Synthetic molecular machines for active self-assembly : prototype algorithms, designs, and experimental study

Computer science and electrical engineering have been the great success story of the twentieth century. The neat modularity and mapping of a language onto circuits has led to robots on Mars, desktop computers and smartphones. But these devices are not yet able to do some of the things that life takes for granted: repair a scratch, reproduce, regenerate, or grow exponentially fast–all while remaining functional.

This thesis explores and develops algorithms, molecular implementations, and theoretical proofs in the context of “active self-assembly” of molecular systems. The long-term vision of active self-assembly is the theoretical and physical implementation of materials that are composed of reconfigurable units with the programmability and adaptability of biology’s numerous molecular machines. En route to this goal, we must first find a way to overcome the memory limitations of molecular systems, and to discover the limits of complexity that can be achieved with individual molecules.

One of the main thrusts in molecular programming is to use computer science as a tool for figuring out what can be achieved. While molecular systems that are Turing-complete have been demonstrated [Winfree, 1996], these systems still cannot achieve some of the feats biology has achieved.

One might think that because a system is Turing-complete, capable of computing “anything,” that it can do any arbitrary task. But while it can simulate any digital computational problem, there are many behaviors that are not “computations” in a classical sense, and cannot be directly implemented. Examples include exponential growth and molecular motion relative to a surface.

Passive self-assembly systems cannot implement these behaviors because (a) molecular motion relative to a surface requires a source of fuel that is external to the system, and (b) passive systems are too slow to assemble exponentially-fast-growing structures. We call these behaviors “energetically incomplete” programmable behaviors. This class of behaviors includes any behavior where a passive physical system simply does not have enough physical energy to perform the specified tasks in the requisite amount of time.

As we will demonstrate and prove, a sufficiently expressive implementation of an “active” molecular self-assembly approach can achieve these behaviors. Using an external source of fuel solves part of the the problem, so the system is not “energetically incomplete.” But the programmable system also needs to have sufficient expressive power to achieve the specified behaviors. Perhaps surprisingly, some of these systems do not even require Turing completeness to be sufficiently expressive.

Building on a large variety of work by other scientists in the fields of DNA nanotechnology, chemistry and reconfigurable robotics, this thesis introduces several research contributions in the context of active self-assembly.

We show that simple primitives such as insertion and deletion are able to generate complex and interesting results such as the growth of a linear polymer in logarithmic time and the ability of a linear polymer to treadmill. To this end we developed a formal model for active-self assembly that is directly implementable with DNA molecules. We show that this model is computationally equivalent to a machine capable of producing strings that are stronger than regular languages and, at most, as strong as context-free grammars. This is a great advance in the theory of active self- assembly as prior models were either entirely theoretical or only implementable in the context of macro-scale robotics.

We developed a chain reaction method for the autonomous exponential growth of a linear DNA polymer. Our method is based on the insertion of molecules into the assembly, which generates two new insertion sites for every initial one employed. The building of a line in logarithmic time is a first step toward building a shape in logarithmic time. We demonstrate the first construction of a synthetic linear polymer that grows exponentially fast via insertion. We show that monomer molecules are converted into the polymer in logarithmic time via spectrofluorimetry and gel electrophoresis experiments. We also demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism. This shows the growth of a population of polymers in logarithmic time. We characterize the DNA insertion mechanism that we utilize in Chapter 4. We experimentally demonstrate that we can control the kinetics of this re- action over at least seven orders of magnitude, by programming the sequences of DNA that initiate the reaction.

In addition, we review co-authored work on programming molecular robots using prescriptive landscapes of DNA origami; this was the first microscopic demonstration of programming a molec- ular robot to walk on a 2-dimensional surface. We developed a snapshot method for imaging these random walking molecular robots and a CAPTCHA-like analysis method for difficult-to-interpret imaging data.

Tissue engineering active biological machines : bio-inspired design, directed self-assembly, and characterization of muscular pumps simulating the embryonic heart

Biological machines are active devices that are comprised of cells and other biological components. These functional devices are best suited for physiological environments that support cellular function and survival. Biological machines have the potential to revolutionize the engineering of biomedical devices intended for implantation, where the human body can provide the required physiological environment. For engineering such cell-based machines, bio-inspired design can serve as a guiding platform as it provides functionally proven designs that are attainable by living cells. In the present work, a systematic approach was used to tissue engineer one such machine by exclusively using biological building blocks and by employing a bio-inspired design. Valveless impedance pumps were constructed based on the working principles of the embryonic vertebrate heart and by using cells and tissue derived from rats. The function of these tissue-engineered muscular pumps was characterized by exploring their spatiotemporal and flow behavior in order to better understand the capabilities and limitations of cells when used as the engines of biological machines.

