Cell-Free Synthetic Biology Platform for Engineering Synthetic Biological Circuits and Systems

Synthetic biology brings engineering disciplines to create novel biological systems for biomedical and technological applications. The substantial growth of the synthetic biology field in the past decade is poised to transform biotechnology and medicine. To streamline design processes and facilitate debugging of complex synthetic circuits, cell-free synthetic biology approaches has reached broad research communities both in academia and industry. By recapitulating gene expression systems in vitro, cell-free expression systems offer flexibility to explore beyond the confines of living cells and allow networking of synthetic and natural systems. Here, we review the capabilities of the current cell-free platforms, focusing on nucleic acid-based molecular programs and circuit construction. We survey the recent developments including cell-free transcription– translation platforms, DNA nanostructures and circuits, and novel classes of riboregulators. The links to mathematical models and the prospects of cell-free synthetic biology platforms will also be discussed.11Yscopu

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

Modelling as Research Methodology

Modelling as Research Methodology is written for the scientist and student researching the (expected) functioning of systems under specified conditions. As such, it represents an introduction to the use of modelling in natural, human and economical sciences. The book is divided into two sections. The first section illustrates the universal nature of modelling as aid to the researcher. In the second section, several typical examples of modelling are described

An efficient telemetry system for restoring sight

PhD ThesisThe human nervous system can be damaged as a result of disease or trauma, causing conditions such as Parkinson’s disease. Most people try pharmaceuticals as a primary method of treatment. However, drugs cannot restore some cases, such as visual disorder. Alternatively, this impairment can be treated with electronic neural prostheses. A retinal prosthesis is an example of that for restoring sight, but it is not efficient and only people with retinal pigmentosa benefit from it. In such treatments, stimulation of the nervous system can be achieved by electrical or optical means. In the latter case, the nerves need to be rendered light sensitive via genetic means (optogenetics). High radiance photonic devices are then required to deliver light to the target tissue. Such optical approaches hold the potential to be more effective while causing less harm to the brain tissue. As these devices are implanted in tissue, wireless means need to be used to communicate with them. For this, IEEE 802.15.6 or Bluetooth protocols at 2.4GHz are potentially compatible with most advanced electronic devices, and are also safe and secure. Also, wireless power delivery can operate the implanted device. In this thesis, a fully wireless and efficient visual cortical stimulator was designed to restore the sight of the blind. This system is likely to address 40% of the causes of blindness. In general, the system can be divided into two parts, hardware and software. Hardware parts include a wireless power transfer design, the communication device, power management, a processor and the control unit, and the 3D design for assembly. The software part contains the image simplification, image compression, data encoding, pulse modulation, and the control system. Real-time video streaming is processed and sent over Bluetooth, and data are received by the LPC4330 six layer implanted board. After retrieving the compressed data, the processed data are again sent to the implanted electrode/optrode to stimulate the brain’s nerve cells

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering

Modelling as Research Methodology

Modelling as Research Methodology is written for the scientist and student researching the (expected) functioning of systems under specified conditions. As such, it represents an introduction to the use of modelling in natural, human and economical sciences. The book is divided into two sections. The first section illustrates the universal nature of modelling as aid to the researcher. In the second section, several typical examples of modelling are described