The evolution of molecular biology into 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 the context of the chemical space being considered. Neither are they easily automated nor 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 database that matches 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 multiproperty reference libraries constructed from a subset of 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.

SAnDReS 2.0: Development of machine-learning models to explore the scoring function space

Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

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.

Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists

Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society's most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales-molecular, circuit/network, cell/cell-free systems, biological communities, and societal-giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Unexplored power of CRISPR-Cas9 in neuroscience, a multi-OMICs review

The neuroscience and neurobiology of gene editing to enhance learning and memory is of paramount interest to the scientific community. The advancements of CRISPR system have created avenues to treat neurological disorders by means of versatile modalities varying from expression to suppression of genes and proteins. Neurodegenerative disorders have also been attributed to non-canonical DNA secondary structures by affecting neuron activity through controlling gene expression, nucleosome shape, transcription, translation, replication, and recombination. Changing DNA regulatory elements which could contribute to the fate and function of neurons are thoroughly discussed in this review. This study presents the ability of CRISPR system to boost learning power and memory, treat or cure genetically-based neurological disorders, and alleviate psychiatric diseases by altering the activity and the irritability of the neurons at the synaptic cleft through DNA manipulation, and also, epigenetic modifications using Cas9. We explore and examine how each different OMIC techniques can come useful when altering DNA sequences. Such insight into the underlying relationship between OMICs and cellular behaviors leads us to better neurological and psychiatric therapeutics by intelligently designing and utilizing the CRISPR/Cas9 technology.

Cross-sectional network analysis of plasma proteins/metabolites correlated with pathogenesis and therapeutic response in acute promyelocytic leukemia

The treatment of PML/RARA+ acute promyelocytic leukemia (APL) with all-trans-retinoic acid and arsenic trioxide (ATRA/ATO) has been recognized as a model for translational medicine research. Though an altered microenvironment is a general cancer hallmark, how APL blasts shape their plasma composition is poorly understood. Here, we reported a cross-sectional correlation network to interpret multilayered datasets on clinical parameters, proteomes, and metabolomes of paired plasma samples from patients with APL before or after ATRA/ATO induction therapy. Our study revealed the two prominent features of the APL plasma, suggesting a possible involvement of APL blasts in modulating plasma composition. One was characterized by altered secretory protein and metabolite profiles correlating with heightened proliferation and energy consumption in APL blasts, and the other featured APL plasma-enriched proteins or enzymes catalyzing plasma-altered metabolites that were potential trans-regulatory targets of PML/RARA. Furthermore, results indicated heightened interferon-gamma signaling characterizing a tumor-suppressing function of the immune system at the first hematological complete remission stage, which likely resulted from therapy-induced cell death or senescence and ensuing supraphysiological levels of intracellular proteins. Overall, our work sheds new light on the pathophysiology and treatment of APL and provides an information-rich reference data cohort for the exploratory and translational study of leukemia microenvironment.

Arsenic Contamination of Groundwater Is Determined by Complex Interactions between Various Chemical and Biological Processes

At a great many locations worldwide, the safety of drinking water is not assured due to pollution with arsenic. Arsenic toxicity is a matter of both systems chemistry and systems biology: it is determined by complex and intertwined networks of chemical reactions in the inanimate environment, in microbes in that environment, and in the human body. We here review what is known about these networks and their interconnections. We then discuss how consideration of the systems aspects of arsenic levels in groundwater may open up new avenues towards the realization of safer drinking water. Along such avenues, both geochemical and microbiological conditions can optimize groundwater microbial ecology vis-à-vis reduced arsenic toxicity.

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery

BACKGROUND

The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery.

OBJECTIVE

Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity.

METHODS

We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space.

RESULTS

The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces.