Design, specification, and synthesis of aircraft electric power systems control logic

Cyber-physical systems integrate computation, networking, and physical processes. Substantial research challenges exist in the design and verification of such large-scale, distributed sensing, ac- tuation, and control systems. Rapidly improving technology and recent advances in control theory, networked systems, and computer science give us the opportunity to drastically improve our approach to integrated flow of information and cooperative behavior. Current systems rely on text-based spec- ifications and manual design. Using new technology advances, we can create easier, more efficient, and cheaper ways of developing these control systems. This thesis will focus on design considera- tions for system topologies, ways to formally and automatically specify requirements, and methods to synthesize reactive control protocols, all within the context of an aircraft electric power system as a representative application area.

This thesis consists of three complementary parts: synthesis, specification, and design. The first section focuses on the synthesis of central and distributed reactive controllers for an aircraft elec- tric power system. This approach incorporates methodologies from computer science and control. The resulting controllers are correct by construction with respect to system requirements, which are formulated using the specification language of linear temporal logic (LTL). The second section addresses how to formally specify requirements and introduces a domain-specific language for electric power systems. A software tool automatically converts high-level requirements into LTL and synthesizes a controller.

The final sections focus on design space exploration. A design methodology is proposed that uses mixed-integer linear programming to obtain candidate topologies, which are then used to synthesize controllers. The discrete-time control logic is then verified in real-time by two methods: hardware and simulation. Finally, the problem of partial observability and dynamic state estimation is ex- plored. Given a set placement of sensors on an electric power system, measurements from these sensors can be used in conjunction with control logic to infer the state of the system.

Control of dynamical systems with temporal logic specifications

This thesis is motivated by safety-critical applications involving autonomous air, ground, and space vehicles carrying out complex tasks in uncertain and adversarial environments. We use temporal logic as a language to formally specify complex tasks and system properties. Temporal logic specifications generalize the classical notions of stability and reachability that are studied in the control and hybrid systems communities. Given a system model and a formal task specification, the goal is to automatically synthesize a control policy for the system that ensures that the system satisfies the specification. This thesis presents novel control policy synthesis algorithms for optimal and robust control of dynamical systems with temporal logic specifications. Furthermore, it introduces algorithms that are efficient and extend to high-dimensional dynamical systems.

The first contribution of this thesis is the generalization of a classical linear temporal logic (LTL) control synthesis approach to optimal and robust control. We show how we can extend automata-based synthesis techniques for discrete abstractions of dynamical systems to create optimal and robust controllers that are guaranteed to satisfy an LTL specification. Such optimal and robust controllers can be computed at little extra computational cost compared to computing a feasible controller.

The second contribution of this thesis addresses the scalability of control synthesis with LTL specifications. A major limitation of the standard automaton-based approach for control with LTL specifications is that the automaton might be doubly-exponential in the size of the LTL specification. We introduce a fragment of LTL for which one can compute feasible control policies in time polynomial in the size of the system and specification. Additionally, we show how to compute optimal control policies for a variety of cost functions, and identify interesting cases when this can be done in polynomial time. These techniques are particularly relevant for online control, as one can guarantee that a feasible solution can be found quickly, and then iteratively improve on the quality as time permits.

The final contribution of this thesis is a set of algorithms for computing feasible trajectories for high-dimensional, nonlinear systems with LTL specifications. These algorithms avoid a potentially computationally-expensive process of computing a discrete abstraction, and instead compute directly on the system's continuous state space. The first method uses an automaton representing the specification to directly encode a series of constrained-reachability subproblems, which can be solved in a modular fashion by using standard techniques. The second method encodes an LTL formula as mixed-integer linear programming constraints on the dynamical system. We demonstrate these approaches with numerical experiments on temporal logic motion planning problems with high-dimensional (10+ states) continuous systems.

Signal transduction, regulation, and developmental logic of EGFR signalling in C. elegans

RTKs-mediated signaling systems and the pathways with which they interact (e.g., those initiated by G protein-mediated signaling) involve a highly cooperative network that sense a large number of cellular inputs and then integrate, amplify, and process this information to orchestrate an appropriate set of cellular responses. The responses include virtually all aspects of cell function, from the most fundamental (proliferation, differentiation) to the most specialized (movement, metabolism, chemosensation). The basic tenets of RTK signaling system seem rather well established. Yet, new pathways and even new molecular players continue to be discovered. Although we believe that many of the essential modules of RTK signaling system are rather well understood, we have relatively little knowledge of the extent of interaction among these modules and their overall quantitative importance.

