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Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry., Competing Interests: Declaration of interests The authors declare no conflicts of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena., (© 2023. Springer Nature Limited.)

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network., (© 2021 Federation of European Biochemical Societies.)

Whole-cell modeling aims to incorporate all main genes and processes, and their interactions of a cell in one model. Whole-cell modeling has been regarded as the central aim of systems biology but also as a grand challenge, which plays essential roles in current and future systems biology. In this paper, we analyze whole-cell modeling challenges and requirements and classify them into three aspects (or dimensions): heterogeneous biochemical networks, uncertainties in components, and representation of cell structure. We then explore how to use different Petri net classes to address different aspects of whole-cell modeling requirements. Based on these analyses, we present a Petri nets-based framework for whole-cell modeling, which not only addresses many whole-cell modeling requirements, but also offers a graphical, modular, and hierarchical modeling tool. We think this framework can offer a feasible modeling approach for whole-cell model construction., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Petri nets are a common method for modeling and simulation of systems biology application cases. Usually different Petri net concepts (e.g. discrete, hybrid, functional) are demanded depending on the purpose of the application cases. Modeling complex application cases requires a unification of those concepts, e.g. hybrid functional Petri nets (HFPN) and extended hybrid Petri nets (xHPN). Existing tools have certain limitations which motivated the extension of VANESA, an existing open-source editor for biological networks. The extension can be used to model, simulate, and visualize Petri nets based on the xHPN formalism. Moreover, it comprises additional functionality to support and help the user. Complex (kinetic) functions are syntactically analyzed and mathematically rendered. Based on syntax and given physical unit information, modeling errors are revealed. The numerical simulation is seamlessly integrated and executed in the background by the open-source simulation environment OpenModelica utilizing the Modelica library PNlib. Visualization of simulation results for places, transitions, and arcs are useful to investigate and understand the model and its dynamic behavior. The impact of single parameters can be revealed by comparing multiple simulation results. Simulation results, charts, and entire specification of the Petri net model as Latex file can be exported. All these features are shown in the demonstration case. The utilized Petri net formalism xHPN is fully specified and implemented in PNlib. This assures transparency, reliability, and comprehensible simulation results. Thus, the combination of VANESA and OpenModelica shape a unique open-source Petri net environment focusing on systems biology application cases. VANESA is available at: http://agbi.techfak.uni-bielefeld.de/vanesa., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Whether we emphasize the notion of 'sign' or the notion of 'code', either way the main interest of biosemiotics and Code Biology is the same, and we argue that the idea of the lower threshold is what still unifies these two groups. Code Biology concentrates on the notion of code: living organisms are defined as code-users or code-makers, and so it may be called the 'lower coding threshold' in this case. The semiotic threshold on the other hand is a concept without a specific definition. There are many possible ways of understanding this term. In order to maintain the lower threshold as the unifying concept between Code Biology and biosemiotics, it is important to be very clear about where this threshold is located and how it is defined. We focus on establishing the lower semiotic threshold at protein biosynthesis, and we propose basing the semiotic understanding of the lowest life forms on the following criteria: arbitrariness, representation, repetition, historicity and self-replication. We also offer that this definition of the lower threshold need not include the notion of interpretation, in the hope that this newly specified common principle of the lower threshold be accepted as a way forward in the conversation between Code Biology and biosemiotics., (Copyright © 2021. Published by Elsevier B.V.)

Earlier it was noted that the functioning of biological systems is accompanied by a very low level of energy dissipation, and it was assumed that a physical mechanism similar to that which works in superconductivity can operate here. The paper proposes a hypothesis that the phenomenon of life is not based on superconductivity, but on some so far unexplored macroscopic quantum state of organic structures making up the cell. It is assumed that this state is also characterized by the presence of an energy gap in the electronic spectrum, which makes the state stable and provides a low level of energy dissipation. The possibility of using optical spectroscopy methods for identifying the energy gap in biological objects is analyzed. It is assumed that the virus is alive inside the host cell, but not alive outside the host cell. It is proposed to use Raman spectroscopy of the process of bacterial infection with phages to search for the energy gap. This should confirm or refute the main hypothesis, as well as provide an opportunity to answer the question: "Are viruses alive?", (Copyright © 2021 Elsevier B.V. All rights reserved.)

