Limits and tradeoffs in the control of autocatalytic systems

Despite the complexity of biological networks, we find that certain common architectures govern network structures. These architectures impose fundamental constraints on system performance and create tradeoffs that the system must balance in the face of uncertainty in the environment. This means that while a system may be optimized for a specific function through evolution, the optimal achievable state must follow these constraints. One such constraining architecture is autocatalysis, as seen in many biological networks including glycolysis and ribosomal protein synthesis. Using a minimal model, we show that ATP autocatalysis in glycolysis imposes stability and performance constraints and that the experimentally well-studied glycolytic oscillations are in fact a consequence of a tradeoff between error minimization and stability. We also show that additional complexity in the network results in increased robustness. Ribosome synthesis is also autocatalytic where ribosomes must be used to make more ribosomal proteins. When ribosomes have higher protein content, the autocatalysis is increased. We show that this autocatalysis destabilizes the system, slows down response, and also constrains the system’s performance. On a larger scale, transcriptional regulation of whole organisms also follows architectural constraints and this can be seen in the differences between bacterial and yeast transcription networks. We show that the degree distributions of bacterial transcription network follow a power law distribution while the yeast network follows an exponential distribution. We then explored the evolutionary models that have previously been proposed and show that neither the preferential linking model nor the duplication-divergence model of network evolution generates the power-law, hierarchical structure found in bacteria. However, in real biological systems, the generation of new nodes occurs through both duplication and horizontal gene transfers, and we show that a biologically reasonable combination of the two mechanisms generates the desired network.

The Flocculation of E. coli with Polyethyleneimine

A comprehensive study was made of the flocculation of dispersed E. coli bacterial cells by the cationic polymer polyethyleneimine (PEI). The three objectives of this study were to determine the primary mechanism involved in the flocculation of a colloid with an oppositely charged polymer, to determine quantitative correlations between four commonly-used measurements of the extent of flocculation, and to record the effect of varying selected system parameters on the degree of flocculation. The quantitative relationships derived for the four measurements of the extent of flocculation should be of direct assistance to the sanitary engineer in evaluating the effectiveness of specific coagulation processes.

A review of prior statistical mechanical treatments of absorbed polymer configuration revealed that at low degrees of surface site coverage, an oppositely- charged polymer molecule is strongly adsorbed to the colloidal surface, with only short loops or end sequences extending into the solution phase. Even for high molecular weight PEI species, these extensions from the surface are theorized to be less than 50 Å in length. Although the radii of gyration of the five PEI species investigated were found to be large enough to form interparticle bridges, the low surface site coverage at optimum flocculation doses indicates that the predominant mechanism of flocculation is adsorption coagulation.

The effectiveness of the high-molecular weight PEI species 1n producing rapid flocculation at small doses is attributed to the formation of a charge mosaic on the oppositely-charged E. coli surfaces. The large adsorbed PEI molecules not only neutralize the surface charge at the adsorption sites, but also cause charge reversal with excess cationic segments. The alignment of these positive surface patches with negative patches on approaching cells results in strong electrostatic attraction in addition to a reduction of the double-layer interaction energies. The comparative ineffectiveness of low-molecular weight PEI species in producing E. coli flocculation is caused by the size of the individual molecules, which is insufficient to both neutralize and reverse the negative E.coli surface charge. Consequently, coagulation produced by low molecular weight species is attributed solely to the reduction of double-layer interaction energies via adsorption.

Electrophoretic mobility experiments supported the above conclusions, since only the high-molecular weight species were able to reverse the mobility of the E. coli cells. In addition, electron microscope examination of the seam of agglutination between E. coli cells flocculation by PEI revealed tightly- bound cells, with intercellular separation distances of less than 100-200 Å in most instances. This intercellular separation is partially due to cell shrinkage in preparation of the electron micrographs.

The extent of flocculation was measured as a function of PEl molecular weight, PEl dose, and the intensity of reactor chamber mixing. Neither the intensity of mixing, within the common treatment practice limits, nor the time of mixing for up to four hours appeared to play any significant role in either the size or number of E.coli aggregates formed. The extent of flocculation was highly molecular weight dependent: the high-molecular-weight PEl species produce the larger aggregates, the greater turbidity reductions, and the higher filtration flow rates. The PEl dose required for optimum flocculation decreased as the species molecular weight increased. At large doses of high-molecular-weight species, redispersion of the macroflocs occurred, caused by excess adsorption of cationic molecules. The excess adsorption reversed the surface charge on the E.coli cells, as recorded by electrophoretic mobility measurements.