CONCLUSION

The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic Biology

Synthetic biology is built on genetic engineering and principles of design engineering, which provides control over the biological functions of interest. This chapter explores the uses, processes, and applications of genetic engineering in synthetic biology. The chapter provides a brief history and course of development of the field of synthetic biology and genetic engineering and their unbreakable association. Next, the chapter delves into materials and methods and the applications of synthetic biology. This includes discussing the generally used components of genetic engineering to design new functions into organisms and even the general steps that are part of any synthetic biology experiment. Lastly, the chapter also explains the use of the materials and methodology discussed in solving a specific problem related to a model mentioned in the paper titled "Development of Integrase-mediated differentiation circuits to improve evolutionary stability in E. coli." It explains how by using genetic engineering a synthetic biology-related problem was solved efficiently.

Epistemology of synthetic biology: a new theoretical framework based on its potential objects and objectives

Synthetic biology is a new research field which attempts to understand, modify, and create new biological entities by adopting a modular and systemic conception of the living organisms. The development of synthetic biology has generated a pluralism of different approaches, bringing together a set of heterogeneous practices and conceptualizations from various disciplines, which can lead to confusion within the synthetic biology community as well as with other biological disciplines. I present in this manuscript an epistemological analysis of synthetic biology in order to better define this new discipline in terms of objects of study and specific objectives. First, I present and analyze the principal research projects developed at the foundation of synthetic biology, in order to establish an overview of the practices in this new emerging discipline. Then, I analyze an important scientometric study on synthetic biology to complete this overview. Afterwards, considering this analysis, I suggest a three-level classification of the object of study for synthetic biology (which are different kinds of living entities that can be built in the laboratory), based on three successive criteria: structural hierarchy, structural origin, functional origin. Finally, I propose three successively linked objectives in which synthetic biology can contribute (where the achievement of one objective led to the development of the other): interdisciplinarity collaboration (between natural, artificial, and theoretical sciences), knowledge of natural living entities (past, present, future, and alternative), pragmatic definition of the concept of "living" (that can be used by biologists in different contexts). Considering this new theoretical framework, based on its potential objects and objectives, I take the position that synthetic biology has not only the potential to develop its own new approach (which includes methods, objects, and objectives), distinct from other subdisciplines in biology, but also the ability to develop new knowledge on living entities.

Whole-cell metabolic control analysis

Since its conception some fifty years ago, metabolic control analysis (MCA) aims to understand how cells control their metabolism by adjusting the activity of their enzymes. Here we extend its scope to a whole-cell context. We consider metabolism in the evolutionary context of growth-rate maximisation by optimisation of protein concentrations. This framework allows for the prediction of flux control coefficients from proteomics data or stoichiometric modelling. Since genes compete for finite biosynthetic resources, we treat all protein concentrations as interdependent. We show that elementary flux modes (EFMs) emerge naturally as the optimal metabolic networks in the whole-cell context and we derive their control properties. In the evolutionary optimum, the number of expressed EFMs is determined by the number of protein-concentration constraints that limit growth rate. We use published glucose-limited chemostat data of S. cerevisiae to illustrate that it uses only two EFMs prior to the onset of fermentation and that it uses four EFMs during fermentation. We discuss published enzyme-titration data to show that S. cerevisiae and E. coli indeed can express proteins at growth-rate maximising concentrations. Accordingly, we extend MCA to elementary flux modes operating at an optimal state. We find that the expression of growth-unassociated proteins changes results from classical metabolic control analysis. Finally, we show how flux control coefficients can be estimated from proteomics and ribosome-profiling data. We analyse published proteomics data of E. coli to provide a whole-cell perspective of the control of metabolic enzymes on growth rate. We hope that this paper stimulates a renewed interest in metabolic control analysis, so that it can serve again the purpose it once had: to identify general principles that emerge from the biochemistry of the cell and are conserved across biological species.

