Papers by Alexander Ullrich
Clathrin mediated endocytosis (CME) is an ubiquitous cellular pathway that regulates central aspe... more Clathrin mediated endocytosis (CME) is an ubiquitous cellular pathway that regulates central aspects of cell physiology such as nutrient uptake, modulation of signal transduction, synaptic transmission and membrane turn-over. Endocytic vesicle formation depends on the timed production of specific phosphoinositides and their interactions with various endocytic proteins. Recently, it has been found that phosphatidylinositol-3,4-bisphosphate (PI(3,4)P2) produced by the class II phosphatidylinositol 3-kinase C2alpha plays a key role in the recruitment of the PX-BAR domain protein SNX9, which is proposed to play a role in the constriction of the endocytic vesicle neck [Posor et al, Nature 499, p233 (2013)]. Interestingly, SNX9 and its close paralog SNX18 are not fully specific to PI(3,4)P2 but can also bind other phospholipids, in particular to PI(4,5)P2, an abundant plasma membrane lipid required for the recruitment of many endocytic proteins. In order to understand the dynamical interplay between phospholipids and endocytic proteins, we developed a computational model of the temporal changes in the population of the phosphoinositide-associated endocytic proteins and their spatial distribution at a clathrin-coated pit (CCP). The model resolves single molecules in time and space, and incorporates the complex interplay of proteins and lipids, as well as their movement within the CCP. We find that the comparably small differences in lipid binding affinities of endocytic proteins are amplified by competition among them, allowing for the selective enrichment of SNX9 at late stage CCPs as a result of timed PI(3,4)P2 production.
Spatiotemporal control of endocytosis by phosphatidylinositol-3,4-bisphosphate
Phosphoinositides serve crucial roles in cell physiology, ranging from cell signalling to membran... more Phosphoinositides serve crucial roles in cell physiology, ranging from cell signalling to membrane traffic. Among the seven eukaryotic phosphoinositides the best studied species is phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2), which is concentrated at the plasma membrane where, among other functions, it is required for the nucleation of endocytic clathrin-coated pits. No phosphatidylinositol other than PI(4,5)P2 has been implicated in clathrin-mediated endocytosis, whereas the subsequent endosomal stages of the endocytic pathway are dominated by phosphatidylinositol-3-phosphates(PI(3)P)7. How phosphatidylinositol conversion from PI(4,5)P2-positive endocytic intermediates to PI(3)P-containing endosomes is achieved is unclear. Here we show that formation of phosphatidylinositol-3,4-bisphosphate (PI(3,4)P2) by class II phosphatidylinositol-3-kinase C2α (PI(3)K C2α) spatiotemporally controls clathrin-mediated endocytosis. Depletion of PI(3,4)P2 or PI(3)K C2α impairs the maturation of late-stage clathrin-coated pits before fission. Timed formation of PI(3,4)P2 by PI(3)K C2α is required for selective enrichment of the BAR domain protein SNX9 at late-stage endocytic intermediates. These findings provide a mechanistic framework for the role of PI(3,4)P2 in endocytosis and unravel a novel discrete function of PI(3,4)P2 in a central cell physiological process.
Abstract In the course of evolution, biological organisms have developed certain desirable proper... more Abstract In the course of evolution, biological organisms have developed certain desirable properties such as the robustness against metabolite fluctuations or mutational errors, as well as the ability to switch flexibly between functional modules. Knowledge about the emergence of these properties is beneficial both for understanding the underlying evolutionary mechanisms as well as for developing principles for the construction of artificial systems.
Abstract The formation of complex systems and their properties is an intriguing field of research... more Abstract The formation of complex systems and their properties is an intriguing field of research in biology with many unresolved questions. The knowledge that can be gained from it is not only beneficial for the understanding and optimization of existing systems but also for constructing many kinds of novel artificial systems.
Life emerged, I suggest, not simple, but complex and whole, and has remained complex and whole ev... more Life emerged, I suggest, not simple, but complex and whole, and has remained complex and whole ever since–not because of a mysterious elan vital, but thanks to the simple, profound transformation of dead molecules into an organization by which each molecule's formation is catalyzed by some other molecule in the organization. The secret of life, the wellspring of reproduction, is not to be found in the beauty of Watson-Crick pairing, but in the achievement of collective catalytic closure.
