Guopeng Wei

Efficient Modeling and Simulation of Bacteria-Based Nanonetworks with BNSim

Bacteria-based networks are formed using native or engineered bacteria that communicate at nano-scale. This definition includes the micro-scale molecular transportation system which uses chemotactic bacteria for targeted cargo delivery, as well as genetic circuits for intercellular interactions like quorum sensing or light communication. To characterize the dynamics of bacterial networks accurately, we introduce BNSim, an open-source, parallel, stochastic, and multiscale modeling platform which integrates various simulation algorithms, together with genetic circuits and chemotactic pathway models in a complex 3D environment. Moreover, we show how this platform can be used to model synthetic bacterial consortia which implement a XOR function and aggregate nearby bacteria using light communication. Consequently, the results demonstrate how BNSim can predict various properties of realistic bacterial networks and provide guidance for their actual wet-lab implementations.

Bumpy Rides: Modeling the Dynamics of Chemotactic Interacting Bacteria

Recent advances in synthetic biology have brought the fantasy of having synthetic multicellular systems working at nanoscale closer to reality. Indeed, such systems consisting of networked biological nanomachines have a great potential for many novel applications like environmental monitoring and healthcare. In this paper, we consider dense networks of interacting bacteria capable of monitoring and treating diseases that affect microscopic regions in the human body. Towards this end, we propose a modeling approach for capturing the dynamics of such dense populations of interacting bacteria and estimate their performance for diagnostic and drug delivery purposes. Consequently, our approach can be used to identify various design trade-offs for dense networks of bacteria and design predictable and reliable synthetic multicellular systems.

A traffic-aware adaptive routing algorithm on a highly reconfigurable network-on-chip architecture

In this paper, we propose a flexible NoC architecture and a dynamic distributed routing algorithm which can enhance the NoC communication performance with minimal energy overhead. In particular, our proposed NoC architecture exploits the following two features: i) self-reconfigurable bidirectional channels to increase the effective bandwidth and ii) express virtual paths, as well as localized hub routers, to bypass some intermediate nodes at run time in the network. A deadlock-free and traffic-aware dynamic routing algorithm is further developed for the proposed architecture, which can take advantage of the increased flexibility in the proposed architecture. Both the channels self-reconfiguration and routing decisions are made in a distributed fashion, based on a function of the localized traffic conditions, in order to maximize the performance and minimize the energy costs at the macroscopic level. Our simulation results show that the proposed approach can reduce the network latency by 30\% -80\% in most cases compared to a conventional unidirectional mesh topology, while incurring less than 15\% power overhead.

An efficient Network-on-Chip (NoC) based multicore platform for hierarchical parallel genetic algorithms

In this work, we propose a new Network-on-Chip (NoC) architecture for implementing the hierarchical parallel genetic algorithm (HPGA) on a multi-core System-on-Chip (SoC) platform. We first derive the speedup metric of an NoC architecture which directly maps the HPGA onto NoC in order to identify the main sources of performance bottlenecks. Specifically, it is observed that the speedup is mostly affected by the fixed bandwidth that a master processor can use and the low utilization of slave processor cores. Motivated by the theoretical analysis, we propose a new architecture with two multiplexing schemes, namely dynamic injection bandwidth multiplexing (DIBM) and time-division based island multiplexing (TDIM), to improve the speedup and reduce the hardware requirements. Moreover, a task-aware adaptive routing algorithm is designed for the proposed architecture, which can take advantage of the proposed multiplexing schemes to further reduce the hardware overhead. We demonstrate the benefits of our approach using the problem of protein folding prediction, which is a process of importance in biology. Our experimental results show that the proposed NoC architecture achieves up to 240X speedup compared to a single island design. The hardware cost is also reduced by 50% compared to a direct NoC-based HPGA implementation.

Modeling populations of micro-robots for biological applications

In order to detect and cure diseases affecting microscopic regions of the human body, swarms of micro-robots can harness the motility of bacteria and swim towards inaccessible regions of the body to detect abnormal behavior and/or deliver various drugs. In this paper, we propose a modeling approach for the dynamics of dense networks of swarms and estimate their performance via stochastic metrics.

