The cell in an era of systems biology

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

The Gene: An appraisal

The gene can be described as the foundational concept of modern biology. As such, it has spilled over into daily discourse, yet it is acknowledged among biologists to be ill-defined. Here, following a short history of the gene, I analyse critically its role in inheritance, evolution, development, and morphogenesis. Wilhelm Johannsen's genotype-conception, formulated in 1910, has been adopted as the foundation stone of genetics, giving the gene a higher degree of prominence than is justified by the evidence. An analysis of the results of the Long-Term Evolution Experiment (LTEE) with E. coli bacteria, grown over 60,000 generations, does not support spontaneous gene mutation as the source of variance for natural selection. From this it follows that the gene is not Mendel's unit of inheritance: that must be Johannsen's transmission-conception at the gamete phenotype level, a form of inheritance that Johannsen did not consider. Alternatively, I contend that biology viewed on the bases of thermodynamics, complex system dynamics, and self-organisation, provides a new framework for the foundations of biology. In this framework, the gene plays a passive role as a vital information store: it is the phenotype that plays the active role in inheritance, evolution, development, and morphogenesis.

Thinking in 3 dimensions: philosophies of the microenvironment in organoids and organs-on-chip

Organoids and organs-on-a-chip are currently the two major families of 3D advanced organotypic in vitro culture systems, aimed at reconstituting miniaturized models of physiological and pathological states of human organs. Both share the tenets of the so-called "three-dimensional thinking", a Systems Physiology approach focused on recapitulating the dynamic interactions between cells and their microenvironment. We first review the arguments underlying the "paradigm shift" toward three-dimensional thinking in the in vitro culture community. Then, through a historically informed account of the technical affordances and the epistemic commitments of these two approaches, we highlight how they embody two distinct experimental cultures. We finally argue that the current systematic effort for their integration requires not only innovative "synergistic" engineering solutions, but also conceptual integration between different perspectives on biological causality.

Effects of different remote ischemia perconditioning methods on cerebral infarct volume and neurological impairment in rats

Remote ischemic perconditioning (RIPerC) is a novel neuroprotective method against cerebral infarction that has shown efficacy in animal studies but has not been consistently neuroprotective in clinical trials. We focused on the temporal regulation of ischemia-reperfusion by RIPerC to establish an optimal method for RIPerC. Rats were assigned to four groups: 10 min ischemia, 5 min reperfusion; 10 min ischemia, 10 min reperfusion; 5 min ischemia, 10 min reperfusion; and no RIPerC. RIPerC interventions were performed during ischemic stroke, which was induced by a 60-min left middle cerebral artery occlusion. Infarct volume, sensorimotor function, neurological deficits, and cellular expressions of brain-derived neurotrophic factor (BDNF), B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X protein (Bax), and caspase 3 were evaluated 48 h after the induction of ischemia. Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick-end labeling (TUNEL) was also performed. RIPerC of 10 min ischemia/10 min reperfusion, and 5 min ischemia/10 min reperfusion decreased infarct volume, improved sensorimotor function, decreased Bax, caspase 3, and TUNEL-positive cells, and increased BDNF and Bcl-2 expressions. Our findings suggest RIPerC with a reperfusion time of approximately 10 min exerts its neuroprotective effects via an anti-apoptotic mechanism. This study provides important preliminary data to establish more effective RIPerC interventions.

OpenCell: Endogenous tagging for the cartography of human cellular organization

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles

Dynamic behavior of biological systems is commonly represented by non-linear models such as ordinary differential equations. A frequently encountered task in such systems is the estimation of model parameters based on measurement of biochemical compounds. Non-linear models require special techniques to estimate the uncertainty of the obtained model parameters and predictions, e.g. by exploiting the concept of the profile likelihood. Model parameters with significant uncertainty associated with their estimates hinder the interpretation of model results. Informing these model parameters by optimal experimental design minimizes the additional amount of data and therefore resources required in experiments. However, existing techniques of experimental design either require prior parameter distributions in Bayesian approaches or do not adequately deal with the non-linearity of the system in frequentist approaches. For identification of optimal experimental designs, we propose a two-dimensional profile likelihood approach, providing a design criterion which meaningfully represents the expected parameter uncertainty after measuring data for a specified experimental condition. The described approach is implemented into the open source toolbox Data2Dynamics in Matlab. The applicability of the method is demonstrated on an established systems biology model. For this demonstration, available data has been censored to simulate a setting in which parameters are not yet well determined. After determining the optimal experimental condition from the censored ones, a realistic evaluation was possible by re-introducing the censored data point corresponding to the optimal experimental condition. This provided a validation that our method is feasible in real-world applications. The approach applies to, but is not limited to, models in systems biology.