"Molecular Biology"-Pleonasm or Denotation for a Discipline of Its Own? Reflections on the Origins of Molecular Biology and Its Situation Today

The disciplinary identity of molecular biology has frequently been called into question. Although the debates might sometimes have been more about creating or debunking myths, defending intellectual territory and the distribution of resources, there are interesting underlying questions about this area of biology and how it is conceptually organized. By looking at the history of molecular biology, its origins and development, I examine the possible criteria for its status as a scientific discipline. Doing so allows us to answer the title question in such a way that offers a reasonable middle ground, where molecular biology can be properly viewed as a viable interdisciplinary program that can very well be called a discipline in its own right, even if no strict boundaries can be established. In addition to this historical analysis, a couple of systematic issues from a philosophy of science perspective allow for some assessment of the current situation and the future of molecular biology.

Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool

A growing number of studies shows that it is possible to induce a phenotypic transformation of cancer cells from malignant to benign. This process is currently known as "tumor reversion". However, the concept of reversibility hardly fits the current cancer models, according to which gene mutations are considered the primary cause of cancer. Indeed, if gene mutations are causative carcinogenic factors, and if gene mutations are irreversible, how long should cancer be considered as an irreversible process? In fact, there is some evidence that intrinsic plasticity of cancerous cells may be therapeutically exploited to promote a phenotypic reprogramming, both in vitro and in vivo. Not only are studies on tumor reversion highlighting a new, exciting research approach, but they are also pushing science to look for new epistemological tools capable of better modeling cancer.

Systems biology as a framework to understand the physiological and endocrine bases of behavior and its evolution-From concepts to a case study in birds

Organismal behavior, with its tremendous complexity and diversity, is generated by numerous physiological systems acting in coordination. Understanding how these systems evolve to support differences in behavior within and among species is a longstanding goal in biology that has captured the imagination of researchers who work on a multitude of taxa, including humans. Of particular importance are the physiological determinants of behavioral evolution, which are sometimes overlooked because we lack a robust conceptual framework to study mechanisms underlying adaptation and diversification of behavior. Here, we discuss a framework for such an analysis that applies a "systems view" to our understanding of behavioral control. This approach involves linking separate models that consider behavior and physiology as their own networks into a singular vertically integrated behavioral control system. In doing so, hormones commonly stand out as the links, or edges, among nodes within this system. To ground our discussion, we focus on studies of manakins (Pipridae), a family of Neotropical birds. These species have numerous physiological and endocrine specializations that support their elaborate reproductive displays. As a result, manakins provide a useful example to help imagine and visualize the way systems concepts can inform our appreciation of behavioral evolution. In particular, manakins help clarify how connectedness among physiological systems-which is maintained through endocrine signaling-potentiate and/or constrain the evolution of complex behavior to yield behavioral differences across taxa. Ultimately, we hope this review will continue to stimulate thought, discussion, and the emergence of research focused on integrated phenotypes in behavioral ecology and endocrinology.

The Importance of Sharing Data in Systems Biology

Systems biology research spans a range of biological scales and science domains, and often requires a collaborative effort to collect and share data so that integration is possible. However, sharing data effectively is a challenging task that requires effort and alignment between collaborative partners, as well as coordination between organizations, repositories, and journals. As a community of systems biology researchers, we must get better at efficiently sharing data, and ensuring that shared data comes with the recognition and citations it deserves.

Vitamin D and Systems Biology

The biological actions of the vitamin D receptor (VDR) have been investigated intensively for over 100 years and has led to the identification of significant insights into the repertoire of its biological actions. These were initially established to be centered on the regulation of calcium transport in the colon and deposition in bone. Beyond these well-known calcemic roles, other roles have emerged in the regulation of cell differentiation processes and have an impact on metabolism. The purpose of the current review is to consider where applying systems biology (SB) approaches may begin to generate a more precise understanding of where the VDR is, and is not, biologically impactful. Two SB approaches have been developed and begun to reveal insight into VDR biological functions. In a top-down SB approach genome-wide scale data are statistically analyzed, and from which a role for the VDR emerges in terms of being a hub in a biological network. Such approaches have confirmed significant roles, for example, in myeloid differentiation and the control of inflammation and innate immunity. In a bottom-up SB approach, current biological understanding is built into a kinetic model which is then applied to existing biological data to explain the function and identify unknown behavior. To date, this has not been applied to the VDR, but has to the related ERα and identified previously unknown mechanisms of control. One arena where applying top-down and bottom-up SB approaches may be informative is in the setting of prostate cancer health disparities.