Nanoparticles are often engineered as a scaffolding system to combine targeting, imaging and/or therapeutic moieties into a unitary agent. However, mostly overlooked, the nanomaterial itself interacts with biological systems exclusive of application-specific particle functionalization. This nanoparticle biointerface has been found to elicit specific biological effects, which we term 'ancillary effects'. In this Review, we describe the current state of knowledge of nanobiology gleaned from existing studies of ancillary effects with the objectives to describe the potential of nanoparticles to modulate biological effects independently of any engineered function; evaluate how these effects might be relevant for nanomedicine design and functional considerations, particularly how they might be useful to inform clinical decision-making; identify potential clinical harm that arises from adverse nanoparticle interactions with biology; and, finally, highlight the current lack of knowledge in this area as both a barrier and an incentive to the further development of nanomedicine., (© 2021. Springer Nature Limited.)

The code of codes or metacode is a microcosm where biological layers, as well as their codes, interact together allowing the continuity of information flow in organisms by increasing biological entities' complexity. Through this novel organic code, biological systems scale towards niches with higher informatic freedom building structures that increase the entropy in the universe. Code biology has developed a novel informational framework where biological entities strive themselves through the information flow carried out through organic codes consisting of two molecular or functional landscapes intertwined through arbitrary linkages via an adaptor whose nature is autonomous from molecular determinism. Here we will integrate genomic and epigenomic codes according to the evidence released in ENCODE (phase 3), psychENCODE and GTEx project, outlining the principles of the metacode, to address the continuous nature of biological systems and their inter-layered information flow. This novel complex metacode maps from very constrained sets of elements (i.e., regulation sites modulating gene expression) to new ones with greater freedom of decoding (i.e., a continuous cell phenotypic space). This leads to a new domain in code biology where biological systems are informatic attractors that navigate an energy metaspace through a complexity-noise balance, stalling in emergent niches where organic codes take meaning., (Copyright © 2021 Elsevier B.V. All rights reserved.)

It should now be recognized that codes are central to life and to understanding its more complex forms, including human culture. Recognizing the 'conventional' nature of codes provides solid grounds for rejecting efforts to reduce life to biochemistry and justifies according a place to semantics in life. The question I want to consider is whether this is enough. Focussing on Eigen's paradox of how a complex code could originate, I will argue that along with Barbieri's efforts to account for the origins of life based on the ribosome and then to account for the refined codes through a process of ambiguity reduction, something more is required. Barbieri has not provided an adequate account of emergence, or the basis for providing such an account. I will argue that Stanley Salthe has clarified to some extent the nature of emergence by conceptualizing it as the interpolation of new enabling constraints. Clearly, codes can be seen as enabling constraints. How this actually happens, though, is still not explained. Stuart Kauffman has grappled with this issue and shown that it radically challenges the assumptions of mainstream science going back to Newton. He has attempted to reintroduce real possibilities or potentialities into his ontology, and argued that radically new developments in nature are associated with realizing adjacent possibles. This is still not adequate. What is also involved, I will suggest, utilizing concepts developed by the French natural philosopher Gilbert Simondon, is 'transduction' as part of 'ontogenesis' of individuals in a process of 'individuation', that is, the emergence of 'individuals' from preindividual fields or milieux., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In recent years, epigenetic memory systems have been developed based on DNA methylation and positive feedback systems. Achieving a robust design for these systems is generally a challenging and multifactorial task. We developed and validated a novel mathematical model to describe methylation-based epigenetic memory systems that capture switching dynamics of methylation levels and methyltransferase amounts induced by different inputs. A bifurcation analysis shows that the system operates in the bistable range, but in its current setup is not robust to changes in parameters. An expansion of the model captures heterogeneity of cell populations by accounting for distributed cell division rates. Simulations predict that the system is highly sensitive to variations in temperature, which affects cell division and the efficiency of the zinc finger repressor. A moderate decrease in temperature leads to a highly heterogeneous response to input signals and bistability on a single-cell level. The predictions of our model were confirmed by flow cytometry experiments conducted in this study. Overall, the results of our study give insights into the functional mechanisms of methylation-based memory systems and demonstrate that the switching dynamics can be highly sensitive to experimental conditions., (© 2021 The Authors. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