The gene: An appraisal

The gene can be described as the foundational concept of modern biology. As such, it has spilled over into daily discourse, yet it is acknowledged among biologists to be ill-defined. Here, following a short history of the gene, I analyse critically its role in inheritance, evolution, development, and morphogenesis. Wilhelm Johannsen's genotype-conception, formulated in 1910, has been adopted as the foundation stone of genetics, giving the gene a higher degree of prominence than is justified by the evidence. An analysis of the results of the Long-Term Evolution Experiment (LTEE) with E. coli bacteria, grown over 60,000 generations, does not support spontaneous gene mutation as the source of variance for natural selection. From this it follows that the gene is not Mendel's unit of inheritance: that must be Johannsen's transmission-conception at the gamete phenotype level, a form of inheritance that Johannsen did not consider. Alternatively, I contend that biology viewed on the bases of thermodynamics, complex system dynamics and self-organisation, provides a new framework for the foundations of biology. In this framework, the gene plays a passive role as a vital information store: it is the phenotype that plays the active role in inheritance, evolution, development, and morphogenesis.

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Histone transcription regulator Slm9 is required for cytoophidium biogenesis

The cytoophidium, a subcellular structure composed of CTP synthase, can be observed during the division of Schizosaccharomyces pombe. Cytoophidium formation changes periodically with the cell cycle of yeast cells. Here, we find that histone chaperone Slm9 is required for the integrity of cytoophidia in fission yeast. When the slm9 gene is knocked out, we observe that morphological characteristics, the abundance of cytoophidia and the division of the yeast cells are significantly affected. Fragmented cytoophidia occur in slm9 mutant cells, a phenomenon rarely observed in wild-type cells. Our study reveals a potential link between a chromosomal regulatory factor and cytoophidium biogenesis.

Insulin's Discovery: New Insights on Its Hundredth Birthday: From Insulin Action and Clearance to Sweet Networks

In 2021, the 100th anniversary of the isolation of insulin and the rescue of a child with type 1 diabetes from death will be marked. In this review, we highlight advances since the ingenious work of the four discoverers, Frederick Grant Banting, John James Rickard Macleod, James Bertram Collip and Charles Herbert Best. Macleoad closed his Nobel Lecture speech by raising the question of the mechanism of insulin action in the body. This challenge attracted many investigators, and the question remained unanswered until the third part of the 20th century. We summarize what has been learned, from the discovery of cell surface receptors, insulin action, and clearance, to network and precision medicine.

Systems Level Understanding of Circadian Integration with Cell Physiology

The mammalian circadian clock regulates a wide variety of physiological and behavioral processes. In turn, its disruption is associated with sleep deficiency, metabolic syndrome, neurological and psychiatric disorders, and cancer. At the turn of the century, the circadian clock was determined to be regulated by a transcriptional negative feedback mechanism composed of a dozen core clock genes. More recently, large-scale genomic studies have expanded the clock into a complex network composed of thousands of gene outputs and inputs. A major task of circadian research is to utilize systems biological approaches to uncover the governing principles underlying cellular oscillatory behavior and advance understanding of biological functions at the genomic level with spatiotemporal resolution. This review focuses on the genes and pathways that provide inputs to the circadian clock. Several emerging examples include AMP-activated protein kinase AMPK, nutrient/energy sensor mTOR, NAD⁺-dependent deacetylase SIRT1, hypoxia-inducible factor HIF1α, oxidative stress-inducible factor NRF2, and the proinflammatory factor NF-κB. Among others that continue to be revealed, these input pathways reflect the extensive interplay between the clock and cell physiology through the regulation of core clock genes and proteins. While the scope of this crosstalk is well-recognized, precise molecular links are scarce, and the underlying regulatory mechanisms are not well understood. Future research must leverage genetic and genomic tools and technologies, network analysis, and computational modeling to characterize additional modifiers and input pathways. This systems-based framework promises to advance understanding of the circadian timekeeping system and may enable the enhancement of circadian functions through related input pathways.

Imaging dynamic cell signaling in vivo with new classes of fluorescent reporters

Dynamical features of cell signaling are the essence of living organisms. To understand animal development, it is fundamental to investigate signaling dynamics in vivo. Robust reporters are required to visualize spatial and temporal dynamics of enzyme activities and protein-protein interactions involved in signaling pathways. In this review, we summarize recent development in the design of new classes of fluorescent reporters for imaging dynamic activities of proteases, kinases, and protein-protein interactions. These reporters operate on new physical and/or chemical principles; achieve large dynamic range, high brightness, and fast kinetics; and reveal spatiotemporal dynamics of signaling that is correlated with developmental events such as embryonic morphogenesis in live animals including Drosophila and zebrafish. Therefore, many of these reporters are great tools for biological discovery and mechanistic understanding of animal development and disease progression.

