Distributed Bio-computing

A coordinated distributed network involving multiple computers can be used to take on large computational tasks. Likewise, in the realm of genetic circuits, researchers have proposed multicellular strategies to overcome the limitations of scaling up within single cells. However, existing instances of distributed biological circuits have predominantly relied on a limited repertoire of molecular signals borrowed from nature as external messages for intercellular communication. In this research theme, we aim to develop synthetic orthogonal signals to enable cell-to-cell communication within bacterial communities, and the accompanying computational primitives. These engineered signals will serve as the foundation for scaling up multicellular circuits for complex computational tasks.

Genetic Circuit Design

As synthetic genetic circuits grow in size, they become increasingly challenging for individual cells to maintain and run. The cellular resources required for these circuits impose a burden on the cell, leading to reduced long-term stability, and consequent susceptibility to negative selection that results in their loss. In contrast, natural systems manage to accommodate significantly larger genetic programs by employing intricate regulatory mechanisms that effectively regulate the costs associated with gene expression. In this research theme, our objective is to establish both experimental and theoretical methodologies for quantifying the expenses/ burden associated with maintaining and running synthetic genetic circuits within cells. By leveraging these methodologies, we aim to identify the most efficient design architectures that enable the implementation of a genetic circuit with a specific function while minimizing costs.

Biological Systems Modeling and Simulation

This theme of our research focuses on the modeling and simulation of biological systems across various levels of biological organization. We utilize experimental data to construct realistic models using the chemical reaction networks (CRN) formalism, allowing us to simulate these systems using both deterministic and stochastic methods. Starting from molecular-level modeling, we aim to develop computer-aided-design (CAD) tools for the automated design of biological circuits. These circuits will be designed to serve various applications, including information processing circuits within individual bacteria or in bacterial consortia of multiple cells. By integrating experimental data, mathematical modeling, and computational tools, our goal is to advance the field of synthetic biology and enable the efficient design of biological circuits tailored to specific requirements.