As shown by Hofmeyr, the processes in the living cell can be divided into three classes of efficient causes that produce each other, so making the cell closed to efficient causation, the hallmark of an organism. They are the enzyme catalysts of covalent metabolic chemistry, the intracellular milieu that drives the supramolecular processes of chaperone-assisted folding and self-assembly of polypeptides and nucleic acids into functional catalysts and transporters, and the membrane transporters that maintain the intracellular milieu, in particular its electrolyte composition. Each class of efficient cause can be modelled as a relational diagram in the form of a mapping in graph-theoretic form, and a minimal model of a self-manufacturing system that is closed to efficient causation can be constructed from these three mappings using the formalism of relational biology. This fabrication-assembly or (F,A)-system serves as an alternative to Robert Rosen's replicative metabolism-repair or (M,R)-system, which has been notoriously problematic to realise in terms of real biochemical processes. A key feature of the model is the explicit incorporation of formal cause, which arrests the infinite regress that plagues all relational models of the cell. The (F,A)-system is extended into a detailed relational model of the self-manufacturing cell that has a clear biochemical realisation. This (F,A) cell model allows the interpretation and visualisation of concepts such as the metabolism and repair components of Rosen's (M,R)-system, John von Neumann's universal constructor, Howard Pattee's symbol-function split via the symbol-folding transformation, Marcello Barbieri's genotype-ribotype-phenotype ontology, and Tibor Gánti's chemoton., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Biological computation supporting biological phenomena functionally practices the underlying quantum computation indexically, rather than symbolically. An advantage of the indexical operation of quantum computation rests upon a significant reduction of the computational complexity compared with the corresponding classical counterpart running exclusively upon the symbol manipulation. The reduction of the complexity is sought in allowing for the participation of multiple processors running concurrently in a parallel manner. The concurrent distribution of multiple processors operating mutually in an inseparable manner lets each processor regard the rest of the distribution as its own environment. The environment thus formed and detected by each processor may differ from the similar ones appropriated to the other individual participants nearby. Both the individual processor and the corresponding environment turn out to be agential. Quantum computation practiced indexically may serve as a precursor agency apt for both forming Jakob von Uexküll's umwelt towards the environment and making use of James J. Gibson's affordance from the environment. The individual environment to each material participant there is already indexically agential in pulling that participant in., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation., (Copyright © 2020. Published by Elsevier Ltd.)

When Alzheimer's disease (AD) disease-modifying therapies will be available, global healthcare systems will be challenged by a large-scale demand for clinical and biological screening. Validation and qualification of globally accessible, minimally-invasive, and time-, cost-saving blood-based biomarkers need to be advanced. Novel pathophysiological mechanisms (and related candidate biomarkers) - including neuroinflammation pathways (TREM2 and YKL-40), axonal degeneration (neurofilament light chain protein), synaptic dysfunction (neurogranin, synaptotagmin, α-synuclein, and SNAP-25) - may be integrated into an expanding pathophysiological and biomarker matrix and, ultimately, integrated into a comprehensive blood-based liquid biopsy, aligned with the evolving ATN + classification system and the precision medicine paradigm. Liquid biopsy-based diagnostic and therapeutic algorithms are increasingly employed in Oncology disease-modifying therapies and medical practice, showing an enormous potential for AD and other brain diseases as well. For AD and other neurodegenerative diseases, newly identified aberrant molecular pathways have been identified as suitable therapeutic targets and are currently investigated by academia/industry-led R&D programs, including the nerve-growth factor pathway in basal forebrain cholinergic neurons, the sigma1 receptor, and the GTPases of the Rho family. Evidence for a clinical long-term effect on cognitive function and brain health span of cholinergic compounds, drug candidates for repositioning programs, and non-pharmacological multidomain interventions (nutrition, cognitive training, and physical activity) is developing as well. Ultimately, novel pharmacological paradigms, such as quantitative systems pharmacology-based integrative/explorative approaches, are gaining momentum to optimize drug discovery and accomplish effective pathway-based strategies for precision medicine. This article is part of the special issue on 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments. Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely moving animals in more natural conditions while applying systems techniques: performing temporally specific perturbations, modeling behavioral strategies, and recording from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. Here we discuss the origins of ethology, ecology, and systems neuroscience in the context of our own work and highlight how combining approaches across these fields has provided fresh insights into our research. We hope this review facilitates some of these interactions and alliances and helps us all do even better science, together., (Copyright © 2021 the authors.)