The Redox architecture of physiological function

The ability of organisms to accommodate variations in metabolic need and environmental conditions is essential for their survival. However, an explanation is lacking as to how the necessary accommodations in response to these challenges are organized and coordinated from (sub)cellular to higher-level physiological functions, especially in mammals. We propose that the chemistry that enables coordination and synchronization of these processes dates to the origins of Life. We offer a conceptual framework based upon the nature of electron exchange (Redox) processes that co-evolved with biological complexification, giving rise to a multi-layered system in which intra/intercellular and inter-organ exchange processes essential to sensing and adaptation stay fully synchronized. Our analysis explains why Redox is both the lingua franca and the mechanism that enable integration by connecting the various elements of regulatory processes. We here define these interactions across levels of organization as the 'Redox Interactome'. This framework provides novel insight into the chemical and biological basis of Redox signalling and may explain the recent convergence of metabolism, bioenergetics, and inflammation as well as the relationship between Redox stress and human disease.

Data analysis to modeling to building theory in NK cell biology and beyond: How can computational modeling contribute?

The use of mathematical and computational tools in investigating Natural Killer (NK) cell biology and in general the immune system has increased steadily in the last few decades. However, unlike the physical sciences, there is a persistent ambivalence, which however is increasingly diminishing, in the biology community toward appreciating the utility of quantitative tools in addressing questions of biological importance. We survey some of the recent developments in the application of quantitative approaches for investigating different problems in NK cell biology and evaluate opportunities and challenges of using quantitative methods in providing biological insights in NK cell biology.

Proteolytic dynamics of human 20S thymoproteasome

An efficient immunosurveillance of CD8⁺ T cells in the periphery depends on positive/negative selection of thymocytes and thus on the dynamics of antigen degradation and epitope production by thymoproteasome and immunoproteasome in the thymus. Although studies in mouse systems have shown how thymoproteasome activity differs from that of immunoproteasome and strongly impacts the T cell repertoire, the proteolytic dynamics and the regulation of human thymoproteasome are unknown. By combining biochemical and computational modeling approaches, we show here that human 20S thymoproteasome and immunoproteasome differ not only in the proteolytic activity of the catalytic sites but also in the peptide transport. These differences impinge upon the quantity of peptide products rather than where the substrates are cleaved. The comparison of the two human 20S proteasome isoforms depicts different processing of antigens that are associated to tumors and autoimmune diseases.

Scanning number and brightness yields absolute protein concentrations in live cells: a crucial parameter controlling functional bio-molecular interaction networks

Biological function results from properly timed bio-molecular interactions that transduce external or internal signals, resulting in any number of cellular fates, including triggering of cell-state transitions (division, differentiation, transformation, apoptosis), metabolic homeostasis and adjustment to changing physical or nutritional environments, amongst many more. These bio-molecular interactions can be modulated by chemical modifications of proteins, nucleic acids, lipids and other small molecules. They can result in bio-molecular transport from one cellular compartment to the other and often trigger specific enzyme activities involved in bio-molecular synthesis, modification or degradation. Clearly, a mechanistic understanding of any given high level biological function requires a quantitative characterization of the principal bio-molecular interactions involved and how these may change dynamically. Such information can be obtained using fluctation analysis, in particular scanning number and brightness, and used to build and test mechanistic models of the functional network to define which characteristics are the most important for its regulation.

LUCS (Light-Up Cell System), a universal high throughput assay for homeostasis evaluation in live cells

Observations of fluorescent cyanine dye behavior under illumination at 500 nm lead to a novel concept in cell biology allowing the development of a new live cell assay called LUCS, for Light-Up Cell System, measuring homeostasis in live cells. Optimization of the LUCS process resulted in a standardized, straightforward and high throughput assay with applications in toxicity assessment. The mechanisms of the LUCS process were investigated. Electron Paramagnetic Resonance experiments showed that the singlet oxygen and hydroxyl radical are involved downstream of the light effect, presumably leading to deleterious oxidative stress that massively opens access of the dye to its intracellular target. Reversible modulation of LUCS by both verapamil and proton availability indicated that plasma membrane proton/cation antiporters, possibly of the MATE drug efflux transport family, are involved. A mechanistic model is presented. Our data show that intracellular oxidation can be controlled by tuning light energy, opening applications in regulatory purposes, anti-oxidant research, chemotherapy efficacy and dynamic phototherapy strategies.

Cellular intelligence: Microphenomenology and the realities of being

Traditions of Eastern thought conceptualised life in a holistic sense, emphasising the processes of maintaining health and conquering sickness as manifestations of an essentially spiritual principle that was of overriding importance in the conduct of living. Western science, which drove the overriding and partial eclipse of Eastern traditions, became founded on a reductionist quest for ultimate realities which, in the modern scientific world, has embraced the notion that every living process can be successfully modelled by a digital computer system. It is argued here that the essential processes of cognition, response and decision-making inherent in living cells transcend conventional modelling, and microscopic studies of organisms like the shell-building amoebae and the rhodophyte alga Antithamnion reveal a level of cellular intelligence that is unrecognized by science and is not amenable to computer analysis.