Bridging the gap - biomimetic design of bioelectronic interfaces

Applied bioelectronic interfaces have an enormous potential for their application in personalized medicine and brain-machine interfaces. While significant progress has been made in the translational applications, there are still concerns about the safety and compliance of artificial devices interacting with cells and tissues. Applying biomimetic design principles enables developing new devices with improved properties in terms of their signal transduction efficiency and biocompatibility. Learning from the paradigms of biological architecture, we can define four cornerstones of biomimetics, which can guide designing new bioelectronic devices or providing improved solutions to challenging biomedical problems. Recent progress shows how these paradigms were successfully employed, for example, to create neuron-like electronics and assemble electronic materials in situ onto the cell membranes using genetic targeting.

Metagenomics and Other Omics Approaches to Bacterial Communities and Antimicrobial Resistance Assessment in Aquacultures

The shortage of wild fishery resources and the rising demand for human nutrition has driven a great expansion in aquaculture during the last decades in terms of production and economic value. As such, sustainable aquaculture production is one of the main priorities of the European Union's 2030 agenda. However, the intensification of seafood farming has resulted in higher risks of disease outbreaks and in the increased use of antimicrobials to control them. The selective pressure exerted by these drugs provides the ideal conditions for the emergence of antimicrobial resistance hotspots in aquaculture facilities. Omics technology is an umbrella term for modern technologies such as genomics, metagenomics, transcriptomics, proteomics, culturomics, and metabolomics. These techniques have received increasing recognition because of their potential to unravel novel mechanisms in biological science. Metagenomics allows the study of genomes in microbial communities contained within a certain environment. The potential uses of metagenomics in aquaculture environments include the study of microbial diversity, microbial functions, and antibiotic resistance genes. A snapshot of these high throughput technologies applied to microbial diversity and antimicrobial resistance studies in aquacultures will be presented in this review.

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.

Emerging dynamics pathways of response and resistance to PD-1 and CTLA-4 blockade: tackling uncertainty by confronting complexity

Immune checkpoint inhibitors provide considerable therapeutic benefit in a range of solid cancers as well as in a subgroup of hematological malignancies. Response rates are however suboptimal, and despite considerable efforts, predicting response to immune checkpoint inhibitors ahead of their administration in a given patient remains elusive. The study of the dynamics of the immune system and of the tumor under immune checkpoint blockade brought insight into the mechanisms of action of these therapeutic agents. Equally relevant are the mechanisms of adaptive resistance to immune checkpoint inhibitors that have been uncovered through this approach. In this review, we discuss the dynamics of the immune system and of the tumor under immune checkpoint blockade emanating from recent studies on animal models and humans. We will focus on mechanisms of action and of resistance conveying information predictive of therapeutic response.

Current State of "Omics" Biomarkers in Pancreatic Cancer

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.

Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

One of the central elements in systems biology is the interaction between mathematical modeling and measured quantities. Typically, biological phenomena are represented as dynamical systems, and they are further analyzed and comprehended by identifying model parameters using experimental data. However, all model parameters cannot be found by gradient-based optimization methods by fitting the model to the experimental data due to the non-differentiable character of the problem. Here, we present POPI4SB, a Python-based framework for population-based parameter identification of dynamic models in systems biology. The code is built on top of PySCeS that provides an engine to run dynamic simulations. The idea behind the methodology is to provide a set of derivative-free optimization methods that utilize a population of candidate solutions to find a better solution iteratively. Additionally, we propose two surrogate-assisted population-based methods, namely, a combination of a k-nearest-neighbor regressor with the Reversible Differential Evolution and the Evolution of Distribution Algorithm, that speeds up convergence. We present the optimization framework on the example of the well-studied glycolytic pathway in Saccharomyces cerevisiae.