Successful quantitative comparisons were made between changes in suspension turbidity with flocculation and corresponding changes in aggregate size distribution. E. coli aggregates were treated as coalesced spheres, with Mie scattering coefficients determined for spheres in the anomalous diffraction regime. Good quantitative comparisons were also found to exist between the reduction in refiltration time and the reduction of the total colloid surface area caused by flocculation. As with turbidity measurements, a coalesced sphere model was used since the equivalent spherical volume is the only information available from the Coulter particle counter. However, the coalesced sphere model was not applicable to electrophoretic mobility measurements. The aggregates produced at each PEl dose moved at approximately the same vlocity, almost independently of particle size.

PEl was found to be an effective flocculant of E. coli cells at weight ratios of 1 mg PEl: 100 mg E. coli. While PEl itself is toxic to E.coli at these levels, similar cationic polymers could be effectively applied to water and wastewater treatment facilities to enhance sedimentation and filtration characteristics.

Gut microbiota promote hematopoiesis to control bacterial infection

The commensal microbiota impacts specific immune cell populations and their functions at peripheral sites, such as gut mucosal tissues. However, it remains unknown whether gut microbiota control immunity through regulation of hematopoiesis at primary immune sites. We reveal that germ-free mice display reduced proportions and differentiation potential of specific myeloid cell progenitors of both yolk sac and bone marrow origin. Homeostatic innate immune defects may lead to impaired early responses to pathogens. Indeed, following systemic infection with Listeria monocytogenes, germ-free and oral antibiotic-treated mice display increased pathogen burden and acute death. Recolonization of germ-free mice with a complex microbiota restores defects in myelopoiesis and resistance to Listeria. These findings reveal that gut bacteria direct innate immune cell development via promoting hematopoiesis, contributing to our appreciation of the deep evolutionary connection between mammals and their microbiota.

Sequence-function relationships in E. coli transcriptional regulation

Understanding how transcriptional regulatory sequence maps to regulatory function remains a difficult problem in regulatory biology. Given a particular DNA sequence for a bacterial promoter region, we would like to be able to say which transcription factors bind there, how strongly they bind, and whether they interact with each other and/or RNA polymerase, with the ultimate objective of integrating knowledge of these parameters into a prediction of gene expression levels. The theoretical framework of statistical thermodynamics provides a useful framework for doing so, enabling us to predict how gene expression levels depend on transcription factor binding energies and concentrations. We used thermodynamic models, coupled with models of the sequence-dependent binding energies of transcription factors and RNAP, to construct a genotype to phenotype map for the level of repression exhibited by the lac promoter, and tested it experimentally using a set of promoter variants from E. coli strains isolated from different natural environments. For this work, we sought to ``reverse engineer'' naturally occurring promoter sequences to understand how variations in promoter sequence affects gene expression. The natural inverse of this approach is to ``forward engineer'' promoter sequences to obtain targeted levels of gene expression. We used a high precision model of RNAP-DNA sequence dependent binding energy, coupled with a thermodynamic model relating binding energy to gene expression, to predictively design and verify a suite of synthetic E. coli promoters whose expression varied over nearly three orders of magnitude.

However, although thermodynamic models enable predictions of mean levels of gene expression, it has become evident that cell-to-cell variability or ``noise'' in gene expression can also play a biologically important role. In order to address this aspect of gene regulation, we developed models based on the chemical master equation framework and used them to explore the noise properties of a number of common E. coli regulatory motifs; these properties included the dependence of the noise on parameters such as transcription factor binding strength and copy number. We then performed experiments in which these parameters were systematically varied and measured the level of variability using mRNA FISH. The results showed a clear dependence of the noise on these parameters, in accord with model predictions.

Finally, one shortcoming of the preceding modeling frameworks is that their applicability is largely limited to systems that are already well-characterized, such as the lac promoter. Motivated by this fact, we used a high throughput promoter mutagenesis assay called Sort-Seq to explore the completely uncharacterized transcriptional regulatory DNA of the E. coli mechanosensitive channel of large conductance (MscL). We identified several candidate transcription factor binding sites, and work is continuing to identify the associated proteins.