Jason K Suen | PubMed Health

PubMed2005

A synthetic gene-metabolic oscillator.

Autonomous oscillations found in gene expression and metabolic, cardiac and neuronal systems have attracted significant attention both because of their obvious biological roles and their intriguing dynamics. In addition, de novo designed oscillators have been demonstrated, using components that are not part of the natural oscillators. Such oscillators are useful in testing the design principles and in exploring potential applications not limited by natural cellular behaviour. To achieve transcriptional and metabolic integration characteristic of natural oscillators, here we designed and constructed a synthetic circuit in Escherichia coli K12, using glycolytic flux to generate oscillation through the signalling metabolite acetyl phosphate. If two metabolite pools are interconverted by two enzymes that are placed under the transcriptional control of acetyl phosphate, the system oscillates when the glycolytic rate exceeds a critical value. We used bifurcation analysis to identify the boundaries of oscillation, and verified these experimentally. This work demonstrates the possibility of using metabolic flux as a control factor in system-wide oscillation, as well as the predictability of a de novo gene-metabolic circuit designed using nonlinear dynamic analysis.

Biological Clocks↗

PubMed2005

gNCA: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation.

Network Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to classical approaches such as principal component analysis or independent component analysis, NCA makes use of the connectivity structure from transcriptional regulatory networks to restrict the decomposition to a unique solution. However, the existing version of NCA cannot incorporate information beyond the network topology such as information obtained from regulatory gene knockouts that constrain the dynamics of regulatory signals. The ability of incorporating such information enables a more accurate and self-consistent analysis over different experiments and extends NCA to systems that may not satisfy the identifiability criteria of NCA. In this paper, we derive a generalized form of NCA, gNCA, which significantly expands the capability of transcription network analysis by incorporating regulatory signal constraints arising from genetic knockouts. The theoretical bases including criteria for uniqueness of solution and distinguishability between networks are derived. In addition, numerical techniques for robust decomposition are discussed. gNCA is then demonstrated using an Escherichia coli wild-type strain and an isogenic arcA deletion mutant during a carbon source transition.

Bacterial Outer Membrane Proteins↗