Host-pathogen interactions between the human innate immune system and Candida albicans—understanding and modeling defense and evasion strategies

The diploid, polymorphic yeast Candida albicans is one of the most important humanpathogenic fungi. C. albicans can grow, proliferate and coexist as a commensal on or within thehuman host for a long time. Alterations in the host environment, however, can render C. albicansvirulent. In this review, we describe the immunological cross-talk between C. albicans and thehuman innate immune system. We give an overview in form of pairs of human defense strategiesincluding immunological mechanisms as well as general stressors such as nutrient limitation,pH, fever etc. and the corresponding fungal response and evasion mechanisms. FurthermoreComputational Systems Biology approaches to model and investigate these complex interactionare highlighted with a special focus on game-theoretical methods and agent-based models. Anoutlook on interesting questions to be tackled by Systems Biology regarding entangled defenseand evasion mechanisms is given

Cellular automata modeling depicts degradation of cellulosic material by a cellulase system with single-molecule resolution

BACKGROUND: Enzymatic hydrolysis of cellulose involves the spatiotemporally correlated action of distinct polysaccharide chain cleaving activities confined to the surface of an insoluble substrate. Because cellulases differ in preference for attacking crystalline compared to amorphous cellulose, the spatial distribution of structural order across the cellulose surface imposes additional constraints on the dynamic interplay between the enzymes. Reconstruction of total system behavior from single-molecule activity parameters is a longstanding key goal in the field. RESULTS: We have developed a stochastic, cellular automata-based modeling approach to describe degradation of cellulosic material by a cellulase system at single-molecule resolution. Substrate morphology was modeled to represent the amorphous and crystalline phases as well as the different spatial orientations of the polysaccharide chains. The enzyme system model consisted of an internally chain-cleaving endoglucanase (EG) as well as two processively acting, reducing and non-reducing chain end-cleaving cellobiohydrolases (CBHs). Substrate preference (amorphous: EG, CBH II; crystalline: CBH I) and characteristic frequencies for chain cleavage, processive movement, and dissociation were assigned from biochemical data. Once adsorbed, enzymes were allowed to reach surface-exposed substrate sites through “random-walk” lateral diffusion or processive motion. Simulations revealed that slow dissociation of processive enzymes at obstacles obstructing further movement resulted in local jamming of the cellulases, with consequent delay in the degradation of the surface area affected. Exploiting validation against evidence from atomic force microscopy imaging as a unique opportunity opened up by the modeling approach, we show that spatiotemporal characteristics of cellulose surface degradation by the system of synergizing cellulases were reproduced quantitatively at the nanometer resolution of the experimental data. This in turn gave useful prediction of the soluble sugar release rate. CONCLUSIONS: Salient dynamic features of cellulose surface degradation by different cellulases acting in synergy were reproduced in simulations in good agreement with evidence from high-resolution visualization experiments. Due to the single-molecule resolution of the modeling approach, the utility of the presented model lies not only in predicting system behavior but also in elucidating inherently complex (e.g., stochastic) phenomena involved in enzymatic cellulose degradation. Thus, it creates synergy with experiment to advance the mechanistic understanding for improved application. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13068-016-0463-8) contains supplementary material, which is available to authorized users

Computational Simulation of Gene Regulatory Networks Implementing an Extendable Synchronous Single-Input Delay Flip-Flop and State Machine

We present a detailed and extendable design of the first synchronous single-input delay flip-flop implemented as a gene regulatory network in Escherichia coli (E. coli). The device, which we call the BioD, has one data input (trans-acting RNA), one clock input (far-red light) and an output that reports the state of the device using green fluorescent protein (GFP). The proposed design builds on Gardner’s toggle switch, to provide a more sophisticated device that can be synchronized with other devices within or without the same cell, and which requires only one data input. We provide a mathematical model of the system and simulation results. The results show that the device behaves in line with desired functionality. Further, we discuss the constraints of the design, which pertain to ranges of parameter values. The BioD is extended via the addition of an update function and input and output interfaces. The result is the BioFSM, which constitutes a synchronous and modular finite state machine, which uses an update function to change its state, stored in the BioD. The BioFSM uses its input and output interfaces for inter-cellular communications. This opens the door to the design of a circular cellular automata (the BioCell), which is envisioned as a number of communicating E. coli colonies, each made of clones of one BioFSM

Reduced order modelling of bone resorption and formation.