Investigating the Evolution and Development of Biological Systems from the Perspective of Thermo-Kinetics and Systems Theory

Life itself is grander than the sum of its constituent molecules. Any living organism may be regarded as a part of a dissipative process that connects irreversible energy consumption with growth, reproduction, and evolution. Under energy-fuelled, far-from-equilibrium conditions, chemical systems capable of exponential growth can manifest a specific form of stability- dynamic kinetic stability (DKS) - indicating the persistence of self-reproducible entities. This kinetic behavior is associated with thermodynamic conditions far from equilibrium leading to an evolutionary view of the origin of life in which increasing entities have to be associated with the dissipation of free energy. This review aims to reformulate Darwinian theory in physicochemical terms so that it can handle both animate and inanimate systems, thus helping to overcome this theoretical divide. The expanded formulation is based on the principle of dynamic kinetic stability and evidence from the emerging field of systems chemistry. Although the classic Darwinian theory is useful for understanding the origins and evolution of species, it is not meant to primarily build an explicit framework for predicting potential evolution routes. Throughout the last century, the inherently systemic and dynamic nature of the biological systems has been brought to the attention of researchers. During the last decades, "systems" approaches to biology and genome evolution are gaining ever greater significance providing the possibility of a deeper interpretation of the basic concepts of life. Further progress of this approach depends on crossing disciplinary boundaries and complex simulations of biological systems. Evolutionary systems biology (ESB) through the integration of methods from evolutionary biology and systems biology aims to the understanding of the fundamental principles of life as well as the prediction of biological systems evolution.

Systems biology of host-Candida interactions: understanding how we shape each other

Candida albicans is both a member of the human mucosal microbiota and a common agent of invasive fungal disease. Systems biology approaches allow for analysis of the interactions between this fungus and its mammalian host. Framing these studies by considering how C. albicans and its host construct the niche the other occupies provides insight into how these interactions shape the ecosystems, behavior, and evolution of each organism. Here, we discuss recent work on multiscale systems biology approaches for examining C. albicans in relation to the host ecosystem to identify the emergent properties of the interactions and new variables that can be targeted for development of therapeutic strategies.

Effluents detoxification from pulp and paper industry using microbial engineering and advanced oxidation techniques

Due to the high demand of paper and related items, pulp and paper industry is flourishing day by day. With increased production, come the hazards associated with the toxic elements present in the effluents. Various microorganisms are currently employed in the remediation of these toxic effluents. In addition, various techniques like ozonation, electrocoagulation, UV treatment, Fenton's reagent, and photo-Fenton based techniques are used in advanced oxidation processes to reduce these toxins from effluents. This review highlights various above mentioned advanced techniques and innovative processes along with the biological remediation of these toxic effluents with the help of some potential microbial consortia or their combinatory effects. Moreover, the present review will also disclose the ideas on utilizing the tools of metabolic engineering, systems biology, and artificial intelligence towards microbial engineering for relatively better bioremediation processes. In the future, these techniques might be helpful in increasing the capability of microbial consortia towards detoxification of effluents to make them environmentally safe. Finally, this review gives well-synchronized approaches to get more insights into these innovative methodologies and techniques and their use for various industrial applications.

Model-driven design allows growth of Mycoplasma pneumoniae on serum-free media

Mycoplasma pneumoniae is a slow-growing, human pathogen that causes atypical pneumonia. Because it lacks a cell wall, many antibiotics are ineffective. Due to its reduced genome and dearth of many biosynthetic pathways, this fastidious bacterium depends on rich, undefined medium for growth, which makes large-scale cultivation challenging and expensive. To understand factors limiting growth, we developed a genome-scale, constraint-based model of M. pneumoniae called iEG158_mpn to describe the metabolic potential of this bacterium. We have put special emphasis on cell membrane formation to identify key lipid components to maximize bacterial growth. We have used this knowledge to predict essential components validated with in vitro serum-free media able to sustain growth. Our findings also show that glycolysis and lipid metabolism are much less efficient under hypoxia; these findings suggest that factors other than metabolism and membrane formation alone affect the growth of M. pneumoniae. Altogether, our modelling approach allowed us to optimize medium composition, enabled growth in defined media and streamlined operational requirements, thereby providing the basis for stable, reproducible and less expensive production.

