Sensing and Actuation from Biology to Electronics

We introduce an in vitro diagnostic magnetic biosensing platform for immunoassay and nucleic acid detection. The platform has key characteristics for a point-of-use (POU) diagnostic: portability, low-power consumption, low cost, and multiplexing capability. As a demonstration of capabilities, we use this platform for the room temperature, amplification-free detection of a 31 bp DNA oligomer and interferon-gamma (a protein relevant for tuberculosis diagnosis). Reliable assay measurements down to 100 pM for the DNA and 1 pM for the protein are demonstrated. We introduce a novel "magnetic freezing" technique for baseline measurement elimination and to enable spatial multiplexing. We have created a general protocol for adapting integrated circuit (IC) sensors to any of hundreds of commercially available immunoassay kits and custom designed DNA sequences.

We also introduce a method for immunotherapy treatment of malignant gliomas. We utilize leukocytes internalized with immunostimulatory nanoparticle-oligonucleotide conjugates to localize and retain immune cells near the tumor site. As a proof-of-principle, we develop a novel cell imaging and incubation chamber for in vitro magnetic motility experiments. We use the apparatus to demonstrate the controlled movement of magnetically loaded THP-1 leukocytes.

Finally, we introduce an IC transmitter and power ampli er (PA) that utilizes electronic digital infrastructure, sensors, and actuators to self-heal and adapt to process, dynamic, and environmental variation. Traditional IC design has achieved incredible degrees of reliability by ensuring that billions of transistors on a single IC die are all simultaneously functional. Reliability becomes increasingly difficult as the size of a transistor shrinks. Self-healing can mitigate these variations.

Robust Control of Evolutionary Dynamics

The application of principles from evolutionary biology has long been used to gain new insights into the progression and clinical control of both infectious diseases and neoplasms. This iterative evolutionary process consists of expansion, diversification and selection within an adaptive landscape - species are subject to random genetic or epigenetic alterations that result in variations; genetic information is inherited through asexual reproduction and strong selective pressures such as therapeutic intervention can lead to the adaptation and expansion of resistant variants. These principles lie at the center of modern evolutionary synthesis and constitute the primary reasons for the development of resistance and therapeutic failure, but also provide a framework that allows for more effective control.

A model system for studying the evolution of resistance and control of therapeutic failure is the treatment of chronic HIV-1 infection by broadly neutralizing antibody (bNAb) therapy. A relatively recent discovery is that a minority of HIV-infected individuals can produce broadly neutralizing antibodies, that is, antibodies that inhibit infection by many strains of HIV. Passive transfer of human antibodies for the prevention and treatment of HIV-1 infection is increasingly being considered as an alternative to a conventional vaccine. However, recent evolution studies have uncovered that antibody treatment can exert selective pressure on virus that results in the rapid evolution of resistance. In certain cases, complete resistance to an antibody is conferred with a single amino acid substitution on the viral envelope of HIV.

The challenges in uncovering resistance mechanisms and designing effective combination strategies to control evolutionary processes and prevent therapeutic failure apply more broadly. We are motivated by two questions: Can we predict the evolution to resistance by characterizing genetic alterations that contribute to modified phenotypic fitness? Given an evolutionary landscape and a set of candidate therapies, can we computationally synthesize treatment strategies that control evolution to resistance?

To address the first question, we propose a mathematical framework to reason about evolutionary dynamics of HIV from computationally derived Gibbs energy fitness landscapes -- expanding the theoretical concept of an evolutionary landscape originally conceived by Sewall Wright to a computable, quantifiable, multidimensional, structurally defined fitness surface upon which to study complex HIV evolutionary outcomes.

To design combination treatment strategies that control evolution to resistance, we propose a methodology that solves for optimal combinations and concentrations of candidate therapies, and allows for the ability to quantifiably explore tradeoffs in treatment design, such as limiting the number of candidate therapies in the combination, dosage constraints and robustness to error. Our algorithm is based on the application of recent results in optimal control to an HIV evolutionary dynamics model and is constructed from experimentally derived antibody resistant phenotypes and their single antibody pharmacodynamics. This method represents a first step towards integrating principled engineering techniques with an experimentally based mathematical model in the rational design of combination treatment strategies and offers predictive understanding of the effects of combination therapies of evolutionary dynamics and resistance of HIV. Preliminary in vitro studies suggest that the combination antibody therapies predicted by our algorithm can neutralize heterogeneous viral populations despite containing resistant mutations.