Stochastic simulations of the tetracycline operon

<p>Abstract</p> <p>Background</p> <p>The tetracycline operon is a self-regulated system. It is found naturally in bacteria where it confers resistance to antibiotic tetracycline. Because of the performance of the molecular elements of the tetracycline operon, these elements are widely used as parts of synthetic gene networks where the protein production can be efficiently turned on and off in response to the presence or the absence of tetracycline. In this paper, we investigate the dynamics of the tetracycline operon. To this end, we develop a mathematical model guided by experimental findings. Our model consists of biochemical reactions that capture the biomolecular interactions of this intriguing system. Having in mind that small biological systems are subjects to stochasticity, we use a stochastic algorithm to simulate the tetracycline operon behavior. A sensitivity analysis of two critical parameters embodied this system is also performed providing a useful understanding of the function of this system.</p> <p>Results</p> <p>Simulations generate a timeline of biomolecular events that confer resistance to bacteria against tetracycline. We monitor the amounts of intracellular TetR2 and TetA proteins, the two important regulatory and resistance molecules, as a function of intrecellular tetracycline. We find that lack of one of the promoters of the tetracycline operon has no influence on the total behavior of this system inferring that this promoter is not essential for <it>Escherichia coli</it>. Sensitivity analysis with respect to the binding strength of tetracycline to repressor and of repressor to operators suggests that these two parameters play a predominant role in the behavior of the system. The results of the simulations agree well with experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular TetR2 amounts.</p> <p>Conclusions</p> <p>Computer simulations of the tetracycline operon afford augmented insight into the interplay between its molecular components. They provide useful explanations of how the components and their interactions have evolved to best serve bacteria carrying this operon. Therefore, simulations may assist in designing novel gene network architectures consisting of tetracycline operon components.</p

Multiscale Hy3S: Hybrid stochastic simulation for supercomputers

BACKGROUND: Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. RESULTS: Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users create biological systems and analyze data. We demonstrate the accuracy and efficiency of Hy3S with examples, including a large-scale system benchmark and a complex bistable biochemical network with positive feedback. The software itself is open-sourced under the GPL license and is modular, allowing users to modify it for their own purposes. CONCLUSION: Hy3S is a powerful suite of simulation programs for simulating the stochastic dynamics of networks of biochemical reactions. Its first public version enables computational biologists to more efficiently investigate the dynamics of realistic biological systems

Synthetic tetracycline-inducible regulatory networks: computer-aided design of dynamic phenotypes

<p>Abstract</p> <p>Background</p> <p>Tightly regulated gene networks, precisely controlling the expression of protein molecules, have received considerable interest by the biomedical community due to their promising applications. Among the most well studied inducible transcription systems are the tetracycline regulatory expression systems based on the tetracycline resistance operon of Escherichia coli, Tet-Off (tTA) and Tet-On (rtTA). Despite their initial success and improved designs, limitations still persist, such as low inducer sensitivity. Instead of looking at these networks statically, and simply changing or mutating the promoter and operator regions with trial and error, a systematic investigation of the dynamic behavior of the network can result in rational design of regulatory gene expression systems. Sophisticated algorithms can accurately capture the dynamical behavior of gene networks. With computer aided design, we aim to improve the synthesis of regulatory networks and propose new designs that enable tighter control of expression.</p> <p>Results</p> <p>In this paper we engineer novel networks by recombining existing genes or part of genes. We synthesize four novel regulatory networks based on the Tet-Off and Tet-On systems. We model all the known individual biomolecular interactions involved in transcription, translation, regulation and induction. With multiple time-scale stochastic-discrete and stochastic-continuous models we accurately capture the transient and steady state dynamics of these networks. Important biomolecular interactions are identified and the strength of the interactions engineered to satisfy design criteria. A set of clear design rules is developed and appropriate mutants of regulatory proteins and operator sites are proposed.</p> <p>Conclusion</p> <p>The complexity of biomolecular interactions is accurately captured through computer simulations. Computer simulations allow us to look into the molecular level, portray the dynamic behavior of gene regulatory networks and rationally engineer novel ones with useful applications. We are able to propose, test and accept or reject design principles for each network. Guided by simulations, we develop a set of design principles for novel tetracycline-inducible networks.</p