Systems morphodynamics: understanding the development of tissue hardware

Systems morphodynamics describes a multi-level analysis of mechanical morphogenesis that draws on new microscopy and computational technologies and embraces a systems biology-informed scope. We present a selection of articles that illustrate and explain this rapidly progressing field.This article is part of the themed issue 'Systems morphodynamics: understanding the development of tissue hardware'.

Systematic epistatic mapping of cellular processes

Genetic screens have identified many novel components of various biological processes, such as components required for cell cycle and cell division. While forward genetic screens typically generate unstructured ‘hit’ lists, genetic interaction mapping approaches can identify functional relations in a systematic fashion. Here, we discuss a recent study by our group demonstrating a two-step approach to first screen for regulators of the mitotic cell cycle, and subsequently guide hypothesis generation by using genetic interaction analysis. The screen used a high-content microscopy assay and automated image analysis to capture defects during mitotic progression and cytokinesis. Genetic interaction networks derived from process-specific features generate a snapshot of functional gene relations in those processes, which follow a temporal order during the cell cycle. This complements a recently published approach, which inferred directional genetic interactions reconstructing hierarchical relationships between genes across different phases during mitotic progression. In conclusion, this strategy leverages unbiased, genome-wide, yet highly sensitive and process-focused functional screening in cells.

Potentials of single-cell biology in identification and validation of disease biomarkers

Single-cell biology is considered a new approach to identify and validate disease-specific biomarkers. However, the concern raised by clinicians is how to apply single-cell measurements for clinical practice, translate the message of single-cell systems biology into clinical phenotype or explain alterations of single-cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single-cell gene sequencing in the identification and development of disease-specific biomarkers, the definition and significance of single-cell biology and single-cell systems biology in the understanding of single-cell full picture, the development and establishment of whole-cell models in the validation of targeted biological function and the figure and meaning of single-molecule imaging in single cell to trace intra-single-cell molecule expression, signal, interaction and location. We headline the important role of single-cell biology in the discovery and development of disease-specific biomarkers with a special emphasis on understanding single-cell biological functions, e.g. mechanical phenotypes, single-cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi-dimensional, multi-layer, multi-crossing and stereoscopic single-cell biology definitely benefits the discovery and development of disease-specific biomarkers.

Model Organisms for Studying the Cell Cycle

Regulation of the cell-division cycle is fundamental for the growth, development, and reproduction of all species of life. In the past several decades, a conserved theme of cell cycle regulation has emerged from research in diverse model organisms. A comparison of distinct features of several diverse model organisms commonly used in cell cycle studies highlights their suitability for various experimental approaches, and recaptures their contributions to our current understanding of the eukaryotic cell cycle. A historic perspective presents a recollection of the breakthrough upon unfolding the universal principles of cell cycle control by scientists working with diverse model organisms, thereby appreciating the discovery pathways in this field. A comprehensive understanding is necessary to address current challenging questions about cell cycle control. Advances in genomics, proteomics, quantitative methodologies, and approaches of systems biology are redefining the traditional concept of what constitutes a model organism and have established a new era for development of novel, and refinement of the established model organisms. Researchers working in the field are no longer separated by their favorite model organisms; they have become more integrated into a larger community for gaining greater insights into how a cell divides and cycles. The new technologies provide a broad evolutionary spectrum of the cell-division cycle and allow informative comparisons among different species at a level that has never been possible, exerting unimaginable impact on our comprehensive understanding of cell cycle regulation.

Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks

Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.

Recent strategies and progress in identifying host factors involved in virus replication

Viruses are completely dependent on their host cells for the successful production of progeny viruses. At each stage of the viral life cycle an intricate interplay between virus and host takes place with the virus aiming to usurp the host cell for its purposes and the host cell trying to block the intruder from propagation. In recent years these interactions have been studied on a global level by systems biology approaches, such as RNA interference screens, transcriptomic or proteomic methodologies, and exciting new insights into the pathogen-host relationship have been revealed. In this review, we summarize the available data, give examples for important findings from such studies and point out current limitations and potential future directions.

Systems cell biology

Systems cell biology melds high-throughput experimentation with quantitative analysis and modeling to understand many critical processes that contribute to cellular organization and dynamics. Recently, there have been several advances in technology and in the application of modeling approaches that enable the exploration of the dynamic properties of cells. Merging technology and computation offers an opportunity to objectively address unsolved cellular mechanisms, and has revealed emergent properties and helped to gain a more comprehensive and fundamental understanding of cell biology.