The bone remodelling process, performed by the Bone Multicellular Unit (BMU) is a key multi-hierarchically regulated process, which provides and supports various functionality of bone tissue. It is also plays a critical role in bone disorders, as well as bone tissue healing following damage. Improved modelling of bone turnover processes could play a significant role in helping to understand the underlying cause of bone disorders and thus develop more effective treatment methods. Moreover, despite extensive research in the field of bone tissue engineering, bonescaffold development is still very empirical. The development of improved methods of modelling the bone remodelling process should help to develop new implant designs which encourage rapid osteointegration. There are a number of limitations with respect to previous research in the field of mathematical modelling of the bone remodelling process, including the absence of an osteocyte loop of regulation. It is within this context that this research presented in this thesis utilises a range of modelling methods to develop a framework for bone remodelling which can be used to improve treatment methods for bone disorders. The study concentrated on dynamic and steady state variables that in perspective can be used as constraints for optimisation problem considering bone remodelling or tissue remodelling with the help of the grafts/scaffolds.The cellular and combined allosteric-regulation approaches to modelling of bone turnover, based on the osteocyte loop of regulation, have been studied. Both approaches have been studied different within wide range of rate parameters. The approach to the model validation has been considered, including a statistical approach and parameter reduction approach. From a validation perspective the cellular class of modes is preferable since it has fewer parameters to validate. The optimal control framework for regulation of remodelling has been studied. Future work in to improve the models and their application to bone scaffold design applications have been considered. The study illustrates the complexity of formalisation of the metabolic processes and the relations between hierarchical subsystems in hard tissue where a relatively small number of cells are active. Different types/modes of behaviour have been found in the study: relaxational, periodical and chaotic modes. All of these types of behaviour can be found, in bone tissue. However, a chaotic or periodic modes are ones of the hardest to verify although a number of periodical phenomena have been observed empirically in bone and skeletal development. Implementation of the allosteric loop into cellular model damps other types of behaviour/modes. In this sense it improves the robustness, predictability and control of the system. The developed models represent a first step in a hierarchical model of bone tissue (system versus local effects). The limited autonomy of any organ or tissue implies differentiation on a regulatory level as well as physiological functions and metabolic differences. Implementation into the cellular phenomenological model of allosteric-like loop of regulation has been performed. The results show that the robustness of regulation can be inherited from the phenomenological model. An attempt to correlate the main bone disorders with different modes of behaviour has been undertaken using Paget’s disorder in bone, osteoporosis and some more general skeleton disorders which lead to periodical changes in bone mass, reported by some authors. However, additional studies are needed to make this hypothesis significant. The study has revealed a few interesting techniques. When studying a multidimensional phenomenon, as a bone tissue is, the visualisation and data reduction is important for analysis and interpretation of results. In the study two novel technical methods have been proposed. The first is the graphical matrix method to visualise/project the multidimensional phase space of variables into diagonal matrix of regular combination of two-dimensional graphs. This significantly simplifies the analysis and, in principle, makes it possible to visualise the phase space higher than three-dimensional. The second important technical development is the application of the Monte-Carlo method in combination with the regression method to study the character and stability of the equilibrium points of a dynamic system. The advantage of this method is that it enables the most influential parameters that affect the character and stability of the equilibrium point to be identified from a large number of the rate parameters/constants of the dynamic system. This makes the interpretation of parameters and conceptual verification of the model much easier

Controlling microbial community dynamics through engineered metabolic dependencies