Silencing trust: confidence and familiarity in re-engineering knowledge infrastructures

In this paper, we tell the story of efforts currently underway, on diverse fronts, to build digital knowledge repositories ('knowledge-bases') to support research in the life sciences. If successful, knowledge bases will be part of a new knowledge infrastructure-capable of facilitating ever-more comprehensive, computational models of biological systems. Such an infrastructure would, however, represent a sea-change in the technological management and manipulation of complex data, inducing a generational shift in how questions are asked and answered and results published and circulated. Integrating such knowledge bases into the daily workflow of the lab thus destabilizes a number of well-established habits which biologists rely on to ensure the quality of the knowledge they produce, evaluate, communicate and exploit. As the story we tell here shows, such destabilization introduces a situation of unfamiliarity, one that carries with it epistemic risks. It should elicit-to use Niklas Luhmann's terms-the question of trust: a shared recognition that the reliability of research practices is being risked, but that such a risk is worth taking in view of what may be gained. And yet, the problem of trust is being unexpectedly silenced. How that silencing has come about, why it matters, and what might yet be done forms the heart of this paper.

Considerations of Antibody Geometric Constraints on NK Cell Antibody Dependent Cellular Cytotoxicity

It has been well-established that antibody isotype, glycosylation, and epitope all play roles in the process of antibody dependent cellular cytotoxicity (ADCC). For natural killer (NK) cells, these phenotypes are linked to cellular activation through interaction with the IgG receptor FcγRIIIa, a single pass transmembrane receptor that participates in cytoplasmic signaling complexes. Therefore, it has been hypothesized that there may be underlying spatial and geometric principles that guide proper assembly of an activation complex within the NK cell immune synapse. Further, synergy of antibody phenotypic properties as well as allosteric changes upon antigen binding may also play an as-of-yet unknown role in ADCC. Understanding these facets, however, remains hampered by difficulties associated with studying immune synapse dynamics using classical approaches. In this review, I will discuss relevant NK cell biology related to ADCC, including the structural biology of Fc gamma receptors, and how the dynamics of the NK cell immune synapse are being studied using innovative microscopy techniques. I will provide examples from the literature demonstrating the effects of spatial and geometric constraints on the T cell receptor complex and how this relates to intracellular signaling and the molecular nature of lymphocyte activation complexes, including those of NK cells. Finally, I will examine how the integration of high-throughput and "omics" technologies will influence basic NK cell biology research moving forward. Overall, the goal of this review is to lay a basis for understanding the development of drugs and therapeutic antibodies aimed at augmenting appropriate NK cell ADCC activity in patients being treated for a wide range of illnesses.

Multisystem Trajectories Over the Adult Life Course and Relations to Cardiovascular Disease and Death

BACKGROUND

Comprehensive conjoint characterization of long-term trajectories representing several biological systems is lacking.

METHODS

We measured serially indicators representing 14 distinct biological systems in up to 3,453 participants attending four Framingham Study examinations: bone mineral density, body mass index (BMI), C-reactive protein, glomerular filtration rate, forced vital capacity (FVC), 1 second forced expiratory volume/FVC ratio (FEV1/FVC), gait speed, grip strength, glycosylated hemoglobin (HbA1c), heart rate, left ventricular mass, Mini-Mental State Examination (MMSE), pulse pressure, and total/high-density lipoprotein cholesterol ratio (TC/HDL).