Genes without prominence: a reappraisal of the foundations of biology

The sequencing of the human genome raises two intriguing questions: why has the prediction of the inheritance of common diseases from the presence of abnormal alleles proved so unrewarding in most cases and how can some 25 000 genes generate such a rich complexity evident in the human phenotype? It is proposed that light can be shed on these questions by viewing evolution and organisms as natural processes contingent on the second law of thermodynamics, equivalent to the principle of least action in its original form. Consequently, natural selection acts on variation in any mechanism that consumes energy from the environment rather than on genetic variation. According to this tenet cellular phenotype, represented by a minimum free energy attractor state comprising active gene products, has a causal role in giving rise, by a self-similar process of cell-to-cell interaction, to morphology and functionality in organisms, which, in turn, by a self-similar process entailing Darwin's proportional numbers are influencing their ecosystems. Thus, genes are merely a means of specifying polypeptides: those that serve free energy consumption in a given surroundings contribute to cellular phenotype as determined by the phenotype. In such natural processes, everything depends on everything else, and phenotypes are emergent properties of their systems.

Molecular events in the cell types of the olfactory epithelium during adult neurogenesis

Background

Adult neurogenesis, fundamental for cellular homeostasis in the mammalian olfactory epithelium, requires major shifts in gene expression to produce mature olfactory sensory neurons (OSNs) from multipotent progenitor cells. To understand these dynamic events requires identifying not only the genes involved but also the cell types that express each gene. Only then can the interrelationships of the encoded proteins reveal the sequences of molecular events that control the plasticity of the adult olfactory epithelium.

Results

Of 4,057 differentially abundant mRNAs at 5 days after lesion-induced OSN replacement in adult mice, 2,334 were decreased mRNAs expressed by mature OSNs. Of the 1,723 increased mRNAs, many were expressed by cell types other than OSNs and encoded proteins involved in cell proliferation and transcriptional regulation, consistent with increased basal cell proliferation. Others encoded fatty acid metabolism and lysosomal proteins expressed by infiltrating macrophages that help scavenge debris from the apoptosis of mature OSNs. The mRNAs of immature OSNs behaved dichotomously, increasing if they supported early events in OSN differentiation (axon initiation, vesicular trafficking, cytoskeletal organization and focal adhesions) but decreasing if they supported homeostatic processes that carry over into mature OSNs (energy production, axon maintenance and protein catabolism). The complexity of shifts in gene expression responsible for converting basal cells into neurons was evident in the increased abundance of 203 transcriptional regulators expressed by basal cells and immature OSNs.

Conclusions

Many of the molecular changes evoked during adult neurogenesis can now be ascribed to specific cellular events in the OSN cell lineage, thereby defining new stages in the development of these neurons. Most notably, the patterns of gene expression in immature OSNs changed in a characteristic fashion as these neurons differentiated. Initial patterns were consistent with the transition into a neuronal morphology (neuritogenesis) and later patterns with neuronal homeostasis. Overall, gene expression patterns during adult olfactory neurogenesis showed substantial similarity to those of embryonic brain.

Maximum likelihood inference of the evolutionary history of a PPI network from the duplication history of its proteins

Evolutionary history of protein-protein interaction (PPI) networks provides valuable insight into molecular mechanisms of network growth. In this paper, we study how to infer the evolutionary history of a PPI network from its protein duplication relationship. We show that for a plausible evolutionary history of a PPI network, its relative quality, measured by the so-called loss number, is independent of the growth parameters of the network and can be computed efficiently. This finding leads us to propose two fast maximum likelihood algorithms to infer the evolutionary history of a PPI network given the duplication history of its proteins. Simulation studies demonstrated that our approach, which takes advantage of protein duplication information, outperforms NetArch, the first maximum likelihood algorithm for PPI network history reconstruction. Using the proposed method, we studied the topological change of the PPI networks of the yeast, fruitfly, and worm.

Data-driven quantification of the robustness and sensitivity of cell signaling networks

Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems.

Boolean network model predicts knockout mutant phenotypes of fission yeast

Boolean networks (or: networks of switches) are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.

In silico modeling of Itk activation kinetics in thymocytes suggests competing positive and negative IP4 mediated feedbacks increase robustness

The inositol-phosphate messenger inositol(1,3,4,5)tetrakisphosphate (IP₄) is essential for thymocyte positive selection by regulating plasma-membrane association of the protein tyrosine kinase Itk downstream of the T cell receptor (TCR). IP₄ can act as a soluble analog of the phosphoinositide 3-kinase (PI3K) membrane lipid product phosphatidylinositol(3,4,5)trisphosphate (PIP₃). PIP₃ recruits signaling proteins such as Itk to cellular membranes by binding to PH and other domains. In thymocytes, low-dose IP₄ binding to the Itk PH domain surprisingly promoted and high-dose IP₄ inhibited PIP₃ binding of Itk PH domains. However, the mechanisms that underlie the regulation of membrane recruitment of Itk by IP₄ and PIP₃ remain unclear. The distinct Itk PH domain ability to oligomerize is consistent with a cooperative-allosteric mode of IP₄ action. However, other possibilities cannot be ruled out due to difficulties in quantitatively measuring the interactions between Itk, IP₄ and PIP₃, and in generating non-oligomerizing Itk PH domain mutants. This has hindered a full mechanistic understanding of how IP₄ controls Itk function. By combining experimentally measured kinetics of PLCγ1 phosphorylation by Itk with in silico modeling of multiple Itk signaling circuits and a maximum entropy (MaxEnt) based computational approach, we show that those in silico models which are most robust against variations of protein and lipid expression levels and kinetic rates at the single cell level share a cooperative-allosteric mode of Itk regulation by IP₄ involving oligomeric Itk PH domains at the plasma membrane. This identifies MaxEnt as an excellent tool for quantifying robustness for complex TCR signaling circuits and provides testable predictions to further elucidate a controversial mechanism of PIP₃ signaling.

