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Pili are used by Escherichia coli to attach to and invade mammalian tissues during host infection and colonization. Expression of type 1 pili, believed to act as virulence factors in urinary tract infections, is under control of the 'firm' genetic network. This network is able to sense the environment and actuate phase variation control. It is a prime exemplar of an integrative regulatory system because of its role in mediating a complex infection process, and because it instantiates a number of regulatory motifs, including DNA inversion and stochastic variation. With the help of a mathematical model, we explore the mechanisms and architecture of the fim network. We explain (1) basic network operation, including the roles of the recombinase and global regulatory protein concentrations, their DNA binding affinities, and their switching rates in observed phase variation behavior; (2) why there are two recombinases when one would seem to suffice; (3) the source of on-to-off switching specificity of FimE; (4) the role of fimE orientational control in switch dynamics; and (5) how temperature tuning of piliation is achieved. In the process, we identify a general regulatory motif that tunes phenotype to an environmental variable, and explain a number of apparent experimental inconsistencies.

Dynamical modeling of biological systems is becoming increasingly widespread as people attempt to grasp biological phenomena in their full complexity and make sense of an accelerating stream of experimental data. We review a number of recent modeling studies that focus on systems specifically involving gene expression and regulation. These systems include bacterial metabolic operons and phase-variable piliation, bacteriophages T7 and lambda, and interacting networks of eukaryotic developmental genes. A wide range of conceptual and mathematical representations of genetic components and phenomena appears in these works. We discuss these representations in depth and give an overview of the tools currently available for creating and exploring dynamical models. We argue that for modeling to realize its full potential as a mainstream biological research technique the tools must become more general and flexible, and formal, standardized representations of biological knowledge and data must be developed.

Synthesis of data into formal models of cellular function is rapidly becoming a necessary industry. The complexity of the interactions among cellular constituents and the quantity of data about these interactions hinders the ability to predict how cells will respond to perturbation and how they can be engineered for industrial or medical purposes. Models provide a systematic framework to describe and analyze these complex systems. In the past few years, models have begun to have an impact on mainstream biology by creating deeper insight into the design rules of cellular signal processing, providing a basis for rational engineering of cells, and for resolving debates about the root causes of certain cellular behaviors. This review covers some of the recent work and challenges in developing these "synthetic cell" models and their growing practical applications.

A number of technological innovations are yielding unprecedented data on the networks of biochemical, genetic, and biophysical reactions that underlie cellular behavior and failure. These networks are composed of hundreds to thousands of chemical species and structures, interacting via nonlinear and possibly stochastic physical processes. A central goal of modern biology is to optimally use the data on these networks to understand how their design leads to the observed cellular behaviors and failures. Ultimately, this knowledge should enable cellular engineers to redesign cellular processes to meet industrial needs (such as optimal natural product synthesis), aid in choosing the most effective targets for pharmaceuticals, and tailor treatment for individual genotypes. The size and complexity of these networks and the inevitable lack of complete data, however, makes reaching these goals extremely difficult. If it proves possible to modularize these networks into functional subnetworks, then these smaller networks may be amenable to direct analysis and might serve as regulatory motifs. These motifs, recurring elements of control, may help to deduce the structure and function of partially known networks and form the basis for fulfilling the goals described above. A number of approaches to identifying and analyzing control motifs in intracellular networks are reviewed.

Transient elevations in the concentration of free cytosolic calcium ion ([Ca2+]i) promote cell phase transitions in early embryonic division and persist even if these transitions are blocked. These observations suggest that a [Ca2+]i oscillator is an essential timing element of the early embryonic "master clock." We explore this possibility by coupling a [Ca2+]i oscillator model to an early embryonic cell cycle model based on the protein interactions that govern the activity of the M-phase-promoting factor (MPF). We hypothesize three dynamical states of the MPF system and choose parameter sets to represent each. We then investigate how [Ca2+]i dynamics may control early embryonic division in both sea urchin and Xenopus embryos. To investigate both systems, distinct [Ca2+]i profiles matching those observed in sea urchin embryos (in which [Ca2+]i exhibits sharp transients) and Xenopus embryos (in which [Ca2+]i is elevated and oscillates sinusoidally) are imposed on each of the hypothesized dynamical states of MPF. In the first hypothesis, [Ca2+]i oscillations entrain the autonomous MPF oscillator. In the second and third hypotheses, where the MPF system rests in excitatory and bistable states, respectively, [Ca2+]i oscillations drive MPF activation cycles. Simulation results show that hypotheses two and three, in which a [Ca2+]i oscillator is a fundamental timing element of the master clock, best account for key experimental observations and the questions that they raise. Finally, we propose experiments to elucidate further [Ca2+]i regulation and the fundamental components of the early embryonic master clock.

An algorithm for protein engineering, termed recursive ensemble mutagenesis, has been developed to produce diverse populations of phenotypically related mutants whose members differ in amino acid sequence. This method uses a feedback mechanism to control successive rounds of combinatorial cassette mutagenesis. Starting from partially randomized "wild-type" DNA sequences, a highly parallel search of sequence space for peptides fitting an experimenter's criteria is performed. Each iteration uses information gained from the previous rounds to search the space more efficiently. Simulations of the technique indicate that, under a variety of conditions, the algorithm can rapidly produce a diverse population of proteins fitting specific criteria. In the experimental analog, genetic selection or screening applied during recursive ensemble mutagenesis should force the evolution of an ensemble of mutants to a targeted cluster of related phenotypes.

In random mutagenesis, synthesis of an NNN triplet (i.e. equiprobable A, C, G, and T at each of the three positions in the codon) could be considered an optimal nucleotide mixture because all 20 amino acids are encoded. NN(G,C) might be considered a slightly more intelligent "dope" because the entire set of amino acids is still encoded using only half as many codons. Using a general algorithm described herein, it is possible to formulate more complex doping schemes which encode specific subsets of the twenty amino acids, excluding others from the mix. Maximizing the equiprobability of amino acid residues contributing to such a subset is suggested as an optimal basis for performing semi-random mutagenesis. This is important for reducing the nucleotide complexity of combinatorial cassettes so that "sequence space" can be searched more efficiently. Computer programs have been developed to provide tables of optimized dopes compatible with automated DNA synthesizers.

Digital imaging spectroscopy has been used to obtain the grayscale spectrum of colored bacterial colonies directly from petri dishes. Up to 500 individual colony spectra can be simultaneously recorded and processed from a single plate. Spectra can be obtained in the visible to near infrared region (400nm-900nm) with 10nm resolution. Instrument response is normalized through run-time radiometric calibration such that each grayscale spectrum can be converted to the ground-state absorption spectrum of the colony. In this study, mutants of the photosynthetic bacterium Rhodobacter capsulatus have been differentiated by the absorption spectra of their pigment-protein complexes. This imaging technique is applicable to chromogenic systems in which colony and/or media color (e.g. indicator plates) provides a quantitative indicator of gene expression.