BBA - General Subjects (v.1810, #10)

by Marcus Krantz; Stefan Hohmann (913).

Cells as semantic systems by Dennis Görlich; Stefan Artmann; Peter Dittrich (914-923).
We consider cells as biological systems that process information by means of molecular codes. Many studies analyze cellular information processing exclusively in syntactic terms (e.g., by measuring Shannon entropy of sets of macromolecules), and abstract completely from semantic aspects that are related to the meaning of molecular information.This mini-review focusses on semantic aspects of molecular information, particularly on codes that organize the semantic dimension of molecular information. First, a general conceptual framework for describing molecular information is proposed. Second, some examples of molecular codes are presented. Third, a mathematical approach that makes the identification of molecular codes in reaction networks possible, is developed.By combining a systematic conceptual framework for describing molecular information and a mathematical approach to identify molecular codes, it is possible to give a formally consistent and empirically adequate model of the code-based semantics of molecular information in cells.Research on the semantics of molecular information is of great importance particularly to systems biology since molecular codes embedded in systems of interrelated codes govern main traits of cells. Describing cells as semantic systems may thus trigger new experiments and generate new insights into the fundamental processes of cellular information processing. This article is part of a Special Issue entitled Systems Biology of Microorganisms.► Different subsystems of cells implement different molecular codes. ► Molecular codes embedded in networks of interrelated codes govern main traits of cells. ► Molecular codes can be described in a general conceptual framework. ► The framework makes an objective formalization of molecular codes possible. ► The existence of different molecular codes results in a semantic view of the cell.
Keywords: Molecular information processing; Semantics of molecular information; Molecular code; Algebraic code identification;

Information theory based approaches to cellular signaling by Christian Waltermann; Edda Klipp (924-932).
Cells interact with their environment and they have to react adequately to internal and external changes such changes in nutrient composition, physical properties like temperature or osmolarity and other stresses. More specifically, they must be able to evaluate whether the external change is significant or just in the range of noise. Based on multiple external parameters they have to compute an optimal response. Cellular signaling pathways are considered as the major means of information perception and transmission in cells.Here, we review different attempts to quantify information processing on the level of individual cells. We refer to Shannon entropy, mutual information, and informal measures of signaling pathway cross-talk and specificity.Information theory in systems biology has been successfully applied to identification of optimal pathway structures, mutual information and entropy as system response in sensitivity analysis, and quantification of input and output information.While the study of information transmission within the framework of information theory in technical systems is an advanced field with high impact in engineering and telecommunication, its application to biological objects and processes is still restricted to specific fields such as neuroscience, structural and molecular biology. However, in systems biology dealing with a holistic understanding of biochemical systems and cellular signaling only recently a number of examples for the application of information theory have emerged. This article is part of a Special Issue entitled Systems Biology of Microorganisms.► Intracellular signal transmission is modeled as a noisy channel in information theory. ► Mutual information quantifies input-output information transfer. ► Information theory has predictive power in systems biology.
Keywords: Systems biology; Signaling pathway; Signal integration; Shannon entropy; Mutual information; Sensitivity analysis;

Intracellular pH is a tightly controlled signal in yeast by Rick Orij; Stanley Brul; Gertien J. Smits (933-944).
Nearly all processes in living cells are pH dependent, which is why intracellular pH (pHi) is a tightly regulated physiological parameter in all cellular systems. However, in microbes such as yeast, pHi responds to extracellular conditions such as the availability of nutrients. This raises the question of how pHi dynamics affect cellular function.We discuss the control of pHi, and the regulation of processes by pHi, focusing on the model organism Saccharomyces cerevisiae. We aim to dissect the effects of pHi on various aspects of cell physiology, which are often intertwined. Our goal is to provide a broad overview of how pHi is controlled in yeast, and how pHi in turn controls physiology, in the context of both general cellular functioning as well as of cellular decision making upon changes in the cell's environment.Besides a better understanding of the regulation of pHi, evidence for a signaling role of pHi is accumulating. We conclude that pHi responds to nutritional cues and relays this information to alter cellular make-up and physiology. The physicochemical properties of pH allow the signal to be fast, and affect multiple regulatory levels simultaneously.The mechanisms for regulation of processes by pHi are tightly linked to the molecules that are part of all living cells, and the biophysical properties of the signal are universal amongst all living organisms, and similar types of regulation are suggested in mammals. Therefore, dynamic control of cellular decision making by pHi is therefore likely a general trait. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.► pHi affects the properties of almost all molecules in living cells. ► Intracellular pH is highly dynamic and varies in response to environmental change. ► pHi is tightly controlled in separate organelles. ► Cytosolic pH functions as a signal to relay nutritional cues.
Keywords: Cytosolic pH; Second messenger; Homeostasis; Dynamic regulation; Nutrient signalling;

