome‐wide association studies, computational model, human intestinal crypts, malignant systems, aberrant DNA methylation level, agent‐based model, AgentCrypt model, potential methylation inhibitors, methylation modifications, time 90 d     

Model-assisted feedback control for liquid composite molding

A model-assisted feedback control algorithm, a type of generic model control, is implemented to control cure in resin transfer molding. This control algorithm calculates an apparent temperature of reaction based on the cure data input form a sensor, and this temperature is used to compare the actual rate of reaction to the desired rate and to calculate the mold set-point temperature. The model input into the control algorithm is an empirical cure model of a pre-ceramic polymer with an Arrhenius temperature dependence from 55 to 95 °C. In this work, the effect of varying control parameters is evaluated through cure simulations and experiments. Also, the effect of noise on the controller robustness is evaluated through simulation and experiment. Control parameters are evaluated for 55 and 95 °C.     

Modeling the Dose of Home Dialysis

The growing interest in daily dialysis and combined continuous and intermittent dialysis treatments has created the need for a dialysis dosing model that is valid over a wide range of dosing frequency and intensity. Three models have been described for this purpose and are reviewed here. They have in common the concept of a continuous clearance value which is equivalent to the summed intermittent dialysis prescribed. The continuous clearance models all define a point on the saw-toothed blood urea nitrogen (BUN) concentration profile and calculate the continuous clearance required to achieve this at the same urea generation rate. The points modeled are the peak predialysis concentration (pkKt/V), the average Co (standard Kt/V, stdKt/V), and time-averaged urea concentration (TAC), which is termed equivalent renal clearance (EKRt/V). At the present time the only data for evaluation of clinical relevance of the three models is continuous ambulatory peritoneal dialysis (CAPD) outcome. The stdKt/V predicts that optimal CAPD outcome requires weekly stdKt/V 2.0, while the pkKt/V and EKRt/V models predict optimal doses of 1.8 and 3.0. These results suggest that the stdKt/V is the most realistic model, but data over much higher levels of therapy are not yet available to judge generalizability. The stdKt/V model was used to assess dose in two hemodialysis studies with 5 to 6 dialyses per week and showed that in one study the stdKt/V was only 2.0, while in the second study it was 5.6. These results show that dose can vary widely with a similar number of dialyses per week and point to the need for a generalized dosing model to guide and compare studies of daily home dialysis.