My research has encompassed the study of both positive and negative signaling by RTKs in C. elegans. I identified the C. elegans S0S-1 gene and showed that it is necessary for multiple RAS-mediated developmental signals. In addition, I demonstrated that there is a SOS-1-independent signaling during RAS-mediated vulval differentiation. By assessing signal outputs from various triple mutants, I have concluded that this SOS-1-independent signaling is not mediated by PTP-2/SHP-2 or the removal of inhibition by GAP-1/ RasGAP and it is not under regulation by SLI-1/Cb1. I speculate that there is either another exchange factor for RASor an as yet unidentified signaling pathway operating during RAS-mediated vulval induction in C. elegans.

In an attempt to uncover the molecular mechanisms of negative regulation of EGFR signaling by SLI-1/Cb1, I and two other colleagues codiscovered that RING finger domain of SLI-1 is partially dispensable for activity. This structure-function analysis shows that there is an ubiquitin protein ligase-independent activity for SLI-1 in regulating EGFR signaling. Further, we identified an inhibitory tyrosine of LET-23/ EGFR requiring sli-1(+)for its effects: removal of this tyrosine closely mimics loss of sli-1 but not loss of other negative regulator function.

By comparative analysis of two RTK pathways with similar signaling mechanisms, I have found that clr-1, a previously identified negative regulator of egl-15 mediated FGFR signaling, is also involved in let-23 EGFR signaling. The success of this approach promises a similar reciprocal test and could potentially extend to the study of other signaling pathways with similar signaling logic.

Finally, by correlating the developmental expression of lin-3 EGF to let-23 EGFR signaling activity, I demonstrated the existence of reciprocal EGF signaling in coordinating the morphogenesis of epithelia. This developmental logic of EGF signaling could provide a basis to understand a universal mechanism for organogenesis.

The logic of receptor-ligand interactions in the Notch signaling pathway

The Notch signaling pathway enables neighboring cells to coordinate developmental fates in diverse processes such as angiogenesis, neuronal differentiation, and immune system development. Although key components and interactions in the Notch pathway are known, it remains unclear how they work together to determine a cell's signaling state, defined as its quantitative ability to send and receive signals using particular Notch receptors and ligands. Recent work suggests that several aspects of the system can lead to complex signaling behaviors: First, receptors and ligands interact in two distinct ways, inhibiting each other in the same cell (in cis) while productively interacting between cells (in trans) to signal. The ability of a cell to send or receive signals depends strongly on both types of interactions. Second, mammals have multiple types of receptors and ligands, which interact with different strengths, and are frequently co-expressed in natural systems. Third, the three mammalian Fringe proteins can modify receptor-ligand interaction strengths in distinct and ligand-specific ways. Consequently, cells can exhibit non-intuitive signaling states even with relatively few components.

In order to understand what signaling states occur in natural processes, and what types of signaling behaviors they enable, this thesis puts forward a quantitative and predictive model of how the Notch signaling state is determined by the expression levels of receptors, ligands, and Fringe proteins. To specify the parameters of the model, we constructed a set of cell lines that allow control of ligand and Fringe expression level, and readout of the resulting Notch activity. We subjected these cell lines to an assay to quantitatively assess the levels of Notch ligands and receptors on the surface of individual cells. We further analyzed the dependence of these interactions on the level and type of Fringe expression. We developed a mathematical modeling framework that uses these data to predict the signaling states of individual cells from component expression levels. These methods allow us to reconstitute and analyze a diverse set of Notch signaling configurations from the bottom up, and provide a comprehensive view of the signaling repertoire of this major signaling pathway.

Incremental Control Synthesis for Robotics in the Presence of Temporal Logic Specifications

This thesis presents methods for incrementally constructing controllers in the presence of uncertainty and nonlinear dynamics. The basic setting is motion planning subject to temporal logic specifications. Broadly, two categories of problems are treated. The first is reactive formal synthesis when so-called discrete abstractions are available. The fragment of linear-time temporal logic (LTL) known as GR(1) is used to express assumptions about an adversarial environment and requirements of the controller. Two problems of changes to a specification are posed that concern the two major aspects of GR(1): safety and liveness. Algorithms providing incremental updates to strategies are presented as solutions. In support of these, an annotation of strategies is developed that facilitates repeated modifications. A variety of properties are proven about it, including necessity of existence and sufficiency for a strategy to be winning. The second category of problems considered is non-reactive (open-loop) synthesis in the absence of a discrete abstraction. Instead, the presented stochastic optimization methods directly construct a control input sequence that achieves low cost and satisfies a LTL formula. Several relaxations are considered as heuristics to address the rarity of sampling trajectories that satisfy an LTL formula and demonstrated to improve convergence rates for Dubins car and single-integrators subject to a recurrence task.