Synthetic biology is an emerging engineering disciple, which designs and controls biological systems for creation of materials, biosensors, biocomputing, and much more. To better control and engineer these systems, modular genetic components which allow for highly specific and high dynamic range genetic regulation are necessary. Currently the field struggles to demonstrate reliable regulators which are programmable and specific, yet also allow for a high dynamic range of control. Inspired by the characteristics of the RNA toehold switch in E. coli, this project attempts utilize artificial introns and complementary trans-acting RNAs for gene regulation in a eukaryote host, S. cerevisiae. Following modification to an artificial intron, splicing control with RNA hairpins was demonstrated. Temperature shifts led to increased protein production likely due to increased splicing due to hairpin loosening. Progress is underway to demonstrate trans-acting RNA interaction to control splicing. With continued development, we hope to provide a programmable, specific, and effective means for translational gene regulation in S. cerevisae.

Alzheimer’s Disease (AD) affects over 5 million individuals in the U.S. and has a direct cost estimated in excess of $200 billion per year. Broadly speaking, there are two forms of AD—early-onset, familial AD (FAD) and late-onset-sporadic AD (SAD). Animal models of AD, which rely on the overexpression of FAD-related mutations, have provided important insights into the disease. However, these models do not display important disease-related pathologies and have been limited in their ability to model the complex genetics associated with SAD.

Advances in cellular reprogramming, have enabled the generation of in vitro disease models that can be used to dissect disease mechanisms and evaluate potential therapeutics. To that end, efforts by many groups, including the Brafman laboratory, to generated patient-specific hiPSCs have demonstrated the promise of studying AD in a simplified and accessible system. However, neurons generated from these hiPSCs have shown some, but not all, of the early molecular and cellular hallmarks associated with the disease. Additionally, phenotypes and pathological hallmarks associated with later stages of the human disease have not been observed with current hiPSC-based systems. Further, disease relevant phenotypes in neurons generated from SAD hiPSCs have been highly variable or largely absent. Finally, the reprogramming process erases phenotypes associated with cellular aging and, as a result, iPSC-derived neurons more closely resemble fetal brain rather than adult brain.

It is well-established that in vivo cells reside within a complex 3-D microenvironment that plays a significant role in regulating cell behavior. Signaling and other cellular functions, such as gene expression and differentiation potential, differ in 3-D cultures compared with 2-D substrates. Nonetheless, previous studies using AD hiPSCs have relied on 2-D neuronal culture models that do not reflect the 3-D complexity of native brain tissue, and therefore, are unable to replicate all aspects of AD pathogenesis. Further, the reprogramming process erases cellular aging phenotypes. To address these limitations, this project aimed to develop bioengineering methods for the generation of 3-D organoid-based cultures that mimic in vivo cortical tissue, and to generate an inducible gene repression system to recapitulate cellular aging hallmarks.

While techniques for reading DNA in some capacity has been possible for decades,

the ability to accurately edit genomes at scale has remained elusive. Novel techniques

have been introduced recently to aid in the writing of DNA sequences. While writing

DNA is more accessible, it still remains expensive, justifying the increased interest in

in silico predictions of cell behavior. In order to accurately predict the behavior of

cells it is necessary to extensively model the cell environment, including gene-to-gene

interactions as completely as possible.

Significant algorithmic advances have been made for identifying these interactions,

but despite these improvements current techniques fail to infer some edges, and

fail to capture some complexities in the network. Much of this limitation is due to

heavily underdetermined problems, whereby tens of thousands of variables are to be

inferred using datasets with the power to resolve only a small fraction of the variables.

Additionally, failure to correctly resolve gene isoforms using short reads contributes

significantly to noise in gene quantification measures.

This dissertation introduces novel mathematical models, machine learning techniques,

and biological techniques to solve the problems described above. Mathematical

models are proposed for simulation of gene network motifs, and raw read simulation.

Machine learning techniques are shown for DNA sequence matching, and DNA

sequence correction.

Results provide novel insights into the low level functionality of gene networks. Also

shown is the ability to use normalization techniques to aggregate data for gene network

inference leading to larger data sets while minimizing increases in inter-experimental

noise. Results also demonstrate that high error rates experienced by third generation

sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.

