A computational method for the investigation of multistable systems and its application to genetic switches

BACKGROUND: Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology. RESULTS: Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour. CONCLUSIONS: Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks

An Algorithm to Recognize Multi-Stable Behavior From an Ensemble of Stochastic Simulation Runs

Synthetic biological designers are demanding tools to help with the design and verification process of new biological models. Some of the most common tools available aggregate multiple simulation results into one “clean” trajectory that hopefully is representative of the system’s behavior. However, for systems exhibiting multiple stable states, these techniques fail to show all the possible trajectories of the system. This work introduces a method capable of detecting the presence of more than one “typical” trajectory in a system, which can also be integrated with other available simulation tools

Synthesis of Biological and Mathematical Methods for Gene Network Control

abstract: Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Synthetic in vitro transcriptional oscillators

The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells

Computational Simulation of Gene Regulatory Networks Implementing an Extendable Synchronous Single-Input Delay Flip-Flop and State Machine

We present a detailed and extendable design of the first synchronous single-input delay flip-flop implemented as a gene regulatory network in Escherichia coli (E. coli). The device, which we call the BioD, has one data input (trans-acting RNA), one clock input (far-red light) and an output that reports the state of the device using green fluorescent protein (GFP). The proposed design builds on Gardner’s toggle switch, to provide a more sophisticated device that can be synchronized with other devices within or without the same cell, and which requires only one data input. We provide a mathematical model of the system and simulation results. The results show that the device behaves in line with desired functionality. Further, we discuss the constraints of the design, which pertain to ranges of parameter values. The BioD is extended via the addition of an update function and input and output interfaces. The result is the BioFSM, which constitutes a synchronous and modular finite state machine, which uses an update function to change its state, stored in the BioD. The BioFSM uses its input and output interfaces for inter-cellular communications. This opens the door to the design of a circular cellular automata (the BioCell), which is envisioned as a number of communicating E. coli colonies, each made of clones of one BioFSM

Quantification of signaling networks

Studies in living system in the past several decades have generated qualitative understanding of the molecular interactions resulting in large networks. These networks were essentially deciphered by breaking the components of a cell through a reductionist approach. Biological networks comprising of interactions between genes, proteins and metabolites co-ordinate in the regulation of cellular processes. However, understanding the cellular function also requires quantitative information including network dynamics, which results due to an inherent design principle embedded in the network. Interactions within the network are well organized to form a definite regulatory structure, which in turn exhibits different emergent properties. The property of the network helps the cell to achieve the desired phenotypic state in a controlled manner. The dynamics of the network or the relationship between network structure and cellular behavior cannot be understood intuitively from the interaction map of the network. Computational methods can now be employed to study these networks at system level. The field of systems biology looks at integrating the interaction maps obtained through molecular biological approach. Various studies at the system level have been reported for pathways namely chemotactic response in bacteria, cell cycle and osmotic signaling in yeast, growth factor stimulated signaling pathways in mammals. This review focuses on understanding signaling networks with the help of mathematical models

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

A Robust and Tunable Mitotic Oscillator in Artificial Cells

This dissertation aims to develop a droplet-based artificial cell system using cell-free extracts of Xenopus laevis eggs and understand mitotic oscillations with the proposed system. Single-cell analysis is pivotal to deciphering complex phenomena such as cellular heterogeneity, bistable switches, and oscillations, where a population ensemble cannot represent the individual behaviors. Despite having unique advantages of manipulation and characterization of biochemical networks, bulk cell-free systems lack the essential single-cell information to understand out-of-steady-state dynamics including cell cycles. In this dissertation, we present a novel artificial single-cell system for the study of mitotic dynamics by encapsulating Xenopus egg extracts in water-in-oil micro-emulsions. The artificial cells are different from real cells, i.e., their surface is formed by surfactant oil instead of the cell membrane. These “cells”, adjustable in sizes and periods, encapsulate cycling cytoplasmic extracts that can sustain mitotic oscillations for over 30 cycles. The artificial cells function in forms from the simplest cytoplasmic-only oscillators to the more complicated ones involving demembranated sperm chromatin that can reconstitute downstream mitotic events. The dynamic activities of cell cycle clock can be detected by fluorescent reporters such as cyclin B1-YFP and securin-mCherry. This innate flexibility makes it key to studying cell cycle clock tunability and stochasticity. Our experimental results indicate that the mitotic oscillators generated by our system are effectively tunable in frequency with cyclin B1 mRNAs and the dynamic behavior of single droplet oscillators is size-dependent. We also establish a stochastic model that highlights energy supply as an essential regulator of cell cycles. Moreover, the model explains experimental observations including the increase of baseline and amplitude of cyclin B1 time course. This dissertation study demonstrates a simple, powerful, and likely generalizable strategy of integrating single-cell approaches into conventional in vitro systems to study complex clock functions.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144115/1/yeguan_1.pd

Towards light based dynamic control of synthetic biological systems

For the field of synthetic biology, the adaptation of principles, from the well established traditional engineering disciplines, like mechanical and electrical engineering, in order to realise complex synthetic biological circuits, is an intriguing prospect. These principles can enable a forward engineering, rational design and implementation approach, where a system's properties can be predicted or designed in silico followed by the manufacturing of the in vivo system, that can be tested, used or redesigned in the most efficient possible way. Achieving control over these circuits, is one of the important topics of the field, for these applications to become robust and render useful functions applicable to energy, medicine, pharmaceuticals and agriculture industries. In this work, I attempt to explore light, as a promising control 'dial' for synthetic circuitry. Light is fast, economic compared to chemicals, it can be interfaced with electronics, it is reversible in its effect and can be applied at a fine spatio-temporal resolution. These characteristics, are absent from the classically used chemical inducers, meaning that light, can open new possibilities for the user to control synthetic systems, or even facilitate the cell to cell communication, within population based networks. This work, is a contribution towards harnessing the advantages of light, for achieving control over synthetic circuits. More specifically, I start with the detailed theoretical and experimental study of the Cph8 two component system, a synthetic chimeric receptor which is responsive to red light. This is done, in order to develop a sufficient theoretical understanding of it, through detailed mechanistic modelling, in order to connect the specific system with the toggle switch and the dual feedback oscillator, in an optimal way and achieve control of these devices through light. The developed model, was able to highlight the main aspects and mechanisms inherent to its structure, describe most of the observations from the experimental system, to also make quantitative predictions. The second part of this work, was the development of novel promoters, that can be regulated by a commonly used transcription factor, such as LacI, but also, light responsive regulators like OmpR and CcaR. This yielded a direct way to integrate light and chemical inputs, into a single output, while the dual regulation, allowed to connect and modulate the toggle switch without the need of additional transcription factors. The latter, a light tuneable toggle switch, showed indications that it can function as a memory controller that can be reset by light. Finally, I show the design and modelling of a light tuneable dual feedback oscillator, where light of one wavelength can be used to tune the amplitude, while another wavelength can tune the period. The developed models and synthetic circuits are expected to contribute towards implementing finely tuned and controlled synthetic circuits through light.Open Acces