48 results found
Tonn M, Thomas P, Barahona M, et al., 2019, Stochastic modelling reveals mechanisms of metabolic heterogeneity, Communications Biology, Vol: 2, ISSN: 2399-3642
Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.
O'Day E, Hosta-Rigau L, Oyarzún DA, et al., 2019, Are we there yet? How and when specific biotechnologies will improve human health, Biotechnology Journal, Vol: 14, ISSN: 1860-6768
Patient X: A 67-year-old Caucasian man slips on a patch of ice. He has abrasions to his hands and has sustained significant damage to his hip. At the emergency room, he informs clinicians he takes atorvastatin, metformin, and glimepiride to treat hypertension and Type 2 Diabetes Mellitus (T2DM). X-rays reveal a fractured hip, which will require total hip replacement surgery.
Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic flows via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic flows and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions.
Liu D, Mannan AA, Han Y, et al., 2018, Dynamic metabolic control: towards precision engineering of metabolism, Journal of Industrial Microbiology and Biotechnology, Vol: 45, Pages: 535-543, ISSN: 1367-5435
Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.
de Lorenzo V, Prather KL, Chen G-Q, et al., 2018, The power of synthetic biology for bioproduction, remediation and pollution control, EMBO Reports, Vol: 19, ISSN: 1469-221X
Mannan AA, Liu D, Zhang F, et al., 2017, Fundamental design principles for transcription-factor-based metabolite biosensors, ACS Synthetic Biology, Vol: 6, Pages: 1851-1859, ISSN: 2161-5063
Metabolite biosensors are central to current efforts toward precision engineering of metabolism. Although most research has focused on building new biosensors, their tunability remains poorly understood and is fundamental for their broad applicability. Here we asked how genetic modifications shape the dose–response curve of biosensors based on metabolite-responsive transcription factors. Using the lac system in Escherichia coli as a model system, we built promoter libraries with variable operator sites that reveal interdependencies between biosensor dynamic range and response threshold. We developed a phenomenological theory to quantify such design constraints in biosensors with various architectures and tunable parameters. Our theory reveals a maximal achievable dynamic range and exposes tunable parameters for orthogonal control of dynamic range and response threshold. Our work sheds light on fundamental limits of synthetic biology designs and provides quantitative guidelines for biosensor design in applications such as dynamic pathway control, strain optimization, and real-time monitoring of metabolism.
Weisse AY, Mannan AA, Oyarzun DA, 2016, Signaling tug-of-war delivers the whole message, Cell Systems, Vol: 3, Pages: 414-46, ISSN: 2405-4720
How do cells transmit biochemical signals accurately? It turns out,pushing and pulling can go a long way.
Sootla A, Oyarzun DA, Angeli D, et al., 2016, Shaping Pulses to Control Bistable Systems: Analysis, Computation and Counterexamples, Automatica, Vol: 63, Pages: 254-264, ISSN: 1873-2836
In this paper we study how to shape temporal pulses to switch a bistable system between its stable steady states. Our motivation forpulse-based control comes from applications in synthetic biology, where it is generally difficult to implement real-time feedback controlsystems due to technical limitations in sensors and actuators. We show that for monotone bistable systems, the estimation of the set ofall pulses that switch the system reduces to the computation of one non-increasing curve. We provide an efficient algorithm to computethis curve and illustrate the results with a genetic bistable system commonly used in synthetic biology. We also extend these results tomodels with parametric uncertainty and provide a number of examples and counterexamples that demonstrate the power and limitationsof the current theory. In order to show the full potential of the framework, we consider the problem of inducing oscillations in a monotonebiochemical system using a combination of temporal pulses and event-based control. Our results provide an insight into the dynamics ofbistable systems under external inputs and open up numerous directions for future investigation.
Oyarzun DA, Chaves M, 2015, Design of a bistable switch to control cellular uptake, Journal of the Royal Society Interface, Vol: 20150618, ISSN: 1742-5689
Sootla A, Oyarzun DA, Angeli D, et al., 2015, Shaping Pulses to Control Bi-Stable Biological Systems, American Control Conference 2015, Publisher: IEEE, Pages: 3138-3143
In this paper, we present a framework for shaping pulses to control biological systems, and specifically systems in synthetic biology. By shaping we mean computing the magnitude and the length of a pulse, application of which results in reaching the desired control objective. Hence the control signals have only two parameters, which makes these signals amenable to wetlab implementations. We focus on the problem of switching between steady states in a bistable system. We show how to estimate the set of the switching pulses, if the trajectories of the controlled system can be bounded from above and below by the trajectories of monotone systems. We then generalise this result to systems with parametric uncertainty under some mild assumptions on the set of admissible parameters, thus providing some robustness guarantees. We illustrate the results on some example genetic circuits.