We extend our previous work on the exploration of static metabolic networks to evolving, and ther... more We extend our previous work on the exploration of static metabolic networks to evolving, and therefore dynamic, pathways. We apply our visualization software to data from a simulation of early metabolism. Thereby, we show that our technique allows us to test and argue for or against different scenarios for the evolution of metabolic pathways. This supports a profound and efficient analysis of the structure and properties of the generated metabolic networks and its underlying components, while giving the user a vivid impression of the dynamics of the system. The analysis process is inspired by Ben Shneiderman’s mantra of information visualization. For the overview, user-defined diagrams give insight into topological changes of the graph as well as changes in the attribute set associated with the participating enzymes, substances and reactions. This way, “interesting features” in time as well as in space can be recognized. A linked view implementation enables the navigation into more detailed layers of perspective for in-depth analysis of individual network configurations.
We developed a simulation tool for investigating the evolution
of early metabolism, allowing us t... more We developed a simulation tool for investigating the evolution
of early metabolism, allowing us to speculate on the formation
ofmetabolic pathways fromcatalyzed chemical reactions
and development of characteristic properties. Our model
consists of a protocellular entity with a simple RNA-based
genetic system and an evolving metabolism of ribozymecatalyzed
enzymes that manipulate a rich underlying chemistry.
Ensuring an almost open-ended and fairly realistic simulation
is crucial for understanding the first steps in metabolic
evolution. We show here, how our simulation tool can be
helpful in arguing for or against hypotheses on the evolution
of metabolic pathways. We demonstrate that seemingly mutually
exclusive hypotheses may well be compatible when we
take into account that different processes dominate different
phases in the evolution of a metabolic system. Our results
suggest that forward evolution shapes metabolic network in
the very early steps of evolution. In later and more complex
stages, enzyme recruitment supersedes forward evolution,
keeping a core set of pathways from the early phase.
Background: The metabolic architectures of extant organisms share many key pathways such as the c... more Background: The metabolic architectures of extant organisms share many key pathways such as the citric acid
cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary
mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support
from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied
by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a
universe of chemical reaction upon which the metabolism is built, an explicit representation of the enzymes that
implement the metabolism, of a genetic system that encodes these enzymes, and of a fitness function that can
be selected for.
Results: We describe here a simulation environment that implements all these components in a simplified ways so
that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as
graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters.
Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is
determined by an ad hoc mapping between their secondary structure and the transition state graphs that they
stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the
complete system and first simulation results.
Conclusions: The simulation system presented here allows coherent investigations into the evolutionary mecha-
nisms of the first steps of metabolic evolution using a self-consistent toy universe.
One important aspect of computational systems biology
includes the identification and analysis of... more One important aspect of computational systems biology
includes the identification and analysis of functional response
networks within large biochemical networks. These functional
response networks represent the response of a biological system
under a particular experimental condition which can be used to
pinpoint critical biological processes.
For this purpose, we have developed a novel algorithm to calculate
response networks as scored/weighted sub-graphs spanned by
k-shortest simple (loop free) paths. The k-shortest simple path
algorithm is based on a forward/backward chaining approach
synchronized between pairs of processors. The algorithm scales
linear with the number of processors used. The algorithm
implementation is using a Linux cluster platform, MPI lam
and mpiJava messaging as well as the Java language for the
application.
The algorithm is performed on a hybrid human network consisting
of 45,041 nodes and 438,567 interactions together with
gene expression information obtained from human cell-lines
infected by influenza virus. Its response networks show the early
innate immune response and virus triggered processes within
human epithelial cells. Especially under the imminent threat of
a pandemic caused by novel influenza strains, such as the current
H1N1 strain, these analyses are crucial for a comprehensive
understanding of molecular processes during early phases of
infection. Such a systems level understanding may aid in the
identification of therapeutic markers and in drug development
for diagnosis and finally prevention of a potentially dangerous
disease.
We introduce a novel genotype-phenotype mapping based on
the relation between RNA sequence and it... more We introduce a novel genotype-phenotype mapping based on
the relation between RNA sequence and its secondary structure for the use in evolutionary studies. Various extensive studies concerning RNA folding in the context of neutral theory yielded insights about properties of the structure space and the mapping itself. We intend to get a better understanding of some of these properties and especially of the evolution of RNA-molecules as well as their effect on the evolution of the entire molecular system. We investigate the constitution of the neutral
network and compare our mapping with other artificial approaches using cellular automatons, random boolean networks and others also based on RNA folding. We yield the highest extent, connectivity and evolvability of the underlying neutral network. Further, we successfully apply the mapping in an existing model for the evolution of a ribozyme-catalyzed
metabolism
Functional Evolution of Ribozyme-Catalyzed Metabolisms in a Graph-Based Toy-Universe
Abstract. The origin and evolution of metabolism is an interesting field
of research with many un... more Abstract. The origin and evolution of metabolism is an interesting field
of research with many unsolved questions. Simulation approaches, even though mostly very abstract and specific, have proven to be helpful in explaining properties and behavior observed in real world metabolic reaction networks, such as the occurrence of hub-metabolites. We propose here a more complex and intuitive graph-based model combined with an artificial chemistry. Instead of differential equations, enzymes are represented as graph rewriting rules and reaction rates are derived from energy calculations of the involved metabolite graphs. The generated networks were shown to possess the typical properties and further studied using our metabolic pathway analysis tool implemented for the observation of system properties such as robustness and modularity. The analysis of our simulations also leads to hypotheses about the evolution of catalytic
molecules and its effect on the emergence of the properties mentioned
above.
