Cybernetic model predictive control of a continuous bioreactor with cell recycle

The control of poly-beta-hydroxybutyrate (PHB) productivity in a continuous bioreactor with cell recycle is studied by simulation. A cybernetic model of PHB synthesis in Alcaligenes eutrophus is developed. Model parameters are identified using experimental data, and simulation results are presented. The model is interfaced to a multirate model predictive control (MPC) algorithm. PHB productivity and concentration are controlled by manipulating dilution rate and recycle ratio. Unmeasured time varying disturbances are imposed to study regulatory control performance, including unreachable setpoints. With proper controller tuning, the nonlinear MPC algorithm can track productivity and concentration setpoints despite a change in the sign of PHB productivity gain with respect to dilution rate. It is shown that the nonlinear MPC algorithm is able to track the maximum achievable productivity for unreachable setpoints under significant process/model mismatch. The impact of model uncertainty upon controller performance is explored. The multirate MPC algorithm is tested using three controllers employing models that vary in complexity of regulation. It is shown that controller performance deteriorates as a function of decreasing biological complexity.     

A model predictive control based scheduling method for HIV therapy

Recently developed models of the interaction of the human immune system and the human immunodeficiency virus (HIV) suggest the possibility of using interruptions of highly active anti-retroviral therapy (HAART) to simulate a therapeutic vaccine and induce cytotoxic lymphocyte (CTL) mediated control of HIV infection. We have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. This method provides a clinically implementable framework for calculating interruption schedules that are robust to errors due to measurement and patient variations. In this paper, we discuss the medical motivation for this work, introduce the MPC-based method, show simulation results, and discuss future work necessary to implement the method.     

Integral rein control in physiology II: a general model

We generalize the principle of integral rein control to include other systems which partition in such a way that the equilibrium values of some variables are not dependent on the equations governing those variables. Instead, they are determined by the dynamics of other, "regulator" variables. We improve our earlier model for the control of glucose by insulin and glucagon by relaxing the condition necessary for it to operate. The two hormones do not have to be inhibited in the same way; they need only respond to the same combination of their concentrations. We also present a model for the control of ionized calcium by PTH and calcitonin and suggest that the role of chromogranin A may be to stabilize an otherwise unstable system.     

A predictive model of cell traction forces based on cell geometry

Recent work has indicated that the shape and size of a cell can influence how a cell spreads, develops focal adhesions, and exerts forces on the substrate. However, it is unclear how cell shape regulates these events. Here we present a computational model that uses cell shape to predict the magnitude and direction of forces generated by cells. The predicted results are compared to experimentally measured traction forces, and show that the model can predict traction force direction, relative magnitude, and force distribution within the cell using only cell shape as an input. Analysis of the model shows that the magnitude and direction of the traction force at a given point is proportional to the first moment of area about that point in the cell, suggesting that contractile forces within the cell act on the entire cytoskeletal network as a single cohesive unit. Through this model, we demonstrate that intrinsic properties of cell shape can facilitate changes in traction force patterns, independently of heterogeneous mechanical properties or signaling events within the cell.     

Neuroendocrine control of thyroid secretion in living systems: A feedback control system model

A model of the regulation of thyroid hormone in the bloodstream of living systems is formulated and analyzed. The portion of this model defined as theregulator includes components representing the thyroid, anterior pituitary and hypothalamic organs and their intercommunicating channels, that is, the peripheral plasma and hypophysial portal circulations and certain neuro-secretory connections. The loss of hormones from the plasma in the living system associated with physiological mechanisms within the peripheral tissue space and the excretory pathways is represented in the model by a lumpedload on the regulator. The model is reduced to a system of differential equations involving eleven parameters and variables, all of which are identified with certain physiological structures and states. Five of these are currently observable by available laboratory techniques and two others are computable explicity from the equations of the model; the remaining four can be computed in the same way to within a multiplicative constant. Procedires for carrying out ten of these measurements and calculations are suggested. On the basis of the equations and parameters of the model, a discussion of the normal behavior and the response of this system to certain types of disturbances is presented. A systematic effort has been made in the development of this model to include all relevant physiological data and relationships reported in the biological literature. A summary of this literature, reflecting the views and interpretations made by the authors of this paper, is included for completeness and ease of reference.     