Rewired biological pathways and/or rewired microRNA (miRNA)-mRNA interactions might also influence the activity of biological pathways. Here, rewired biological pathways is defined as differential (rewiring) effect of genes on the topology of biological pathways between controls and cases. Similarly, rewired miRNA-mRNA interactions are defined as the differential (rewiring) effects of miRNAs on the topology of biological pathways between controls and cases. In the dissertation, it is discussed that how rewired biological pathways (Chapter 1) and/or rewired miRNA-mRNA interactions (Chapter 2) aberrantly influence the activity of biological pathways and their association with disease.

This dissertation proposes two PageRank-based analytical methods, Pathways of Topological Rank Analysis (PoTRA) and miR2Pathway, discussed in Chapter 1 and Chapter 2, respectively. PoTRA focuses on detecting pathways with an altered number of hub genes in corresponding pathways between two phenotypes. The basis for PoTRA is that the loss of connectivity is a common topological trait of cancer networks, as well as the prior knowledge that a normal biological network is a scale-free network whose degree distribution follows a power law where a small number of nodes are hubs and a large number of nodes are non-hubs. However, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the scale-free structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal samples. Hence, it is hypothesized that if the number of hub genes is different in a pathway between normal and cancer, this pathway might be involved in cancer. MiR2Pathway focuses on quantifying the differential effects of miRNAs on the activity of a biological pathway when miRNA-mRNA connections are altered from normal to disease and rank disease risk of rewired miRNA-mediated biological pathways. This dissertation explores how rewired gene-gene interactions and rewired miRNA-mRNA interactions lead to aberrant activity of biological pathways, and rank pathways for their disease risk. The two methods proposed here can be used to complement existing genomics analysis methods to facilitate the study of biological mechanisms behind disease at the systems-level.

This dissertation treats a number of related problems in control and data analysis of complex networks.

First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.

Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.

Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.

At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.

Many systems in the world \u2014 such as cellular networks, the post service, or transportation pathways \u2014 can be modeled as networks or graphs. The practical applications of graph algorithms generally seek to achieve some goal while minimizing some cost such as money or distance. While the minimum linear arrangement (MLA) problem has been widely-studied amongst graph ordering and embedding problems, there have been no developments into versions of the problem involving degree higher than 2. An application of our problem can be seen in overlay networks in telecommunications. An overlay network is a virtual network that is built on top of another network. It is a logical network where the links between nodes represent the physical paths connecting the nodes in the underlying infrastructure. The underlying physical network may be incomplete, but as long as it is connected, we can build a complete overlay network on top of it. Since some nodes may be overloaded by traffic, we can reduce the strain on the overlay network by limiting the communication between nodes. Some edges, however, may have more importance than others so we must be careful about our selection of which nodes are allowed to communicate with each other. The balance of reducing the degree of the network while maximizing communication forms the basis of our d-degree minimum arrangement problem. In this thesis we will look at several approaches to solving the generalized d-degree minimum arrangement d-MA problem where we embed a graph onto a subgraph of a given degree. We first look into the requirements and challenges of solving the d-MA problem. We will then present a polynomial-time heuristic and compare its performance with the optimal solution derived from integer linear programming. We will show that a simple (d-1)-ary tree construction provides the optimal structure for uniform graphs with large requests sets. Finally, we will present experimental data gathered from running simulations on a variety of graphs to evaluate the efficiency of our heuristic and tree construction.

The focus shift towards Silicon Valley and similar ecosystems in the past decade, the recent boom in startups and entrepreneurship, and the resurgence of venture capital funding is fueling rapid advancement of modern technologies, such as software, biotechnology, and renewable energy. One facet of the growing entrepreneurial landscape features healthcare technology—a field of research centered upon various technical advances in medicine, software, and hardware. Trends in healthcare technology commercialization represent a promising opportunity for disruption in the healthcare industry. The integration of rapidly iterating software with medical research, timed perfectly with the passage of the Affordable Care Act and the boom of venture capital investment in both Big Data and mobile technology, has the healthcare technology primed for explosive growth over the next decade. Investment data indicates that strong public market activity in the past year will continue to fuel venture capital growth in both the biotechnology and digital health sectors, with the potential for multiple large exits by life sciences companies, more than even software, in the coming year.