Weisse AY, Oyarzun DA, Danos V, et al., 2015, Mechanistic links between cellular trade-offs, gene expression, and growth, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 112, Pages: E1038-E1047, ISSN: 0027-8424
Waldherr S, Oyarzun DA, Bockmayr A, 2014, Dynamic optimization of metabolic networks coupled with gene expression, Journal of Theoretical Biology, Vol: 365, Pages: 469-485, ISSN: 1095-8541
Oyarzun DA, Lugagne J-B, Stan G-B, 2014, Noise propagation in synthetic gene circuits for metabolic control, ACS Synthetic Biology, Vol: 4, Pages: 116-125, ISSN: 2161-5063
Dynamic control of enzyme expression can be an effective strategy to engineer robust metabolic pathways. It allows a synthetic pathway to self-regulate in response to changes in bioreactor conditions or the metabolic state of the host. The implementation of this regulatory strategy requires gene circuits that couple metabolic signals with the genetic machinery, which is known to be noisy and one of the main sources of cell-to-cell variability. One of the unexplored design aspects of these circuits is the propagation of biochemical noise between enzyme expression and pathway activity. In this article, we quantify the impact of a synthetic feedback circuit on the noise in a metabolic product in order to propose design criteria to reduce cell-to-cell variability. We consider a stochastic model of a catalytic reaction under negative feedback from the product to enzyme expression. On the basis of stochastic simulations and analysis, we show that, depending on the repression strength and promoter strength, transcriptional repression of enzyme expression can amplify or attenuate the noise in the number of product molecules. We obtain analytic estimates for the metabolic noise as a function of the model parameters and show that noise amplification/attenuation is a structural property of the model. We derive an analytic condition on the parameters that lead to attenuation of metabolic noise, suggesting that a higher promoter sensitivity enlarges the parameter design space. In the theoretical case of a switch-like promoter, our analysis reveals that the ability of the circuit to attenuate noise is subject to a trade-off between the repression strength and promoter strength.
Lopez-Caamal F, Oyarzún DA, Middleton RH, et al., 2014, Spatial quantification of cytosolic Ca2+ accumulation in nonexcitable cells: ananalytical study, IEEE/ACM Transactions on Computational Biology and Bioinformatics, to appear.
Lugagne JB, Oyarzún DA, Stan GB, 2013, Stochastic simulation of enzymatic reactions under transcriptional feedback regulation, Proceedings of the European Control Conference, Pages: 3646-3651
Kuntz J, Oyarzún DA, Stan GB, 2013, Model reduction of genetic-metabolic systems using timescale separation, System Theoretic Approaches to Systems and Synthetic Biology (to appear), Publisher: Springer-Verlag
Vignoni A, Oyarzún DA, Pico J, et al., 2013, Control of protein concentrations in heterogeneous cell populations, Proceedings of the European Control Conference, Pages: 3633-3639
Oyarzun DA, Lopez-Caamal F, Garcia MR, et al., 2013, Cumulative signal transmission in nonlinear reaction-diffusion networks, Plos One, Vol: 5
Oyarzun DA, Chaves M, Hoff-Hoffmeyer-Zlotnik M, 2012, Multistability and oscillations in genetic control of metabolism, Journal of Theoretical Biology, Vol: 295, Pages: 139-153, ISSN: 0022-5193
Oyarzún DA, Stan GB, 2012, Synthetic gene circuits for metabolic control: design tradeoffs and constraints, Journal of the Royal Society Interface, Vol: 10
Lopez-Caamal F, Garcia MR, Oyarzun DA, et al., 2012, Analytic computation of the integrated response in nonlinear reaction-diffusion systems, Proceedings of the 51st IEEE Conference on Decision and Control, Pages: 1047-1052
Oyarzun DA, Stan GB, 2012, Design constraints in an operon circuit for engineered control of metabolic networks, Proceedings of the 51st IEEE Conference on Decision and Control, Pages: 3608-3613
Oyarzun DA, Stan GB, 2012, Design tradeoffs in a synthetic gene control circuit for metabolic networks, Proceedings of the 31st American Control Conference, Pages: 2743-2748
Krippendorff BF, Oyarzun DA, Huisinga W, 2012, Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modeling, Journal of Pharmacokinetics and Pharmacodynamics, Vol: 39
Oyarzun DA, Middleton RH, 2011, Optimal adaptation of metabolic networks in dynamic equilibrium., Proceedings of 29th American Control Conference, San Francisco, USA, Pages: 2897-2902
Oyarzun DA, 2011, Optimal control of metabolic networks with saturable enzyme kinetics, IET Systems Biology, Vol: 5, Pages: 110-119
Oyarzun DA, Chaves M, 2011, Global gene regulation in metabolic networks, Proceedings of the 18th IFAC World Congress, Milan, Italy, Pages: 14838-14843
Oyarzun DA, 2010, A control-theoretic approach to dynamic optimization of metabolic networks
Oyarzun DA, 2010, A control-theoretic approach to dynamic optimization of metabolic networks, Phd Thesis, National University of Ireland, Maynooth
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