Alexandros Kiparissides

The regulatory logic of m-xylene biodegradation by Pseudomonas putida mt-2 exposed by dynamic modelling of the principal node Ps/Pr of the TOL plasmid

The structure of the extant transcriptional control network of the TOL plasmid pWW0 born by Pseudomonas putida mt-2 for biodegradation of m-xylene is far more complex than one would consider necessary from a mere engineering point of view. In order to penetrate the underlying logic of such a network, which controls a major environmental cleanup bioprocess, we have developed a dynamic model of the key regulatory node formed by the Ps/Pr promoters of pWW0, where the clustering of control elements is maximal. The model layout was validated with batch cultures estimating parameter values and its predictive capability was confirmed with independent sets of experimental data. The model revealed how regulatory outputs originated in the divergent and overlapping Ps/Pr segment, which expresses the transcription factors XylS and XylR respectively, are computed into distinct instructions to the upper and lower catabolic xyl operons for either simultaneous or stepwise consumption of m-xylene and/or succinate. In this respect, the model reveals that the architecture of the Ps/Pr is poised to discriminate the abundance of alternative and competing C sources, in particular m-xylene versus succinate. The proposed framework provides a first systemic understanding of the causality and connectivity of the regulatory elements that shape this exemplary regulatory network, facilitating the use of model analysis towards genetic circuit optimization.

Improving Embryonic Stem Cell Expansion through the Combination of Perfusion and Bioprocess Model Design

Background High proliferative and differentiation capacity renders embryonic stem cells (ESCs) a promising cell source for tissue engineering and cell-based therapies. Harnessing their potential, however, requires well-designed, efficient and reproducible expansion and differentiation protocols as well as avoiding hazardous by-products, such as teratoma formation. Traditional, standard culture methodologies are fragmented and limited in their fed-batch feeding strategies that afford a sub-optimal environment for cellular metabolism. Herein, we investigate the impact of metabolic stress as a result of inefficient feeding utilizing a novel perfusion bioreactor and a mathematical model to achieve bioprocess improvement. Methodology/Principal Findings To characterize nutritional requirements, the expansion of undifferentiated murine ESCs (mESCs) encapsulated in hydrogels was performed in batch and perfusion cultures using bioreactors. Despite sufficient nutrient and growth factor provision, the accumulation of inhibitory metabolites resulted in the unscheduled differentiation of mESCs and a decline in their cell numbers in the batch cultures. In contrast, perfusion cultures maintained metabolite concentration below toxic levels, resulting in the robust expansion (>16-fold) of high quality ‘naïve’ mESCs within 4 days. A multi-scale mathematical model describing population segregated growth kinetics, metabolism and the expression of selected pluripotency (‘stemness’) genes was implemented to maximize information from available experimental data. A global sensitivity analysis (GSA) was employed that identified significant (6/29) model parameters and enabled model validation. Predicting the preferential propagation of undifferentiated ESCs in perfusion culture conditions demonstrates synchrony between theory and experiment. Conclusions/Significance The limitations of batch culture highlight the importance of cellular metabolism in maintaining pluripotency, which necessitates the design of suitable ESC bioprocesses. We propose a novel investigational framework that integrates a novel perfusion culture platform (controlled metabolic conditions) with mathematical modeling (information maximization) to enhance ESC bioprocess productivity and facilitate bioprocess optimization.

Modelling the Delta1/Notch1 pathway: in search of the mediator(s) of neural stem cell differentiation.

