Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for ‘synthetic cellular heterogeneity’ that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a ‘phenotypic sensitivity analysis’ method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.National Institutes of Health (U.S.) (NIH NIGMS grant R01GM086881)National Science Foundation (U.S.) (NSF Award #1001092)National Science Foundation (U.S.) (NSF Graduate Research Fellowship Program)Swiss National Science Foundation (SystemsX.ch grant

ARTICLE NO. AL970888 A Randomized Parallel Algorithm for Single-Source Shortest Paths*

We give a randomized parallel algorithm for computing single-source shortest paths in weighted digraphs. We show that the exact shortest-path problem can be efficiently reduced to solving a series of approximate shortest-path subproblems. Our algorithm for the approximate shortest-path problem is based on the technique used by Ullman and Yannakakis in a parallel algorithm for breadth-first search. � 1997 Academic Press 1

A Linear-Processor Polylog-Time Algorithm for Shortest Paths in Planar Graphs

We give an algorithm requiring polylog time and a linear number of processors to solve singlesource shortest paths in directed planar graphs, bounded-genus graphs, and 2-dimensional overlap graphs. More generally, the algorithm works for any graph provided with a decomposition tree constructed using size-O( p n polylog n) separators