The Origins of Time-Delay in Template Biopolymerization Processes

Time-delays are common in many physical and biological systems and they give rise to complex dynamic phenomena. The elementary processes involved in template biopolymerization, such as mRNA and protein synthesis, introduce significant time delays. However, there is not currently a systematic mapping between the individual mechanistic parameters and the time delays in these networks. We present here the development of mathematical, time-delay models for protein translation, based on PDE models, which in turn are derived through systematic approximations of first-principles mechanistic models. Theoretical analysis suggests that the key features that determine the time-delays and the agreement between the time-delay and the mechanistic models are ribosome density and distribution, i.e., the number of ribosomes on the mRNA chain relative to their maximum and their distribution along the mRNA chain. Based on analytical considerations and on computational studies, we show that the steady-state and dynamic responses of the time-delay models are in excellent agreement with the detailed mechanistic models, under physiological conditions that correspond to uniform ribosome distribution and for ribosome density up to 70%. The methodology presented here can be used for the development of reduced time-delay models of mRNA synthesis and large genetic networks. The good agreement between the time-delay and the mechanistic models will allow us to use the reduced model and advanced computational methods from nonlinear dynamics in order to perform studies that are not practical using the large-scale mechanistic models

Engineering Probiotic Bacteria for Use as Antibiotic Alternatives

University of Minnesota PhD dissertation. February 2018. Major: Chemical Engineering. Advisor: Wei-Shou Hu. 1 computer file (PDF): xi, 110 pages.Decades of overuse of antibiotics has led to the emergence of resistant infections across the globe. Healthcare professionals are running out of viable options, as clinical isolates have begun resisting treatment to even last resort therapies. The emergence of these ‘superbugs’, coupled with the lack of new drugs in the discovery pipeline, has led to the possibility of a ‘post-antibiotic’ era. With the primary driving force for resistance development being the overuse of antibiotics, technologies are being sought to limit their injudicious application within the clinical and agricultural sectors. For decades, antimicrobial peptides (AMPs) have been proposed as a promising contender in the fight against microbial resistance. AMPs are small peptides that are produced natively from organisms across all domains of life as a first line of defense against microbial challenge. However, despite their therapeutic potential, AMPs have widely failed in translational success due to delivery and synthesis challenges. In this work, we propose engineering probiotic bacteria as AMP-delivery vehicles to overcome the inherent transport barriers of AMP-therapy. We focus on developing engineered probiotics to target resident pathogens of the gastrointestinal tract. The success of this technology could aid in the resistance crisis by unlocking the antibiotic power of many otherwise unusable peptide antibiotics. We have developed several derivatives of the probiotic strain, E.coli Nissle 1917 (EcN), which are capable of eliciting antibiotic activity against clinical and foodborne pathogens. The foundation of this work lays in the reorganization of AMP biosynthetic gene clusters for functional utility. We describe our development of the engineered probiotic, EcN(J25), which led to the first in vivo success of AMP-producing probiotics. Treatment with EcN(J25) was capable of reducing Salmonella carriage in pre-harvest poultry by 97% just 14-days post-treatment. In a similar workflow, we then focused on the development of EcN(C7) for use in decolonizing multidrug resistant E.coli in human carriers. Along the way we studied mechanisms of resistance, applied bioinformatics techniques, and developed novel synthetic biology tools for use in future engineered bacteria. The work within describes many of the challenges and potential of engineered probiotics, laying a foundation for future work in the field

In silico dynamic optimisation studies for batch/fed-batch mammalian cell suspension cultures producing biopharmaceuticals

Mammalian cell cultures are valuable for synthesis of therapeutic proteins and antibodies. They are commonly cultivated in bioindustry in form of large-scale suspension fed-batch cultures. The structure and regulatory responses of mammalian cells are complex, making it challenging to model them for practical process optimisation. The adjustable degrees of freedom in the cell cultures can be continuous variables as well as binary-type variables. The binary-type variables may be irreversible in cases such as cell-cycle arrest. The main aim of this study was to develop a general model for mammalian cell cultures using extracellular variables and capturing major changes in cellular responses between batch and fed-batch cultures. The model development started with a simple model for a hybridoma cell culture using first-principle equations. The growth kinetics was only linked to glucose and glutamine and the cell population was divided into three cell-cycle phases to study the phenomenon of cell-cycle arrest. But there were certain deficiencies in predicting growth rates in the death phase in fed-batch cultures although it was successful to simultaneously optimise a combination of continuous and binary-irreversible degrees of freedom. Thus, the growth kinetics was further related to amino acids concentration and cellular responses to high versus low concentration of glutamine and glucose based on a Chinese hamster ovary cell-line where amino acids data were available. The model contained 192 parameters with 26 measured cell culture variables. Most of the sensitive parameters were able to be identified using the Sobol' method of Global Sensitivity Analysis. The model could capture the main trends of key variables and be used to search for the optimal working range of the controllable variables. But uncertainties in the sensitive model parameters caused non-negligible variations in the model-based optimisation results. It is recommended to couple such off-line optimisation with on-line measurements of a few major variables to tackle the real-time uncertain nature of the complex cell culture system.Open acces