The Notch1 signalling pathway has been shown to control neural stem cell fate through lateral inhibition of mash1, a key promoter of neuronal differentiation. Interaction between the Delta1 ligand of a differentiating cell and the Notch1 protein of a neighbouring cell results in cleavage of the trans-membrane protein, releasing the intracellular domain (NICD) leading to the up regulation of hes1. Hes1 homodimerisation leads to down regulation of mash1. Most mathematical models currently represent this pathway up to the formation of the HES1 dimer. Herein, we present a detailed model ranging from the cleavage of the NICD and how this signal propagates through the Delta1/Notch1 pathway to repress the expression of the proneural genes. Consistent with the current literature, we assume that cells at the self renewal state are represented by a stable limit cycle and through in silico experimentation we conclude that a drastic change in the main pathway is required in order for the transition from self-renewal to differentiation to take place. Specifically, a model analysis based approach is utilised in order to generate hypotheses regarding potential mediators of this change. Through this process of model based hypotheses generation and testing, the degradation rates of Hes1 and Mash1 mRNA and the dissociation constant of Mash1-E47 heterodimers are identified as the most potent mediators of the transition towards neural differentiation.

Linking genes to microbial growth kinetics: an integrated biochemical systems engineering approach

The majority of models describing the kinetic properties of a microorganism for a given substrate are unstructured and empirical. They are formulated in this manner so that the complex mechanism of cell growth is simplified. Herein, a novel approach for modelling microbial growth kinetics is proposed, linking biomass growth and substrate consumption rates to the gene regulatory programmes that control these processes. A dynamic model of the TOL (pWW0) plasmid of Pseudomonas putida mt-2 has been developed, describing the molecular interactions that lead to the transcription of the upper and meta operons, known to produce the enzymes for the oxidative catabolism of m-xylene. The genetic circuit model was combined with a growth kinetic model decoupling biomass growth and substrate consumption rates, which are expressed as independent functions of the rate-limiting enzymes produced by the operons. Estimation of model parameters and validation of the models predictive capability were successfully performed in batch cultures of mt-2 fed with different concentrations of m-xylene, as confirmed by relative mRNA concentration measurements of the promoters encoded in TOL. The growth formation and substrate utilisation patterns could not be accurately described by traditional Monod-type models for a wide range of conditions, demonstrating the critical importance of gene regulation for the development of advanced models closely predicting complex bioprocesses. In contrast, the proposed strategy, which utilises quantitative information pertaining to upstream molecular events that control the production of rate-limiting enzymes, predicts the catabolism of a substrate and biomass formation and could be of central importance for the design of optimal bioprocesses.

Bioprocess systems engineering: transferring traditional process engineering principles to industrial biotechnology

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals.

On the model‐based optimization of secreting mammalian cell (GS‐NS0) cultures

The global bio‐manufacturing industry requires improved process efficiency to satisfy the increasing demands for biochemicals, biofuels, and biologics. The use of model‐based techniques can facilitate the reduction of unnecessary experimentation and reduce labor and operating costs by identifying the most informative experiments and providing strategies to optimize the bioprocess at hand. Herein, we investigate the potential of a research methodology that combines model development, parameter estimation, global sensitivity analysis, and selection of optimal feeding policies via dynamic optimization methods to improve the efficiency of an industrially relevant bioprocess. Data from a set of batch experiments was used to estimate values for the parameters of an unstructured model describing monoclonal antibody (mAb) production in GS‐NS0 cell cultures. Global Sensitivity Analysis (GSA) highlighted parameters with a strong effect on the model output and data from a fed‐batch experiment were used to refine their estimated values. Model‐based optimization was used to identify a feeding regime that maximized final mAb titer. An independent fed‐batch experiment was conducted to validate both the results of the optimization and the predictive capabilities of the developed model. The successful integration of wet‐lab experimentation and mathematical model development, analysis, and optimization represents a unique, novel, and interdisciplinary approach that addresses the complicated research and industrial problem of model‐based optimization of cell based processes. Biotechnol. Bioeng. 2015;112: 536–548.