Miniature Devices in the Wild: Modeling Molecular Communication in Complex Extracellular Spaces

Miniature devices voyaging inside the human body for diagnostic and drug delivery purposes is no longer a wild dream. At the very heart of such an endeavor lies the capability of miniature devices like synthetic cells and microrobots to achieve complex tasks collectively by exchanging information molecules. Towards this end, we model the spatiotemporal dynamics of the molecular transport process in complex extracellular spaces (ECSs) such that the signaling delay can be accurately predicted. More precisely, we use parameters like ECS volume fraction, tortuosity, and cross-section area of diffusion paths to capture the physicochemical features of the ECS. Based on these parameters, we propose a new algorithm to calculate the directional diffusion coefficient, which is then used in an effective diffusion equation to describe the molecular transport process across the region of interest. Our modeling results show good agreement with detailed 3D simulations in complex ECSs, while the classical diffusion and previous approaches fail to capture the heterogeneity and directionality of the transport process. Consequently, the proposed approach represents a major step towards characterizing the interaction of cooperative miniature devices that can achieve complex tasks via diffusion-based molecular communication.

Multi-fractal characterization of bacterial swimming dynamics: a case study on real and simulated Serratia marcescens

To add to the current state of knowledge about bacterial swimming dynamics, in this paper, we study the fractal swimming dynamics of populations of Serratia marcescens bacteria both in vitro and in silico, while accounting for realistic conditions like volume exclusion, chemical interactions, obstacles and distribution of chemoattractant in the environment. While previous research has shown that bacterial motion is non-ergodic, we demonstrate that, besides the non-ergodicity, the bacterial swimming dynamics is multi-fractal in nature. Finally, we demonstrate that the multi-fractal characteristic of bacterial dynamics is strongly affected by bacterial density and chemoattractant concentration.

Towards Autonomous Control of Molecular Communication in Populations of Bacteria

Quorum sensing is a chemical communication process bacteria use to sense population density and regulate their collective behavior. By using quorum sensing inhibitors that degrade quorum sensing molecules and inactivate their receptors, one can inhibit bacterial pathogenesis, such as the production of virulence and biofilm development. To keep the level of quorum sensing molecules below the activation threshold, we propose a biological controller that can generate different concentration levels of inhibitors under different environment conditions. We provide a detailed analysis of our proposed controller that includes system response, stability, and sensitivity analysis. We also discuss the autonomous controller design under specified environment constraints and validate our results via simulation. This work represents a first step towards a paradigm change in reducing bacterial pathogenesis via controlling the dynamics of bacterial cell-to-cell communication network.

Molecular tweeting: unveiling the social network behind heterogeneous bacteria populations

It is well established that bacteria engage in social behavior and form networked communities via molecular signaling. However, most studies published to date focus on the intracellular molecular networks rather than the intercellular networks formed across strains and species. Therefore, in this paper, we define for the first time a bacteria intercellular network and describe its dynamics and contribution to biofilm formation. We apply our methods to a heterogeneous bacteria population consisting of strains that are often identified from clinical isolates, namely, the wild-type (co-operator), and its signal-blind and signal-negative (cheater) mutants. We analyze the network dynamics and biofilm metrics, showing that our method can effectively reveal the underlying intercellular communication process and community organization within the biofilm. We claim that the application of social and network sciences to understanding bacteria population dynamics can aid in developing better drugs to control the many pathogenic bacteria that use social interactions to cause infections.

A Reconfigurable Wireless NoC for Large Scale Microbiome Community Analysis

Understanding the role of competition and cooperation among multiple interacting species of microorganisms that constitute the microbiome and decipher how they enforce homeostasis or trigger diseases requires the development of multi-scale computational models capable of capturing both intra-cell processing (i.e., gene-to-protein interactions) and inter-cell interactions. The multi-scale interdependency that governs the interactions from genes to proteins within a cell and from molecular messengers to cells to microbial communities within the environment raises numerous computation and communication challenges. Internal cell processing cannot be simulated without knowledge of the surroundings. Similarly, cell-cell communication cannot be fully abstracted without stated of internal processing and diffusion effects of molecular messengers. To address the compute- and communication-intensive nature of modeling microbial communities, in this paper, we propose a novel reconfigurable NoC-based manycore architecture capable of simulating a large scale microbial community. The reconfiguration of the NoC topology is achieved through the fractal analysis of NoC traffic and use of the on-chip wireless interfaces. More precisely, we analyze the computational and communication workloads and exploit the observed fractal characteristics for proposing a mathematical strategy for NoC reconfiguration. Experimental results demonstrate that the proposed NoC architecture achieves 56.6 and 62.8 percent improvement in energy delay product over the conventional wireline mesh and flatten butterfly-based high radix NoC architectures, respectively.