Redox regulation in respiring Saccharomyces cerevisiae by Douglas B. Murray; Ken Haynes; Masaru Tomita (945-958).
In biological systems, redox reactions are central to most cellular processes and the redox potential of the intracellular compartment dictates whether a particular reaction can or cannot occur. Indeed the widespread use of redox reactions in biological systems makes their detailed description outside the scope of one review.Here we will focus on how system-wide redox changes can alter the reaction and transcriptional landscape of Saccharomyces cerevisiae. To understand this we explore the major determinants of cellular redox potential, how these are sensed by the cell and the dynamic responses elicited.Redox regulation is a large and complex system that has the potential to rapidly and globally alter both the reaction and transcription landscapes. Although we have a basic understanding of many of the sub-systems and a partial understanding of the transcriptional control, we are far from understanding how these systems integrate to produce coherent responses. We argue that this non-linear system self-organises, and that the output in many cases is temperature-compensated oscillations that may temporally partition incompatible reactions in vivo.Redox biochemistry impinges on most of cellular processes and has been shown to underpin ageing and many human diseases. Integrating the complexity of redox signalling and regulation is perhaps one of the most challenging areas of biology. This article is part of a Special Issue entitled Systems Biology of Microorganisms.► Intra-organelle redox shuttling rapidly re-routes the reaction network. ► Mitochondrial criticality is a causal effect in metabolic re-routing. ► Thiols rapidly respond to regulate metabolic re-routing. ► Complex redox transcription factor network shows non-linear behaviours. ► Yeast redox systems are non-linear leading to stable oscillatory dynamics.
Keywords: Yeast; Nicotinamide adenine dinucleotides; thiols; Reactive oxygen species; Mitochondria; Transcription factor network;

From sequence to function: Insights from natural variation in budding yeasts by Conrad A. Nieduszynski; Gianni Liti (959-966).
Natural variation offers a powerful approach for assigning function to DNA sequence—a pressing challenge in the age of high throughput sequencing technologies.Here we review comparative genomic approaches that are bridging the sequence–function and genotype–phenotype gaps. Reverse genomic approaches aim to analyse sequence to assign function, whereas forward genomic approaches start from a phenotype and aim to identify the underlying genotype responsible.Comparative genomic approaches, pioneered in budding yeasts, have resulted in dramatic improvements in our understanding of the function of both genes and regulatory sequences. Analogous studies in other systems, including humans, demonstrate the ubiquity of comparative genomic approaches. Recently, forward genomic approaches, exploiting natural variation within yeast populations, have started to offer powerful insights into how genotype influences phenotype and even the ability to predict phenotypes.Comparative genomic experiments are defining the fundamental rules that govern complex traits in natural populations from yeast to humans.This article is part of a Special Issue entitled Systems Biology of Microorganisms.► A major challenge in biology is the assigning of function to DNA sequence. ► We review the power of comparative genomics and natural variation. ► We contrast forward and reverse genomic approaches. ► We present a case study of telomere biology as a complex phenotype.
Keywords: Saccharomyces cerevisiae; Forward genomics; Reverse genomics; Functional analysis; Quantitative trait locus; Comparative genomics;

Connecting genotype to phenotype in the era of high-throughput sequencing by Christopher S. Henry; Ross Overbeek; Fangfang Xia; Aaron A. Best; Elizabeth Glass; Jack Gilbert; Peter Larsen; Rob Edwards; Terry Disz; Folker Meyer; Veronika Vonstein; Matthew DeJongh; Daniela Bartels; Narayan Desai; Mark D'Souza; Scott Devoid; Kevin P. Keegan; Robert Olson; Andreas Wilke; Jared Wilkening; Rick L. Stevens (967-977).
The development of next generation sequencing technology is rapidly changing the face of the genome annotation and analysis field. One of the primary uses for genome sequence data is to improve our understanding and prediction of phenotypes for microbes and microbial communities, but the technologies for predicting phenotypes must keep pace with the new sequences emerging.This review presents an integrated view of the methods and technologies used in the inference of phenotypes for microbes and microbial communities based on genomic and metagenomic data. Given the breadth of this topic, we place special focus on the resources available within the SEED Project. We discuss the two steps involved in connecting genotype to phenotype: sequence annotation, and phenotype inference, and we highlight the challenges in each of these steps when dealing with both single genome and metagenome data.This integrated view of the genotype-to-phenotype problem highlights the importance of a controlled ontology in the annotation of genomic data, as this benefits subsequent phenotype inference and metagenome annotation. We also note the importance of expanding the set of reference genomes to improve the annotation of all sequence data, and we highlight metagenome assembly as a potential new source for complete genomes. Finally, we find that phenotype inference, particularly from metabolic models, generates predictions that can be validated and reconciled to improve annotations.This review presents the first look at the challenges and opportunities associated with the inference of phenotype from genotype during the next generation sequencing revolution. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.► Scalable annotation and curation of genomic data is becoming a necessity. ► A controlled vocabulary for annotation facilitates the phenotype inference process. ► Phenotype inference methods enable the correction of underlying annotations. ► The growth in reference genomes is enhancing annotation and phenotype inference. ► The SEED Project provides integrated tools for predicting phenotype from genotype.
Keywords: SEED; RAST; MG-RAST; Metagenomics; Genome-scale metabolic models; Assembly;