An in silico erythropoiesis model rationalizing synergism between stem cell factor and erythropoietin

Stem cell factor (SCF) and erythropoietin (EPO) are two most recognized growth factors that play in concert to control in vitro erythropoiesis. However, exact mechanisms underlying the interplay of these growth factors in vitro remain unclear. We developed a mathematical model to study co-signaling effects of SCF and EPO utilizing the ERK1/2 and GATA-1 pathways (activated by SCF and EPO) that drive the proliferation and differentiation of erythroid progenitors. The model was simplified and formulated based on three key features: synergistic contribution of SCF and EPO on ERK1/2 activation, positive feedback effects on proliferation and differentiation, and cross-inhibition effects of activated ERK1/2 and GATA-1. The model characteristics were developed to correspond with biological observations made known thus far. Our simulation suggested that activated GATA-1 has a more dominant cross-inhibition effect and stronger positive feedback response on differentiation than the proliferation pathway, while SCF contributed more to the activation of ERK1/2 than EPO. A sensitivity analysis performed to gauge the dynamics of the system was able to identify the most sensitive model parameters and illustrated a contribution of transient activity in EPO ligand to growth factor synergism. Based on theoretical arguments, we have successfully developed a model that can simulate growth factor synergism observed in vitro for erythropoiesis. This hypothesized model can be applied to further computational studies in biological systems where synergistic effects of two ligands are seen.

A combined growth kinetics, metabolism and gene expression model for 3D ESC bioprocesses

Abstract Exploiting the immense potential of embryonic stem cells (ESC) lies in controlling their differentiation towards the desired cell type. Cell culture variables are known to affect ESC pluripotency, one of its defining characteristics. Therefore, we develop a mathematical model coupling cell growth, metabolism and gene expression for an ESC expansion bioprocess. Batch cultures were performed to obtain estimates for model parameters; however, they were unable to maintain cell proliferation and pluripotency levels. When perfusion feeding is administered to the expansion bioprocess, we observe different metabolic characteristics; hence we use global sensitivity analysis (GSA) to identify the most significant model parameters for re-estimation during dynamic feeding conditions. Perfusion feeding negates the sub-optimal nutrient/metabolite conditions found in batch cultures, facilitates the expansion of ESCs and maintains maximal pluripotency levels. The mathematical model herein is able to capture the experimental observations closely for batch and perfusion experiments. We highlight the importance of optimal nutrient/metabolite culture conditions during ESC bioprocess and initiate the development of an in silico design tool for their optimization.

Towards energy-based dynamic optimization of monoclonal antibody producing GS-NS0 Cultures

Abstract Mammalian cell culture systems produce clinically important high-value biologics, such as monoclonal antibodies (mAb). Cell lines transfected with the Glutamine Synthetase (GS) gene are amongst the most industrially significant mAb production systems due to the high yields they achieve. Metabolic models of GS culture systems presented thus far take into account only glucose as a growth limiting nutrient, neglecting the fact that in the absence of glutamine in the media, glutamate becomes a necessary dietary component in GS systems. Previously, we have presented the development of a systematic framework for modelling of mammalian cell bioprocesses. Herein, we present, for the first time, the development of a dynamic model describing growth and monoclonal antibody formation in GS-NS0 cell cultures that interlinks cellular growth rate with the availability of both glucose and glutamate. This is the first step, of many, towards the derivation of a dynamic model that interlinks the availability of ATP, through the dietary intake of the cell, to its growth and productivity characteristics. Such a model would facilitate the derivation of an optimal feeding profile, constraining the amount of provided energy through the feed to the required minimal, hence avoiding the excessive feeding of glucose which in turn shifts metabolism towards energy inefficient pathways.

Combining Genetic Circuit and Microbial Growth Kinetic Models: A Challenge for Biological Modelling

Abstract A modelling framework that consists of model building, validation and analysis, leading to model-based design of experiments and to the application of optimisation-based model-predictive control strategies for the development of optimised bioprocesses is presented. An example of this framework is given with the construction and experimental validation of a dynamic mathematical model of the Ps/Pr promoters system of the TOL plasmid, which is used for the metabolism of m -xylene by Pseudomonas putida mt-2. Furthermore, the genetic circuit model is combined with the growth kinetics of the strain in batch cultures, demonstrating how the description of key genetic circuits can facilitate the improvement of existing growth kinetic models that fail to predict unusual growth patterns. Consequently, the dynamic model is combined with global sensitivity analysis, which is used to identify the presence of significant model parameters, constituting a model-based methodology for the formulation of genetic circuit optimization methods.