Modeling growth and adaptation in bacteria

Bakterielle Wirte wie Escherichia coli dienen der Produktion industrieller rekombinanter Proteine. Dieser Prozess verursacht systemischen Stress und führt zu umfangreichen Veränderungen in mRNA- und Proteinexpression. In meiner Arbeit analysiere ich Regulationsmechanismen der zellulären Reaktion auf diesen Stress. Zudem untersuche ich die zelluläre Ressourcenallokation mittels eines stationären Ganzzellmodells von E. coli, basierend auf der Resource Balance Analysis. Das Modell berücksichtigt Kosten zellulärer Prozesse und Einschränkungen wie Energie, Effizienz und Raum. Es unterstützt die Experimentplanung in der Bioproduktion. Weiterhin habe ich an der Entwicklung von RBApy mitgewirkt, einer Software zur Erstellung und Simulation von RBA-Modellen. Schließlich entwickle ich ein Modell zur Untersuchung der Regulation von Stressreaktionen durch die Tendenz der Zelle, wachstumsoptimale Ressourcenstrategien anzuwenden. Das Modell berücksichtigt zelluläre Beschränkungen und zeigt, dass die erhaltene Stressreaktion der experimentell ermittelten Reaktion ähnelt. Die Integration von Ressourcenzuteilung in Zellmodelle ermöglicht Einsichten in regulatorische Ereignisse und Anpassungen während der Bioproduktion, was zur Optimierung der rekombinanten Proteinexpression in Escherichia coli beiträgt.Bacterial hosts such as Escherichia coli are used for the production of industrial recombinant proteins. This process causes systemic stress and leads to extensive changes in mRNA and protein expression. In my work, I analyze regulatory mechanisms of the cellular response to this stress. In addition, I investigate cellular resource allocation using a steady-state whole-cell model of E. coli based on resource balance analysis. The model accounts for costs of cellular processes and constraints such as energy, efficiency, and space. It supports experiment design in bioproduction. Furthermore, I contributed to the development of RBApy, a software to create and simulate RBA models. Finally, I developed a model to study the regulation of stress responses by the tendency of the cell to adopt growth-optimal resource strategies. The model accounts for cellular constraints and shows that the obtained stress response resembles the experimentally determined response. Integrating resource allocation into cell models provides insights into regulatory events and adaptations during bioproduction, which contributes to the optimization of recombinant protein expression in Escherichia coli

Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling

We propose a method for finding metabolic parameters of cells, organs and whole organisms, which is based on the earlier discovered general growth law. Based on the obtained results and analysis of available biological models, we propose a general framework for modeling biological phenomena and discuss how it can be used in Virtual Liver Network project. The foundational idea of the study is that growth of cells, organs, systems and whole organisms, besides biomolecular machinery, is influenced by biophysical mechanisms acting at different scale levels. In particular, the general growth law uniquely defines distribution of nutritional resources between maintenance needs and biomass synthesis at each phase of growth and at each scale level. We exemplify the approach considering metabolic properties of growing human and dog livers and liver transplants. A procedure for verification of obtained results has been introduced too. We found that two examined dogs have high metabolic rates consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per day, and verified this using the proposed verification procedure. We also evaluated consumption rate of nutrients in human livers, determining it to be about 0.088 gram of nutrients per cubic centimeter of liver per day for males, and about 0.098 for females. This noticeable difference can be explained by evolutionary development, which required females to have greater liver processing capacity to support pregnancy. We also found how much nutrients go to biomass synthesis and maintenance at each phase of liver and liver transplant growth. Obtained results demonstrate that the proposed approach can be used for finding metabolic characteristics of cells, organs, and whole organisms, which can further serve as important inputs for many applications in biology (protein expression), biotechnology (synthesis of substances), and medicine.Comment: 20 pages, 6 figures, 4 table

Nanoporous silica-based protocells at multiple scales for designs of life and nanomedicine.