The ability to edit chromosomal regions is an important tool for the study of gene function and the ability to engineer synthetic gene networks. CRISPR-Cas systems, a bacterial RNA-guided immune system against foreign nucleic acids, have recently been engineered for a plethora of genome engineering and transcriptional regulation applications. Here we employ engineered variants of CRISPR systems in proof-of-principle experiments demonstrating the ability of CRISPR-Cas derived single-DNA-strand cutting enzymes (nickases) to direct host-cell genomic recombination. E.coli is generally regarded as a poorly recombinogenic host with double-stranded DNA breaks being highly lethal. However, CRISPR-guided nickase systems can be easily programmed to make very precise, non-lethal, incisions in genomic regions directing both single reporter gene and larger-scale recombination events deleting up to 36 genes. Genome integrated repetitive elements of variable sizes can be employed as sites for CRISPR induced recombination. We project that single-stranded based editing methodologies can be employed alongside preexisting genome engineering techniques to assist and expedite metabolic engineering and minimalized genome research.

Currently in synthetic biology only the Las, Lux, and Rhl quorum sensing pathways have been adapted for broad engineering use. Quorum sensing allows a means of cell to cell communication in which a designated sender cell produces quorum sensing molecules that modify gene expression of a designated receiver cell. While useful, these three quorum sensing pathways exhibit a nontrivial level of crosstalk, hindering robust engineering and leading to unexpected effects in a given design. To address the lack of orthogonality among these three quorum sensing pathways, previous scientists have attempted to perform directed evolution on components of the quorum sensing pathway. While a powerful tool, directed evolution is limited by the subspace that is defined by the protein. For this reason, we take an evolutionary biology approach to identify new orthogonal quorum sensing networks and test these networks for cross-talk with currently-used networks. By charting characteristics of acyl homoserine lactone (AHL) molecules used across quorum sensing pathways in nature, we have identified favorable candidate pathways likely to display orthogonality. These include Aub, Bja, Bra, Cer, Esa, Las, Lux, Rhl, Rpa, and Sin, which we have begun constructing and testing. Our synthetic circuits express GFP in response to a quorum sensing molecule, allowing quantitative measurement of orthogonality between pairs. By determining orthogonal quorum sensing pairs, we hope to identify and adapt novel quorum sensing pathways for robust use in higher-order genetic circuits.

Synthetic gene networks have evolved from simple proof-of-concept circuits to

complex therapy-oriented networks over the past fifteen years. This advancement has

greatly facilitated expansion of the emerging field of synthetic biology. Multistability is a

mechanism that cells use to achieve a discrete number of mutually exclusive states in

response to environmental inputs. However, complex contextual connections of gene

regulatory networks in natural settings often impede the experimental establishment of

the function and dynamics of each specific gene network.

In this work, diverse synthetic gene networks are rationally designed and

constructed using well-characterized biological components to approach the cell fate

determination and state transition dynamics in multistable systems. Results show that

unimodality and bimodality and trimodality can be achieved through manipulation of the

signal and promoter crosstalk in quorum-sensing systems, which enables bacterial cells to

communicate with each other.

Moreover, a synthetic quadrastable circuit is also built and experimentally

demonstrated to have four stable steady states. Experiments, guided by mathematical

modeling predictions, reveal that sequential inductions generate distinct cell fates by

changing the landscape in sequence and hence navigating cells to different final states.

Circuit function depends on the specific protein expression levels in the circuit.

We then establish a protein expression predictor taking into account adjacent

transcriptional regions’ features through construction of ~120 synthetic gene circuits

(operons) in Escherichia coli. The predictor’s utility is further demonstrated in evaluating genes’ relative expression levels in construction of logic gates and tuning gene expressions and nonlinear dynamics of bistable gene networks.

These combined results illustrate applications of synthetic gene networks to

understand the cell fate determination and state transition dynamics in multistable

systems. A protein-expression predictor is also developed to evaluate and tune circuit

dynamics.