Growth rate management in fast-growing bacteria is currently an active research area. In spite of the huge progress made in our understanding of the molecular mechanisms controlling the growth rate, fundamental questions concerning its intrinsic limitations are still relevant today. In parallel, systems biology claims that mathematical models could shed light on these questions.This review explores some possible reasons for the limitation of the growth rate in fast-growing bacteria, using a systems biology approach based on constraint-based modeling methods.Recent experimental results and a new constraint-based modelling method named Resource Balance Analysis (RBA) reveal the existence of constraints on resource allocation between biological processes in bacterial cells. In this context, the distribution of a finite amount of resources between the metabolic network and the ribosomes limits the growth rate, which implies the existence of a bottleneck between these two processes. Any mechanism for saving resources increases the growth rate.Consequently, the emergence of genetic regulation of metabolic pathways, e.g. catabolite repression, could then arise as a means to minimise the protein cost, i.e. maximising growth performance while minimising the resource allocation. This article is part of a Special Issue entitled Systems Biology of Microorganisms.► Growth rate is limited through the sharing of resources between cellular processes. ► Resource Balance Analysis method predicts resource distribution among processes. ► Resource allocation is achieved through genetic and enzymatic regulations.
Keywords: Growth rate adaptation; Resource allocation; Constraint-based modelling; Systems biology;

Protein phosphorylation in bacterial signal transduction by Ahasanul Kobir; Lei Shi; Ana Boskovic; Christophe Grangeasse; Damjan Franjevic; Ivan Mijakovic (989-994).
Protein phosphorylation has emerged as one of the major post translational modifications in bacteria, involved in regulating a myriad of physiological processes. In a complex and dynamic system such as the bacterial cell, connectivity of its components accounts for a number of emergent properties. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.This review focuses on the implications of bacterial protein phosphorylation in cell signaling and regulation and highlights the connections and cross talk between various signaling pathways: bacterial two-component systems and serine/threonine kinases, but also the interference between phosphorylation and other post-translational modifications (methylation and acetylation).Recent technical developments in high accuracy mass spectrometry have profoundly transformed proteomics, and today exhaustive site-specific phosphoproteomes are available for a number of bacterial species. Nevertheless, prediction of phosphorylation sites remains the main guide for many researchers, so we discuss the characteristics, limits and advantages of available phosphorylation predictors.The advent of quantitative phosphoproteomics has brought the field on the doorstep of systems biology, but a number of challenges remain before the bacterial phosphorylation networks can be efficiently modeled and their physiological role understood. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.► Protein phosphorylation regulates bacterial housekeeping processes and virulence. ► Bacterial phosphorylation networks exhibit connectivity and signal integration. ► Mass spectrometry can detect and quantify >100 phosphorylation sites per bacterium. ► Bacterial phosphorylation sites are not conserved; kinases show relaxed specificity. ► Bacterial phosphorylation predictors exist, but are limited in prediction accuracy.
Keywords: Protein phosphorylation; Protein kinase; Phosphoproteomics; Phosphorylation predictor; Signal transduction;

Pseudomonas putida KT2440 is endowed with a variant of the phosphoenolpyruvate-carbohydrate phosphotransferase system (PTSNtr), which is not related to sugar transport but believed to rule the metabolic balance of carbon vs. nitrogen. The metabolic targets of such a system are largely unknown.Dielectric breakdown of P. putida cells grown in rich medium revealed the presence of forms of the EIIANtr (PtsN) component of PTSNtr, which were strongly associated to other cytoplasmic proteins. To investigate such intracellular partners of EIIANtr, a soluble protein extract of bacteria bearing an E epitope tagged version of PtsN was immunoprecipitated with a monoclonal anti-E antibody and the pulled-down proteins identified by mass spectrometry.The E1 subunit of the pyruvate dehydrogenase (PDH) complex, the product of the aceE gene, was identified as a major interaction partner of EIIANtr. To examine the effect of EIIANtr on PDH, the enzyme activity was measured in extracts of isogenic ptsN + /ptsN P. putida strains and the role of phosphorylation was determined. Expression of PtsN and AceE proteins fused to different fluorescent moieties and confocal laser microscopy indicated a significant co-localization of the two proteins in the bacterial cytoplasm.EIIANtr down-regulates PDH activity. Both genetic and biochemical evidence revealed that the non-phosphorylated form of PtsN is the protein species that inhibits PDH.EIIANtr takes part in the node of C metabolism that checks the flux of carbon from carbohydrates into the Krebs cycle by means of direct protein–protein interactions with AceE. This type of control might connect metabolism to many other cellular functions. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.► The E1 subunit of pyruvate dehydrogenase (PDH) complex is a major partner of EIIANtr. ► Non-phosphorylated EIIANtr inhibits pyruvate dehydrogenase of P. putida in vivo. ► Manipulating PTSNtr may help engineering bacteria for environmental bioremediation.
Keywords: PTS; Metabolic control; Pseudomonas putida; Pyruvate dehydrogenase; Catabolic repression; Biodegradation;