Various protocell models have been constructed de novo with the bottom-up approach. Here we describe a silica-based protocell composed of a nanoporous amorphous silica core encapsulated within a lipid bilayer built by self-assembly that provides for independent definition of cell interior and the surface membrane. In this review, we will first describe the essential features of this architecture and then summarize the current development of silica-based protocells at both micro- and nanoscale with diverse functionalities. As the structure of the silica is relatively static, silica-core protocells do not have the ability to change shape, but their interior structure provides a highly crowded and, in some cases, authentic scaffold upon which biomolecular components and systems could be reconstituted. In basic research, the larger protocells based on precise silica replicas of cells could be developed into geometrically realistic bioreactor platforms to enable cellular functions like coupled biochemical reactions, while in translational research smaller protocells based on mesoporous silica nanoparticles are being developed for targeted nanomedicine. Ultimately we see two different motivations for protocell research and development: (1) to emulate life in order to understand it; and (2) to use biomimicry to engineer desired cellular interactions

Novel Strategies for Drug Discovery Based on Intrinsically Disordered Proteins (IDPs)

Intrinsically disordered proteins (IDPs) are proteins that usually do not adopt well-defined native structures when isolated in solution under physiological conditions. Numerous IDPs have close relationships with human diseases such as tumor, Parkinson disease, Alzheimer disease, diabetes, and so on. These disease-associated IDPs commonly play principal roles in the disease-associated protein-protein interaction networks. Most of them in the disease datasets have more interactants and hence the size of the disease-associated IDPs interaction network is simultaneously increased. For example, the tumor suppressor protein p53 is an intrinsically disordered protein and also a hub protein in the p53 interaction network; α-synuclein, an intrinsically disordered protein involved in Parkinson diseases, is also a hub of the protein network. The disease-associated IDPs may provide potential targets for drugs modulating protein-protein interaction networks. Therefore, novel strategies for drug discovery based on IDPs are in the ascendant. It is dependent on the features of IDPs to develop the novel strategies. It is found out that IDPs have unique structural features such as high flexibility and random coil-like conformations which enable them to participate in both the “one to many” and “many to one” interaction. Accordingly, in order to promote novel strategies for drug discovery, it is essential that more and more features of IDPs are revealed by experimental and computing methods

Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into ‘cumulus-type’, i.e., those similar to puffy (white) clouds, and ‘stratus-type’, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an ‘energy transfer’ mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by ‘multi-trajectories’; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach ‘rarely visited’ but functionally-related states. We also show the role of disorder in ‘spatial games’ of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks

Integration of Mass Spectrometry Data for Structural Biology

Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies

How the projection domains of NF-L and alpha-internexin determine the conformations of NF-M and NF-H in neurofilaments

Making use of a numerical self-consistent field method and polymer brush concepts, we model the solvated corona of neurofilaments (NF) composed of projection domains (unstructured tails) of constituent proteins. Projections are modeled with amino acid resolution. We focus on the importance of the two shortest ones (alpha-internexin and NF-L) in regulating the conformations of the two longer ones (NF-M and NF-H) in an isolated NF. We take the wild-type NF with no alpha-internexin as the reference, for which the phosphorylation-induced translocation of M- and H-tails has been examined previously. We demonstrate that a subbrush of L-tails creates an electrostatic potential profile with an approximately parabolic shape. An experimentally relevant (2:1) ratio of L- to alpha-projections reduces the charge density of the L subbrush and shifts the translocation transition of the H-tails to slightly higher degrees of phosphorylation. Replacing all L-tails by alpha-projections destroys the substructure of the NF corona and this alters the NF response to the phosphorylation of long tail