RESULTS

We observed that correlations among the 14 sex-specific trajectories were modest (r < .30 for 169 of 182 sex-specific correlations). During follow-up (median 8 years), 232 individuals experienced a cardiovascular disease (CVD) event and 393 participants died. In multivariable regression models, CVD incidence was positively related to trajectories of BMI, HbA1c, TC/HDL, gait time, and pulse pressure (p < .06); mortality risk was related directly to trajectories of gait time, C-reactive protein, heart rate, and pulse pressure but inversely to MMSE and FEV1/FVC (p < .006). A unit increase in the trajectory risk score was associated with a 2.80-fold risk of CVD (95% confidence interval [CI], 2.04-3.84; p < .001) and a 2.71-fold risk of death (95% CI, 2.30-3.20; p < .001). Trajectory risk scores were suggestive of a greater increase in model c-statistic compared with single occasion measures (delta-c compared with age- and sex-adjusted models: .032 vs .026 for CVD; .042 vs .030 for mortality).

CONCLUSIONS

Biological systems age differentially over the life course. Longitudinal data on a parsimonious set of biomarkers reflecting key biological systems may facilitate identification of high-risk individuals.

Vitalism in contemporary chiropractic: a help or a hinderance?

BACKGROUND

Chiropractic emerged in 1895 and was promoted as a viable health care substitute in direct competition with the medical profession. This was an era when there was a belief that one cause and one cure for all disease would be discovered. The chiropractic version was a theory that most diseases were caused by subluxated (slightly displaced) vertebrae interfering with "nerve vibrations" (a supernatural, vital force) and could be cured by adjusting (repositioning) vertebrae, thereby removing the interference with the body's inherent capacity to heal. DD Palmer, the originator of chiropractic, established chiropractic based on vitalistic principles. Anecdotally, the authors have observed that many chiropractors who overtly claim to be "vitalists" cannot define the term. Therefore, we sought the origins of vitalism and to examine its effects on chiropractic today.

DISCUSSION

Vitalism arose out of human curiosity around the biggest questions: Where do we come from? What is life? For some, life was derived from an unknown and unknowable vital force. For others, a vital force was a placeholder, a piece of knowledge not yet grasped but attainable. Developments in science have demonstrated there is no longer a need to invoke vitalistic entities as either explanations or hypotheses for biological phenomena. Nevertheless, vitalism remains within chiropractic. In this examination of vitalism within chiropractic we explore the history of vitalism, vitalism within chiropractic and whether a vitalistic ideology is compatible with the legal and ethical requirements for registered health care professionals such as chiropractors.

CONCLUSION

Vitalism has had many meanings throughout the centuries of recorded history. Though only vaguely defined by chiropractors, vitalism, as a representation of supernatural force and therefore an untestable hypothesis, sits at the heart of the divisions within chiropractic and acts as an impediment to chiropractic legitimacy, cultural authority and integration into mainstream health care.

A systems approach to infectious disease

Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host-pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery - the design, collection and analysis of omics data; representation - the iterative modelling, integration and visualization of complex data sets; and application - the interpretation and hypothesis-based inquiry towards translational outcomes.

The Construction of Biological 'Inter-Identity' as the Outcome of a Complex Process of Protocell Development in Prebiotic Evolution

The concept of identity is used both (i) to distinguish a system as a particular material entity that is conserved as such in a given environment (token-identity: i.e., identity as permanence or endurance over time), and (ii) to relate a system with other members of a set (type-identity: i.e., identity as an equivalence relationship). Biological systems are characterized, in a minimal and universal sense, by a highly complex and dynamic, far-from-equilibrium organization of very diverse molecular components and transformation processes (i.e., 'genetically instructed cellular metabolisms') that maintain themselves in constant interaction with their corresponding environments, including other systems of similar nature. More precisely, all living entities depend on a deeply convoluted organization of molecules and processes (a naturalized von Neumann constructor architecture) that subsumes, in the form of current individuals (autonomous cells), a history of ecological and evolutionary interactions (across cell populations). So one can defend, on those grounds, that living beings have an identity of their own from both approximations: (i) and (ii). These transversal and trans-generational dimensions of biological phenomena, which unfold together with the actual process of biogenesis, must be carefully considered in order to understand the intricacies and metabolic robustness of the first living cells, their underlying uniformity (i.e., their common biochemical core) and the eradication of previous -or alternative- forms of complex natural phenomena. Therefore, a comprehensive approach to the origins of life requires conjugating the actual properties of the developing complex individuals (fusing and dividing protocells, at various stages) with other, population-level features, linked to their collective-evolutionary behavior, under much wider and longer-term parameters. On these lines, we will argue that life, in its most basic sense, here on Earth or anywhere else, demands crossing a high complexity threshold and that the concept of 'inter-identity' can help us realize the different aspects involved in the process. The article concludes by pointing out some of the challenges ahead if we are to integrate the corresponding explanatory frameworks, physiological and evolutionary, in the hope that a more general theory of biology is on its way.

