Design Constraints on a Synthetic Metabolism

A metabolism is a complex network of chemical reactions that converts sources of energy and chemical elements into biomass and other molecules. To design a metabolism from scratch and to implement it in a synthetic genome is almost within technological reach. Ideally, a synthetic metabolism should be able to synthesize a desired spectrum of molecules at a high rate, from multiple different nutrients, while using few chemical reactions, and producing little or no waste. Not all of these properties are achievable simultaneously. We here use a recently developed technique to create random metabolic networks with pre-specified properties to quantify trade-offs between these and other properties. We find that for every additional molecule to be synthesized a network needs on average three additional reactions. For every additional carbon source to be utilized, it needs on average two additional reactions. Networks able to synthesize 20 biomass molecules from each of 20 alternative sole carbon sources need to have at least 260 reactions. This number increases to 518 reactions for networks that can synthesize more than 60 molecules from each of 80 carbon sources. The maximally achievable rate of biosynthesis decreases by approximately 5 percent for every additional molecule to be synthesized. Biochemically related molecules can be synthesized at higher rates, because their synthesis produces less waste. Overall, the variables we study can explain 87 percent of variation in network size and 84 percent of the variation in synthesis rate. The constraints we identify prescribe broad boundary conditions that can help to guide synthetic metabolism design

Escherichia coli proteomics and bioinformatics

A lot of things happen to proteins when Escherichia coli cells enter stationary phase, such as protein amount, post-translational modifications, conformation changes, and component of protein complex. Proteomics, which study the whole component of proteins, can be used to study the products of the genome and the physiology of Escherichia coli cells at different conditions. By comparing proteome from different growth phases, such as exponential and stationary phase, a lot of proteins with changes can be identified at the same time, which provides a pilot for further studies of mechanism. Current global proteomic studies have identified about 27% of the annotated proteins of E. coli, most of which are predicted to be abundance proteins. Subproteomics, the study of specific subsets of the proteome, can be used to study specific functional classes of proteins and low abundance proteins. In this dissertation, using non-denatured anion exchange column with 2D SDS-PAGE and tandem mass spectrometry, difference of E. coli cells between exponential and stationary phase were studied for whole soluble proteome. Also, using heparin column and mass spectrometry with tandem mass spectrometry, heparin-binding proteins were identified and analyzed for exponential growth and stationary phases. To manage and display the data generated by proteomics, a web-based database has been constructed for experiments in E. coli proteomics (EEP), which includes NonDeLC, Heparome, AIX/2D PAGE and other proteomic studies

Genes and proteins of Escherichia coli K-12 (GenProtEC).

GenProtEC is a database of Escherichia coli genes and their gene products, classified by type of function and physiological role and with citations to the literature for each. Also present are data on sequence similarities amongE.coliproteins with PAM values, percent identity of amino acids, length of alignment and percent aligned. GenProtEC can also be accessed through the World Wide Web at URL http://mbl.edu/html/ecoli.html