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales - from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Place a drop of pond water under the microscope, and you will likely find an ocean of extraordinary and diverse single-celled organisms called ciliates . This remarkable group of single-celled organisms wield microtubules, active systems, electrical signaling, and chemical sensors to build intricate geometrical structures and perform complex behaviors that can appear indistinguishable from those of macroscopic animals. Advances in computer vision and machine learning are making it possible to completely digitize and track the dynamics of complex ciliates and mine these data for the hidden structure, patterns, and motifs that are responsible for their behaviors. By deconstructing the diversity of ciliate behaviors in the natural world, themes for organizing and controlling matter at the microscale are beginning to take hold, suggesting new modular approaches for the design of autonomous molecular machines that emulate nature's finest examples.

Approximately one third of all epilepsy patients are resistant to current therapeutic treatments. Some patients with focal forms of epilepsy benefit from invasive surgical approaches that can lead to large surgical resections of human epileptic neocortex. We have developed a systems biology approach to take full advantage of these resections and the brain tissues they generate as a means to understand underlying mechanisms of neocortical epilepsy and to identify novel biomarkers and therapeutic targets. In this review, we will describe our unique approach that has led to the development of a 'NeuroRepository' of electrically-mapped epileptic tissues and associated data. This 'Big Data' approach links quantitative measures of ictal and interictal activities corresponding to a specific intracranial electrode to clinical, imaging, histological, genomic, proteomic, and metabolomic measures. This highly characterized data and tissue bank has given us an extraordinary opportunity to explore the underlying electrical, cellular, and molecular mechanisms of the human epileptic brain. We describe specific examples of how an experimental design that compares multiple cortical regions with different electrical activities has led to discoveries of layer-specific pathways and how these can be 'reverse translated' from animal models back to humans in the form of new biomarkers and therapeutic targets. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Systems biology is one of the integrated ways to study biological systems and is more favourable than the earlier used approaches. It includes metabolic pathway analysis, modelling, and regulatory as well as signal transduction for getting insights into cellular behaviour. Among the various techniques of modelling, simulation, analysis of networks and pathways, flux-based analysis (FBA) has been recognised because of its extensibility as well as simplicity. It is widely accepted because it is not like a mechanistic simulation which depends on accurate kinetic data. The study of fluxes through the network is informative and can give insights even in the absence of kinetic data. FBA is one of the widely used tools to study biochemical networks and needs information of reaction stoichiometry, growth requirements, specific measurement parameters of the biological system, in particular the reconstruction of the metabolic network for the genome-scale, many of which have already been built previously. It defines the boundaries of flux distributions which are possible and achievable with a defined set of genes. This review article gives an insight into FBA, from the extension of flux balancing to mathematical representation followed by a discussion about the formulation of flux-balance analysis problems, defining constraints for the stoichiometry of the pathways and the tools that can be used in FBA such as FASIMA, COBRA toolbox, and OptFlux. It also includes broader areas in terms of applications which can be covered by FBA as well as the queries which can be addressed through FBA.