Model selection in systems and synthetic biology

Developing mechanistic models has become an integral aspect of systems biology, as has the need to differentiate between alternative models. Parameterizing mathematical models has been widely perceived as a formidable challenge, which has spurred the development of statistical and optimisation routines for parameter inference. But now focus is increasingly shifting to problems that require us to choose from among a set of different models to determine which one offers the best description of a given biological system. We will here provide an overview of recent developments in the area of model selection. We will focus on approaches that are both practical as well as build on solid statistical principles and outline the conceptual foundations and the scope for application of such methods in systems biology.

Aptamers: versatile probes for flow cytometry

Aptamers are nucleic acid oligomers with distinct conformational shapes that allow them to bind targets with high affinity and specificity. Aptamers are selected from a random oligonucleotide library by their capability to bind a certain molecular target. A variety of targets ranging from small molecules like amino acids to complex targets and whole cells have been used to select aptamers. These characteristics and the ability to create specific aptamers against virtually any cell type in a process termed "systematic evolution by exponential enrichment" make them interesting tools for flow cytometry. In this contribution, we review the application of aptamers as probes for flow cytometry, especially cell-phenotyping and detection of various cancer cell lines and virus-infected cells and pathogens. We also discuss the potential of aptamers combined with nanoparticles such as quantum dots for the generation of new multivalent detector molecules with enhanced affinity and sensitivity. With regard to recent advancements in aptamer selection and the decreasing costs for oligonucleotide synthesis, aptamers may rise as potent competitors for antibodies as molecular probes in flow cytometry.

A systems biology analysis of autophagy in cancer therapy

Autophagy, which degrades redundant or damaged cellular constituents, is intricately relevant to a variety of human diseases, most notably cancer. Autophagy exerts distinct effects on cancer initiation and progression, due to the intrinsic overlapping of autophagic and cancer signalling pathways. However, due to the complexity of cancer as a systemic disease, the fate of cancer cells is not decided by any one signalling pathway. Numerous autophagic inter-connectivity and cross-talk pathways need to be further clarified at a systems level. In this review, we propose a systems biology perspective for the comprehensive analysis of the autophagy-cancer network, focusing on systems biology analysis in autophagy and cancer therapy. Together, these analyses may not only improve our understanding on autophagy-cancer relationships, but also facilitate cancer drug discovery.

The search for organizing principles as a cure against reductionism in systems medicine

Biological complexity has forced scientists to develop highly reductive approaches, with an ever-increasing degree of specialization. As a consequence, research projects have become fragmented, and their results strongly dependent on the experimental context. The general research question, that originally motivated these projects, appears to have been forgotten in many highly specialized research programmes. We here investigate the prospects for use of an old regulative ideal from systems theory to describe the organization of cellular systems 'in general' by identifying key concepts, challenges and strategies to pursue the search for organizing principles. We argue that there is no tension between the complexity of biological systems and the search for organizing principles. On the contrary, it is the complexity of organisms and the current level of techniques and knowledge that urge us to renew the search for organizing principles in order to meet the challenges that are arise from reductive approaches in systems medicine. Reductive approaches, as important and inevitable as they are, should be complemented by an integrative strategy that de-contextualizes through abstractions, and thereby generalizes results.

Decoding the language of phosphorylation site dynamics

Immune defenses depend on the ability of immunoreceptors to recognize foreign antigens and initiate intracellular signaling when a pathogen is detected. Signal initiation requires spatial reorganization of proteins and site-specific receptor phosphorylation, which leads to engagement of feedback loops. This Journal Club discusses recent work using combined experimental and computational approaches to investigate these processes in B cell antigen receptor (BCR) signaling. Specifically, the roles of different kinases in the presence and absence of BCR clustering were evaluated. Results indicated that spleen tyrosine kinase (SYK) can compensate for loss of Src-family kinase activity when receptors are spatially clustered, in part because receptor clustering enables SYK to trigger a positive feedback loop. This study and its implications suggest additional uses for computational models in studies of immunoreceptor signaling and highlight areas where extensions of current methodology are needed to better understand the complexities of biomolecular interactions.

