Leonidas Bleris

Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template

Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network‐coding genes. Gene product levels could potentially be decoupled from these changes via built‐in adaptation mechanisms, thereby boosting network reliability. Here, we show that a mechanism based on an incoherent feedforward motif enables adaptive gene expression in mammalian cells. We modeled, synthesized, and tested transcriptional and post‐transcriptional incoherent loops and found that in all cases the gene product adapts to changes in DNA template abundance. We also observed that the post‐transcriptional form results in superior adaptation behavior, higher absolute expression levels, and lower intrinsic fluctuations. Our results support a previously hypothesized endogenous role in gene dosage compensation for such motifs and suggest that their incorporation in synthetic networks will improve their robustness and reliability.

Rationally designed logic integration of regulatory signals in mammalian cells

Molecular-level information processing1,2, or computing, is essential for ‘smart’ in vivo nanosystems. Natural molecular computing, such as messenger RNA (mRNA) synthesis regulation by special proteins called transcription factors (TFs)3,4, may inspire engineered systems leading to the next generation of nanobiotechnological and nanomedical applications. Synthetic pathways5–15 have already implemented logical control of mRNA levels by certain TF combinations. Here we show an alternative approach toward general-purpose control of mRNA and protein levels by logic integration of transcription factor input signals in mammalian cells. The factors regulate synthetic genes coding for small regulatory RNAs – microRNAs – that in turn control mRNA of interest (i.e., output) via RNA interference pathway. Simple nature of these modular interactions allows in theory to implement any arbitrary logic relation between the TFs and the output16. We construct, test, and optimize increasingly complex circuits with up to three TF inputs, establishing a platform for in-vivo molecular computing.

A System-on-a-Chip Implementation for Embedded Real-Time Model Predictive Control

This paper presents a hardware architecture for embedded real-time model predictive control (MPC). The computational cost of an MPC problem, which relies on the solution of an optimization problem at every time step, is dominated by operations on real matrices. In order to design an efficient and low-cost application-specific processor, we analyze the computational cost of MPC, and we propose a limited-resource host processor to be connected with an application-specific matrix coprocessor. The coprocessor uses a 16-b logarithmic number system arithmetic unit, which is designed using cotransformation, to carry out the required arithmetic operations. The proposed architecture is implemented by means of a hardware description language and then prototyped and emulated on a field-programmable gate array. Results on computation time and architecture area are presented and analyzed, and the functionality of the proposed architecture is verified using two case studies: a linear problem of a rotating antenna and a nonlinear glucose-regulation problem. The proposed MPC architecture yields a small-in-size and energy-efficient implementation that is capable of solving the aforementioned problems on the order of milliseconds, and we compare its performance and area requirements with other MPC designs that have appeared in the literature.

A co-processor FPGA platform for the implementation of real-time model predictive control

In order to effectively control nonlinear and multivariable models, and to incorporate constraints on system states, inputs and outputs (bounds, rate of change), a suitable (sometimes necessary) controller is model predictive control (MPC). MPC is an optimization-based control scheme that requires abundant matrix operations for the calculation of the optimal control moves. In this work we propose a mixed software and hardware embedded MPC implementation. Using a codesign step and based on profiling results, we decompose the optimization algorithm into two parts: one that fits into a host processor and one that fits into a custom made unit that performs the computationally demanding arithmetic operations. The profiling results and information on the co-processor design are provided

Transcription activator-like effector hybrids for conditional control and rewiring of chromosomal transgene expression.

The ability to conditionally rewire pathways in human cells holds great therapeutic potential. Transcription activator-like effectors (TALEs) are a class of naturally occurring specific DNA binding proteins that can be used to introduce targeted genome modifications or control gene expression. Here we present TALE hybrids engineered to respond to endogenous signals and capable of controlling transgenes by applying a predetermined and tunable action at the single-cell level. Specifically, we first demonstrate that combinations of TALEs can be used to modulate the expression of stably integrated genes in kidney cells. We then introduce a general purpose two-hybrid approach that can be customized to regulate the function of any TALE either using effector molecules or a heterodimerization reaction. Finally, we demonstrate the successful interface of TALEs to specific endogenous signals, namely hypoxia signaling and microRNAs, essentially closing the loop between cellular information and chromosomal transgene expression.