Metabolic cross-feeding is an important process that can broadly shape microbial communities. Comparative genomic analysis of >6000 sequenced bacteria from diverse environments provides evidence to suggesting that amino acid biosynthesis has been broadly optimized to reduce individual metabolic burden in favor of enhanced cross-feeding to support synergistic growth across the biosphere. Still, little is known about specific cross-feeding principles that drive the formation and maintenance of individuals within a mixed population. Here, we devised a series of synthetic syntrophic communities to probe the complex interactions underlying metabolic exchange of amino acids. We experimentally analyzed multi-member, multi-dimensional communities of Escherichia coli of increasing sophistication to assess the outcomes of synergistic cross-feeding. We find that biosynthetically costly amino acids including methionine, lysine, isoleucine, arginine and aromatics, tend to promote stronger cooperative interactions than amino acids that are cheaper to produce. Furthermore, cells that share common intermediates along branching pathways yielded more synergistic growth, but exhibited many instances of both positive and negative epistasis when these interactions scaled to higher-dimensions. This system enabled the identification of synergistic pairings and optimal expression levels of amino acid exporters of arginine, threonine and aromatics towards drastic improvements of ecosystem productivity. Tradeoffs identified in these mutualistic systems between secretion, relative abundance and absolute community productivity have implication in the evolution of cooperative behaviors. Long-term evolution of these synthetic communities highlight transporter over-expression, amino acid pool redistribution, and perturbations to nitrogen regulation as strategies to circumvent imposed metabolic dependencies. To address this potentially problematic genomic plasticity, a genetically reassigned organism is leveraged to investigate synthetic metabolic dependencies showing improved biocontainment and potential for microbial consortia control. These results improve our basic understanding of microbial syntrophy while also highlighting the utility and limitations of current approaches to modeling and controlling the dynamic complexities of microbial ecosystems. This work sets a foundation for future endeavors in microbial ecology and evolution, and presents a platform to develop better and more robust engineered synthetic communities for industrial biotechnology

Mathematical modeling of intracellular signaling pathways

Dynamic modeling and simulation of signal transduction pathways is an important topic in systems biology and is obtaining growing attention from researchers with experimental or theoretical background. Here we review attempts to analyze and model specific signaling systems. We review the structure of recurrent building blocks of signaling pathways and their integration into more comprehensive models, which enables the understanding of complex cellular processes. The variety of mechanisms found and modeling techniques used are illustrated with models of different signaling pathways. Focusing on the close interplay between experimental investigation of pathways and the mathematical representations of cellular dynamics, we discuss challenges and perspectives that emerge in studies of signaling systems

Visions of DNA Nanotechnology at 40 for the Next 40

This open access book provides a unique and state-of-the-art view on DNA nanotechnology with an eye toward future developments. Intended as a tribute to Nadrian C. Seeman, who founded the field of DNA nanotechnology, the content is an exciting mixture of technical and non-technical material, reviews, tutorials, perspectives, new findings, and open questions. The book aims to inspire current researchers to sit back and think about the big picture, while also enticing new researchers to enter the field. Most of all, the book captures voices from a unique moment in time: 40 years after the publication of the first paper that envisioned DNA nanotechnology. From this vantage point, what are the untold stories, the unspoken concerns, the underlying fundamental issues, the overlooked opportunities, and the unifying grand challenges? What will help us see more clearly, see more creatively, or see farther? What is transpiring right now that could pave the way for the future? To address these questions, leading researchers have contributed 22 chapters, grouped into five sections: perspectives, chemistry and physics, structures, biochemical circuits, and spatial systems. This book will be an important reference point in the field of DNA nanotechnology, both for established researchers looking to take stock of the field and its future, and for newcomers such as graduate students and researchers in other fields who are beginning to appreciate the power and applicability of its methods

Abstracts of Papers, 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond VA

Full abstracts of the 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond V

91st Annual Meeting of the Virginia Academy of Science: Proceedings

Proceedings of the 91st Annual Meeting of the Virginia Academy of Science, held at Virginia Polytechnic Institute and State University, May 22-24, 2013