Maximizing the information content of experiments in systems biology

Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.

For most biological signalling and regulatory systems we still lack reliable mechanistic models. And where such models exist, e.g. in the form of differential equations, we typically have only rough estimates for the parameters that characterize the biochemical reactions. In order to improve our knowledge of such systems we require better estimates for these parameters and here we show how judicious choice of experiments, based on a combination of simulations and information theoretical analysis, can help us. Our approach builds on the available, frequently rudimentary information, and identifies which experimental set-up provides most additional information about all the parameters, or individual parameters. We will also consider the related but subtly different problem of which experiments need to be performed in order to decrease the uncertainty about the behaviour of the system under altered conditions. We develop the theoretical framework in the necessary detail before illustrating its use and applying it to the repressilator model, the regulation of Hes1 and signal transduction in the Akt pathway.

ON MODELING OF LIVING ORGANISMS USING HIERARCHICAL COARSE-GRAINING ABSTRACTIONS OF KNOWLEDGE

High throughput technologies such as gene expression microarray, ChIP-chips, siRNA and protein arrays and high throughput mass spectrometry are enabling an ever increasing amount of data becoming available about DNA, RNA, proteins, metabolites as well as biological pathways and networks. The knowledge embedded in this data deluge needs to be recast in forms that lend themselves to analysis with the expectation of developing analytical instruments to gain insight and answer questions about life and living organisms. The powers of abstraction and model building are fundamental to the quest of making sense of the biological complexity embedded in these biological and clinical datasets. The modeling of living organisms is explored with a proposed framework for model representation of biological complexity. The principal foundational assumption of the proposed modeling philosophy recognizes the symbiotic relationship between information and energy flows, required for the transformation of matter, as a fundamental organizing force underlying the observable nature of living organisms. The use of the concept of regularities to refer to complexity of structure, function and dynamics alike provides a unified approach to the reasoning about the integration of knowledge representations of varying natures and scales of granularities. The application of the proposed modeling approach is illustrated in broad qualitative terms for the human organism.

Next-generation proteomics: towards an integrative view of proteome dynamics

Next-generation sequencing allows the analysis of genomes, including those representing disease states. However, the causes of most disorders are multifactorial, and systems-level approaches, including the analysis of proteomes, are required for a more comprehensive understanding. The proteome is extremely multifaceted owing to splicing and protein modifications, and this is further amplified by the interconnectivity of proteins into complexes and signalling networks that are highly divergent in time and space. Proteome analysis heavily relies on mass spectrometry (MS). MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis. Here we describe this emerging next generation of proteomics and highlight recent applications.

Systems biology and cardiac arrhythmias

During the past few years, the development of effective, empirical technologies for treatment of cardiac arrhythmias has exceeded the pace at which detailed knowledge of the underlying biology has accumulated. As a result, although some clinical arrhythmias can be cured with techniques such as catheter ablation, drug treatment and prediction of the risk of sudden death remain fairly primitive. The identification of key candidate genes for monogenic arrhythmia syndromes shows that to bring basic biology to the clinic is a powerful approach. Increasingly sophisticated experimental models and methods of measurement, including stem cell-based models of human cardiac arrhythmias, are being deployed to study how perturbations in several biologic pathways can result in an arrhythmia-prone heart. The biology of arrhythmia is largely quantifiable, which allows for systematic analysis that could transform treatment strategies that are often still empirical into management based on molecular evidence.

On the search for design principles in biological systems

The search for basic concepts and underlying principles was at the core of the systems approach to science and technology. This approach was somehow abandoned in mainstream biology after its initial proposal, due to the rise and success of molecular biology. This situation has changed. The accumulated knowledge of decades of molecular studies in combination with new technological advances, while further highlighting the intricacies of natural systems, is also bringing back the quest-for-principles research program. Here, I present two lessons that I derived from my own quest: the importance of studying biological information processing to identify common principles in seemingly unrelated contexts and the adequacy of using known design principles at one level of biological organization as a valuable tool to help recognizing principles at an alternative one. These and additional lessons should contribute to the ultimate goal of establishing principles able to integrate the many scales of biological complexity.