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Papers by Alexander Ullrich
of early metabolism, allowing us to speculate on the formation
ofmetabolic pathways fromcatalyzed chemical reactions
and development of characteristic properties. Our model
consists of a protocellular entity with a simple RNA-based
genetic system and an evolving metabolism of ribozymecatalyzed
enzymes that manipulate a rich underlying chemistry.
Ensuring an almost open-ended and fairly realistic simulation
is crucial for understanding the first steps in metabolic
evolution. We show here, how our simulation tool can be
helpful in arguing for or against hypotheses on the evolution
of metabolic pathways. We demonstrate that seemingly mutually
exclusive hypotheses may well be compatible when we
take into account that different processes dominate different
phases in the evolution of a metabolic system. Our results
suggest that forward evolution shapes metabolic network in
the very early steps of evolution. In later and more complex
stages, enzyme recruitment supersedes forward evolution,
keeping a core set of pathways from the early phase.
cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary
mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support
from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied
by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a
universe of chemical reaction upon which the metabolism is built, an explicit representation of the enzymes that
implement the metabolism, of a genetic system that encodes these enzymes, and of a fitness function that can
be selected for.
Results: We describe here a simulation environment that implements all these components in a simplified ways so
that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as
graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters.
Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is
determined by an ad hoc mapping between their secondary structure and the transition state graphs that they
stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the
complete system and first simulation results.
Conclusions: The simulation system presented here allows coherent investigations into the evolutionary mecha-
nisms of the first steps of metabolic evolution using a self-consistent toy universe.
includes the identification and analysis of functional response
networks within large biochemical networks. These functional
response networks represent the response of a biological system
under a particular experimental condition which can be used to
pinpoint critical biological processes.
For this purpose, we have developed a novel algorithm to calculate
response networks as scored/weighted sub-graphs spanned by
k-shortest simple (loop free) paths. The k-shortest simple path
algorithm is based on a forward/backward chaining approach
synchronized between pairs of processors. The algorithm scales
linear with the number of processors used. The algorithm
implementation is using a Linux cluster platform, MPI lam
and mpiJava messaging as well as the Java language for the
application.
The algorithm is performed on a hybrid human network consisting
of 45,041 nodes and 438,567 interactions together with
gene expression information obtained from human cell-lines
infected by influenza virus. Its response networks show the early
innate immune response and virus triggered processes within
human epithelial cells. Especially under the imminent threat of
a pandemic caused by novel influenza strains, such as the current
H1N1 strain, these analyses are crucial for a comprehensive
understanding of molecular processes during early phases of
infection. Such a systems level understanding may aid in the
identification of therapeutic markers and in drug development
for diagnosis and finally prevention of a potentially dangerous
disease.
the relation between RNA sequence and its secondary structure for the use in evolutionary studies. Various extensive studies concerning RNA folding in the context of neutral theory yielded insights about properties of the structure space and the mapping itself. We intend to get a better understanding of some of these properties and especially of the evolution of RNA-molecules as well as their effect on the evolution of the entire molecular system. We investigate the constitution of the neutral
network and compare our mapping with other artificial approaches using cellular automatons, random boolean networks and others also based on RNA folding. We yield the highest extent, connectivity and evolvability of the underlying neutral network. Further, we successfully apply the mapping in an existing model for the evolution of a ribozyme-catalyzed
metabolism
of research with many unsolved questions. Simulation approaches, even though mostly very abstract and specific, have proven to be helpful in explaining properties and behavior observed in real world metabolic reaction networks, such as the occurrence of hub-metabolites. We propose here a more complex and intuitive graph-based model combined with an artificial chemistry. Instead of differential equations, enzymes are represented as graph rewriting rules and reaction rates are derived from energy calculations of the involved metabolite graphs. The generated networks were shown to possess the typical properties and further studied using our metabolic pathway analysis tool implemented for the observation of system properties such as robustness and modularity. The analysis of our simulations also leads to hypotheses about the evolution of catalytic
molecules and its effect on the emergence of the properties mentioned
above.