Radiogenomics Based on PET Imaging

Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine

Mesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.

Chromosomal origin of replication coordinates logically distinct types of bacterial genetic regulation

For a long time it has been hypothesized that bacterial gene regulation involves an intricate interplay of the transcriptional regulatory network (TRN) and the spatial organization of genes in the chromosome. Here we explore this hypothesis both on a structural and on a functional level. On the structural level, we study the TRN as a spatially embedded network. On the functional level, we analyze gene expression patterns from a network perspective (“digital control”), as well as from the perspective of the spatial organization of the chromosome (“analog control”). Our structural analysis reveals the outstanding relevance of the symmetry axis defined by the origin (Ori) and terminus (Ter) of replication for the network embedding and, thus, suggests the co-evolution of two regulatory infrastructures, namely the transcriptional regulatory network and the spatial arrangement of genes on the chromosome, to optimize the cross-talk between two fundamental biological processes: genomic expression and replication. This observation is confirmed by the functional analysis based on the differential gene expression patterns of more than 4000 pairs of microarray and RNA-Seq datasets for E. coli from the Colombos Database using complex network and machine learning methods. This large-scale analysis supports the notion that two logically distinct types of genetic control are cooperating to regulate gene expression in a complementary manner. Moreover, we find that the position of the gene relative to the Ori is a feature of very high predictive value for gene expression, indicating that the Ori–Ter symmetry axis coordinates the action of distinct genetic control mechanisms.

Rethinking the pragmatic systems biology and systems-theoretical biology divide: Toward a complexity-inspired epistemology of systems biomedicine

This paper examines some methodological and epistemological issues underlying the ongoing "artificial" divide between pragmatic-systems biology and systems-theoretical biology. The pragmatic systems view of biology has encountered problems and constraints on its explanatory power because pragmatic systems biologists still tend to view systems as mere collections of parts, not as "emergent realities" produced by adaptive interactions between the constituting components. As such, they are incapable of characterizing the higher-level biological phenomena adequately. The attempts of systems-theoretical biologists to explain these "emergent realities" using mathematics also fail to produce satisfactory results. Given the increasing strategic importance of systems biology, both from theoretical and research perspectives, we suggest that additional epistemological and methodological insights into the possibility of further integration between traditional experimental studies and complex modeling are required. This integration will help to improve the currently underdeveloped pragmatic-systems biology and system-theoretical biology. The "epistemology of complexity," I contend, acts as a glue that connects and integrates different and sometimes opposing viewpoints, perspectives, streams, and practices, thus maintaining intellectual and research coherence of systems research of life. It allows scientists to shift the focus from traditional experimental research to integrated, modeling-based holistic practices capable of providing a comprehensive knowledge of organizing principles of living systems. It also opens the possibility of the development of new practical and theoretical foundations of systems biology to build a better understanding of complex organismic functions.

A systems biology pipeline identifies regulatory networks for stem cell engineering

A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, yet disparate, biological processes to uncover molecular mechanisms governing cell fate transitions. This approach builds on our earlier CellNet and recent trajectory-defining algorithms, as illustrated by our analysis of hematopoietic specification along the erythroid lineage, which reveals a role for the EGF receptor family member, ErbB4, as an important mediator of blood development. We experimentally validate this prediction and perturb the pathway to improve erythroid maturation from human pluripotent stem cells. These results exploit an integrative systems perspective to identify new regulatory processes and nodes useful in cell engineering.