Cancer cell growth and survival as a system-level property sustained by enhanced glycolysis and mitochondrial metabolic remodeling

Systems Biology holds that complex cellular functions are generated as system-level properties endowed with robustness, each involving large networks of molecular determinants, generally identified by “omics” analyses. In this paper we describe four basic cancer cell properties that can easily be investigated in vitro: enhanced proliferation, evasion from apoptosis, genomic instability, and inability to undergo oncogene-induced senescence. Focusing our analysis on a K-ras dependent transformation system, we show that enhanced proliferation and evasion from apoptosis are closely linked, and present findings that indicate how a large metabolic remodeling sustains the enhanced growth ability. Network analysis of transcriptional profiling gives the first indication on this remodeling, further supported by biochemical investigations and metabolic flux analysis (MFA). Enhanced glycolysis, down-regulation of TCA cycle, decoupling of glucose and glutamine utilization, with increased reductive carboxylation of glutamine, so to yield a sustained production of growth building blocks and glutathione, are the hallmarks of enhanced proliferation. Low glucose availability specifically induces cell death in K-ras transformed cells, while PKA activation reverts this effect, possibly through at least two mitochondrial targets. The central role of mitochondria in determining the two investigated cancer cell properties is finally discussed. Taken together the findings reported herein indicate that a system-level property is sustained by a cascade of interconnected biochemical pathways that behave differently in normal and in transformed cells.

Short-term information processing, long-term responses: Insights by mathematical modeling of signal transduction. Early activation dynamics of key signaling mediators can be predictive for cell fate decisions

How do cells interpret information from their environment and translate it into specific cell fate decisions? We propose that cell fate is already encoded in early signaling events and thus can be predicted from defined signal properties. Specifically, we hypothesize that the time integral of activated key signaling molecules can be correlated to cellular behavior such as proliferation or differentiation. The identification of these decisive key signal mediators and their connection to cell fate is facilitated by mathematical modeling. A possible mechanistic linkage between signaling dynamics and cellular function is the directed control of gene regulatory networks by defined signals. Targeted experiments in combination with mathematical modeling can increase our understanding of how cells process information and realize distinct cell fates.

Bayesian design strategies for synthetic biology

We discuss how statistical inference techniques can be applied in the context of designing novel biological systems. Bayesian techniques have found widespread application and acceptance in the systems biology community, where they are used for both parameter estimation and model selection. Here we show that the same approaches can also be used in order to engineer synthetic biological systems by inferring the structure and parameters that are most likely to give rise to the dynamics that we require a system to exhibit. Problems that are shared between applications in systems and synthetic biology include the vast potential spaces that need to be searched for suitable models and model parameters; the complex forms of likelihood functions; and the interplay between noise at the molecular level and nonlinearity in the dynamics owing to often complex feedback structures. In order to meet these challenges, we have to develop suitable inferential tools and here, in particular, we illustrate the use of approximate Bayesian computation and unscented Kalman filtering-based approaches. These partly complementary methods allow us to tackle a number of recurring problems in the design of biological systems. After a brief exposition of these two methodologies, we focus on their application to oscillatory systems.

Cell growth and cell cycle in Saccharomyces cerevisiae: basic regulatory design and protein-protein interaction network

In this review we summarize the major connections between cell growth and cell cycle in the model eukaryote Saccharomyces cerevisiae. In S. cerevisiae regulation of cell cycle progression is achieved predominantly during a narrow interval in the late G1 phase known as START (Pringle and Hartwell, 1981). At START a yeast cell integrates environmental and internal signals (such as nutrient availability, presence of pheromone, attainment of a critical size, status of the metabolic machinery) and decides whether to enter a new cell cycle or to undertake an alternative developmental program. Several signaling pathways, that act to connect the nutritional status to cellular actions, are briefly outlined. A Growth & Cycle interaction network has been manually curated. More than one fifth of the edges within the Growth & Cycle network connect Growth and Cycle proteins, indicating a strong interconnection between the processes of cell growth and cell cycle. The backbone of the Growth & Cycle network is composed of middle-degree nodes suggesting that it shares some properties with HOT networks. The development of multi-scale modeling and simulation analysis will help to elucidate relevant central features of growth and cycle as well as to identify their system-level properties. Confident collaborative efforts involving different expertises will allow to construct consensus, integrated models effectively linking the processes of cell growth and cell cycle, ultimately contributing to shed more light also on diseases in which an altered proliferation ability is observed, such as cancer.

Giant sucking sound: can physiology fill the intellectual void left by the reductionists?

Molecular reductionism has so far failed to deliver the broad-based therapeutic insights that were initially hoped for. This form of reductionism is now being replaced by so-called "systems biology." This is a nebulously defined approach and/or discipline, with some versions of it relying excessively on hypothesis-neutral approaches and only minimally informed by key physiological concepts such as homeostasis and regulation. In this context, physiology is uniquely positioned to continue to provide impressive levels of both biological and therapeutic insight by using hypothesis-driven "classical" approaches and concepts to help frame what might be described as the "pieces of the puzzle" that emerge from molecular reductionism. The strength of physiology as a "bridge" between reductionism and epidemiology, along with its unparalleled ability to generate therapeutic insights and opportunities justifies increased attention and emphasis on our discipline into the future. Arguments relevant to this set of assertions are advanced and this paper, which was based on the 2011 Adolph Lecture, represents an effort to fill the intellectual void left by reductionism and improve scientific progress.