A mathematical model for cell size control in fission yeast

Experimental investigations of cell size control in fission yeast Schizosaccharomyces pombe have illustrated that the cell cycle features ‘sizer’ and ‘timer’ phases which are distinguished by a growth rate changing point. Based on current biological knowledge of fission yeast size control, we propose here a model of ordinary differential equations (ODEs) for a possible explanation of the facts and control mechanism which is coupled with the cell cycle. Simulation results of the ODE model are demonstrated to agree with experimental data for the wild type and the cdc2-33 mutant. We show that the coupling of cell growth to cell division by translational control may account for observed properties of size control in fission yeast. As the translational control in the expression of cycle proteins Cdc13 and Cdc25 constructs positive feedback loops, the dynamical activities of the key components undergoes a rapid rising after a preliminary stage of slow increase. The coupling of this dynamical behavior to the elongation of the cell naturally gives rise to a rate change point and to ‘sizer’ and ‘timer’ phases, which characterize the cell cycle of fission yeast.     

A model for a non-chemical form of life: Crystalline physiology

A definition of fundamental living units is given according to which they are constituted by the material support of some ‘memory’ the latter is required

- to be stable,

- to contain rich information,

- to diffuse it into the surrounding medium.

It is then shown that the complex dislocation networks encountered in crystals can in some cases follow these criteria and lead to a crystalline physiology. The places of possible occurrence in nature of this kind of physiology, terrestrial and extraterrestrial rocks, interplanetary dust, white dwarfs and neutron stars are then discussed.     

A predictive model for life expectancy curves

Life expectancy curves have a characteristic ominous shape that has fascinated scientists for centuries. Medawar was the first to explain this shape, specifically the steeply rising proneness of an average individual to die as a function of age, in evolutionary terms. The idea was that the "selective value" of the individual decreases as it has triggered other individuals taking its place (and carrying its genes) into existence. We demonstrate that this idea can be turned into a quantitative model. The resulting 4-parameter function reproduces well two well-known life expectancy curves from the first half of this century. Moreover, the easily interpretable parameters (3 of the 4) seem intuitively reasonable.     

Spatiotemporal chemical dynamics in living cells: from information trafficking to cell physiology

Biological thought in the 20th century was dominated by the study of structures at increasingly minute levels. For biology to advance beyond structural reductionism and contribute its full measure to clinical care, living biological structures must be understood in the context of their collective chemical processes at the relevant chemical time-scales. Using high-speed fluorescence microscopy, we have studied intra- and inter-cellular signaling using shutter speeds ( approximately 100 ns) that remove the effects of wave motion and diffusion from optical images. By collecting a series of such images, stop-action movies of signal trafficking in living cells are created; these have revealed a new level of spatiotemporal chemical organization within cells. Numerous types of chemical waves have been found in living cells expressing a great variety of physical properties. In this article I will review some of these basic findings, discuss these events in the context of information trafficking, and illustrate the potential implications of this work in medicine.     

Machine learning-based model predictive controller design for cell culture processes

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost-effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed-batch cell culture processes. The lack of high-fidelity physics-based models and the high complexity of cell culture processes motivated us to use machine learning algorithms in the forecast model to aid our development. We took advantage of linear regression, the Gaussian process and neural network models in the MPC design to maximize the daily protein production for each batch. The control scheme of the cell culture process solves an optimization problem while maintaining all metabolites and cell culture process variables within the specification. The linear and nonlinear models are developed based on real cell culture process data, and the performance of the designed controllers is evaluated by running several real-time experiments.     

A predictive model for identifying proteins by a single peptide match

MOTIVATION: Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, > or = 2 unique peptides identified within a single protein are generally recommended. Still, in a typical high-throughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68-98% of the correct single-hit proteins with an error rate of < 2%. This results in a 22-65% increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach

By means of a model predictive control strategy it was possible to ensure a high batch-to-batch reproducibility in animal cell (CHO-cell) suspensions cultured for a recombinant therapeutic protein (EPO) production. The general control objective was derived by identifying an optimal specific growth rate taking productivity, protein quality and process controllability into account. This goal was approached indirectly by controlling the oxygen mass consumed by the cells which is related to specific biomass growth rate and cell concentration profile by manipulating the glutamine feed rate. Process knowledge represented by a classical model was incorporated into the model predictive control algorithm. The controller was employed in several cultivation experiments. During these cultivations, the model parameters were adapted after each sampling event to cope with changes in the process’ dynamics. The ability to predict the state variables, particularly for the oxygen consumption, led to only moderate changes in the desired optimal operational trajectories. Hence, nearly identical oxygen consumption profiles, cell and protein titers as well as sialylation patterns were obtained for all cultivation runs.