Logic integration of mRNA signals by an RNAi-based molecular computer

Synthetic in vivo molecular ‘computers’ could rewire biological processes by establishing programmable, non-native pathways between molecular signals and biological responses. Multiple molecular computer prototypes have been shown to work in simple buffered solutions. Many of those prototypes were made of DNA strands and performed computations using cycles of annealing-digestion or strand displacement. We have previously introduced RNA interference (RNAi)-based computing as a way of implementing complex molecular logic in vivo. Because it also relies on nucleic acids for its operation, RNAi computing could benefit from the tools developed for DNA systems. However, these tools must be harnessed to produce bioactive components and be adapted for harsh operating environments that reflect in vivo conditions. In a step toward this goal, we report the construction and implementation of biosensors that ‘transduce’ mRNA levels into bioactive, small interfering RNA molecules via RNA strand exchange in a cell-free Drosophila embryo lysate, a step beyond simple buffered environments. We further integrate the sensors with our RNAi ‘computational’ module to evaluate two-input logic functions on mRNA concentrations. Our results show how RNA strand exchange can expand the utility of RNAi computing and point toward the possibility of using strand exchange in a native biological setting.

Real-time implementation of model predictive control

A real-time implementation of model predictive control (MPC) is presented in this paper. MPC, also known as receding horizon control and moving horizon control, is widely accepted as the controller of choice for multivariable systems that have inequality constraints on system states, inputs and outputs. For processes with slow dynamics and low sampling rates, MPC is typically implemented on a dedicated computer. For systems with fast dynamics such as those in MEMS, a hardware embedded MPC would be an appropriate controller implementation since the size and the application precludes the use of a dedicated computer. Recent manufacturing advances have opened the path for the fabrication of micromechanical devices and electronic subsystems under the same manufacturing and packaging process, thereby opening the path for the use of advanced control algorithms towards systems-on-chip applications.

Reduced order distributed boundary control of thermal transients in microsystems

We study the problem of regulation of thermal transients in a microsystem using empirical eigenfunctions. Proper orthogonal decomposition (POD) is applied to an ensemble of data to obtain the dominant structures, called empirical eigenfunctions, that characterize the dynamics of the process. These eigenfunctions are the most efficient basis for capturing the dynamics of an infinite dimensional process with a finite number of modes. In contrast to published approaches, we propose a new receding horizon boundary control scheme using the empirical eigenfunctions in a constrained optimization procedure to track a desired spatiotemporal profile. Finite element method (FEM) simulations of heat transfer are provided and used in order to implement and test the performance of the controller.

LNS architectures for embedded model predictive control processors

This paper presents a research on arithmetic units targeted to implement model predictive control (MPC) in a custom embedded processor. A novel hardware implementation of cotransformation for the calculation of addition and subtraction in the Logarithmic Number System (LNS) is proposed. This architecture provides a small ROM-less adder/subtracter, with longer operation latency than other LNS techniques, but easily pipelineable. These characteristics make it very adequate for implementing the datapath of custom MPC embeddable microprocessors. A review of the arithmetic customization process is presented, including: an analysis of the finite precision problem, modifications to the standard MPC algorithm that simplify embedding the application, and the reasons that suggest better performance of LNS over standard floating-point (FP) architectures. The proposed arithmetic unit architecture for 16-bit LNS is fully synthesized for ASIC, and compared with an equivalent FP implementation. Area and clock cycle estimates are compared. Finally, considerations on low-precision implementations of LNS arithmetic units are provided, and an embedded-ROM implementation of addition/subtraction in LNS is proposed and analyzed.

CRISPR-based self-cleaving mechanism for controllable gene delivery in human cells

Controllable gene delivery via vector-based systems remains a formidable challenge in mammalian synthetic biology and a desirable asset in gene therapy applications. Here, we introduce a methodology to control the copies and residence time of a gene product delivered in host human cells but also selectively disrupt fragments of the delivery vehicle. A crucial element of the proposed system is the CRISPR protein Cas9. Upon delivery, Cas9 guided by a custom RNA sequence cleaves the delivery vector at strategically placed targets thereby inactivating a co-expressed gene of interest. Importantly, using experiments in human embryonic kidney cells, we show that specific parameters of the system can be adjusted to fine-tune the delivery properties. We envision future applications in complex synthetic biology architectures, gene therapy and trace-free delivery.