Unifying metabolic networks, regulatory constraints, and resource allocation

Metabolic and gene regulatory networks are two classic models of systems biology. Biologically, gene regulatory networks are the control system of protein expression while metabolic networks, especially the genome-scale reconstructions consist of thousands of enzymatic reactions breaking down nutrients into precursors and energy to support the cellular survival. Metabolic-genetic networks, in addition, include the translational processes as an integrated model of classical metabolic networks and the gene expression machinery. Conversely, genetic regulation is also affected by the metabolic activities that provide feedbacks and precursors to the regulatory system. Thus, the two systems are highly interactive and depend on each other. Up to now, various efforts have been made to bridge the two network types. Yet, the dynamic integration of metabolic networks and genetic regulation remains a major challenge in computational systems biology. This PhD thesis is a contribution to mathematical modeling approaches for studying metabolic-regulatory systems. Inspired by regulatory flux balance analysis (rFBA), we first propose an analytic pipeline to explore the optimal solution space in rFBA. Then, our efforts focus on the dynamic combination of metabolic networks together with enzyme production costs and genetic regulation. For this purpose, we first explore the intuitive idea that incorporates Boolean regulatory rules while iterating resource balance analysis. However, with the iterative strategy, the gene expression states are only updated in discrete time steps. Furthermore, formalizing the metabolic-regulatory networks (MRNs) by hybrid automata provides a new mathematical framework that allows the quantitative integration of the metabolic-genetic network with the genetic regulation in a hybrid discrete-continuous system. For the application of this theoretical formalization, we develop a constraint-based approach regulatory dynamic enzyme-cost flux balance analysis (r-deFBA) as an optimal control strategy for the hybrid automata representing MRNs. This allows the prediction of optimal regulatory state transitions, dynamics of metabolism, and resource allocation capable of achieving a maximal biomass production over a time interval. Finally, this PhD project ends with a chapter on perspectives; we apply the theory of product automata to model the dynamics at population-level, integrating continuous metabolism and discrete regulatory states

A physical model of cell metabolism

Cell metabolism is characterized by three fundamental energy demands: to sustain cell maintenance, to trigger aerobic fermentation and to achieve maximum metabolic rate. The transition to aerobic fermentation and the maximum metabolic rate are currently understood based on enzymatic cost constraints. Yet, we are lacking a theory explaining the maintenance energy demand. Here we report a physical model of cell metabolism that explains the origin of these three energy scales. Our key hypothesis is that the maintenance energy demand is rooted on the energy expended by molecular motors to fluidize the cytoplasm and counteract molecular crowding. Using this model and independent parameter estimates we make predictions for the three energy scales that are in quantitative agreement with experimental values. The model also recapitulates the dependencies of cell growth with extracellular osmolarity and temperature. This theory brings together biophysics and cell biology in a tractable model that can be applied to understand key principles of cell metabolism

Microscopy Techniques for Investigating Interactions in Microbial Systems

Biological interactions occur on multiple length scales, ranging from molecular to population wide interactions. This work describes the study of two specific areas of biological interactions in microbial systems: intracellular protein-protein interactions and cell-to-cell interactions. The implementation of optical and atomic force microscopy and the methodologies developed during this study proved to be invaluable tools for investigating these systems. Identifying and characterizing protein interactions are fundamental steps toward understanding complex cellular networks. We have developed a unique methodology which combines an imaging-based protein interaction assay with a fluorescence recovery after photobleaching technique (FRAP). Protein interactions are readily detected by co-localization of two proteins of interest fused to green fluorescent protein (GFP) and DivIVA, a cell division protein from Bacillus subtilis. We demonstrate that the modified co-localization assay is sensitive enough to detect protein interactions over four orders of magnitude. FRAP data was analyzed using a combination of various image processing techniques and analytical models. This combined approach made it possible to estimate cell morphology parameters such as length, diameter, the effective laser probe volume, as well as to the mobile protein concentration in vivo, the number of bound molecules at the cellular poles, and the biophysical parameter koff. Cells not only utilize molecular interactions in the intracellular environment, but also express proteins, polysaccharides and other complex molecules to mediate interactions with the surrounding extracellular environment. In Azospirillum brasilense, cell surface properties, including exopolysaccharide production, are thought to play a direct role in promoting cell-to-cell interactions. Recently, the Che1 chemotaxis-like pathway from A. brasilense was shown to modulate flocculation, suggesting an associated modulation of cell surface properties. Using atomic force microscopy, distinct changes in the surface morphology of flocculating A. brasilense Che1 mutant strains were detected. Further analyses suggest that the extracellular matrix differs between the cheA1 and the cheY1 deletion mutants, despite similarity in the macroscopic floc structures. Collectively, these data indicate that disruption of the Che1 pathway is correlated with distinctive changes in the extracellular matrix, which likely result from changes in surface polysaccharides structure and/or composition