Antimicrobials have been known for millennia, but innovative antibiotics are currently in short supply. New antimicrobial discoveries are being threatened by planetary scale loss of biodiversity that has important impacts on species and ecosystems. This expert review underscores that microorganisms in nature and their diversity are essential cornerstones to revitalize the antibiotic innovation and discovery pipeline. The recent rise of systems ecology and planetary health offers new and actionable potentials in this regard. Without a systems scale focus and appreciation of systems ecology, the global threats to human and planetary health from inappropriate use of antibiotics and antimicrobial resistance will continue to escalate with serious consequences to all life on the planet. With acutely pressing research and development needs to revitalize antibiotic treatment and novel diagnostic tools for personalized medicine, national health systems ought to work across knowledge silos not only within but also across the ministries, for example, health, agriculture, environment, economy, trade, and social services ministries that collectively impact on systems ecology and by extension on health innovations including the antibiotic discovery pipeline. Such systems vision can also help to revitalize antibiotic discovery pipeline as most antibiotics have natural origins or have designs inspired or based on molecules in the environment and microorganisms that produce antibiotics. Above all, our audience and responsibility include every person who has an interest in his or her own health, in the health of his or her fellow human beings and all life on the planet, and in the health of future generations.

Clostridium acetobutylicum has received renewed interest worldwide as a promising producer of biofuels and bulk chemicals such as n-butanol, 1,3-propanediol, 1,3-butanediol, isopropanol, and butyrate. To develop commercial processes for the production of bulk chemicals via a metabolic engineering approach it is necessary to better characterize both the primary metabolism and metabolic regulation of C. acetobutylicum. Here, we review the history of the development of omics studies of C. acetobutylicum, summarize the recent application of quantitative/integrated omics approaches to the physiological analysis and metabolic engineering of this bacterium, and provide directions for future studies to address current challenges., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Given the rapidly decreasing cost and increasing speed and accessibility of massively parallel technologies, the integration of comprehensive genomic, transcriptomic, and proteomic data into a "multi-omics" diagnostic pipeline is within reach. Even though genomic analysis has the capability to reveal all possible perturbations in our genetic code, analysis typically reaches a diagnosis in just 35% of cases, with a diagnostic gap arising due to limitations in prioritization and interpretation of detected variants. Here we review the utility of complementing genetic data with transcriptomic data and give a perspective for the introduction of proteomics into the diagnostic pipeline. Together these methodologies enable comprehensive capture of the functional consequence of variants, unobtainable by the analysis of each methodology in isolation. This facilitates functional annotation and reprioritization of candidate genes and variants-a promising approach to shed light on the underlying molecular cause of a patient's disease, increasing diagnostic rate, and allowing actionability in clinical practice., (© 2019 SSIEM.)

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca 2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca 2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease., (© 2019 Wiley Periodicals, Inc.)

The goal of this editorial is to summarize the 2019 International Conference on Intelligent Biology and Medicine (ICIBM 2019) conference that took place on June 9-11, 2019 in The Ohio State University, Columbus, OH, and to provide an introductory summary of the seven articles presented in this supplement issue. ICIBM 2019 hosted four keynote speakers, four eminent scholar speakers, five tutorials and workshops, twelve concurrent sessions and a poster session, totaling 23 posters, spanning state-of-the-art developments in bioinformatics, genomics, next-generation sequencing (NGS) analysis, scientific databases, cancer and medical genomics, and computational drug discovery. A total of 105 original manuscripts were submitted to ICIBM 2019, and after careful review, seven were selected for this supplement issue. These articles cover methods and applications for functional annotations of miRNA targeting, clonal evolution of bacterial cells, gene co-expression networks that describe a given phenotype, functional binding site analysis of RNA-binding proteins, normalization of genome architecture mapping data, sample predictions based on multiple NGS data types, and prediction of an individual's genetic admixture given exonic single nucleotide polymorphisms data.

Pulmonary hypertension (PH) is a common finding that can result from many different pathological conditions. Depending on the etiology, treatment may be quite different, but early diagnosis and correct classification of PH is difficult. With an aging population and recently suggested decreased pulmonary arterial pressure threshold defining PH, we are facing even more diagnostic uncertainties. A new approach to patients' phenotyping is needed. Here we present available data and future perspectives on employing an in-depth analysis of the omics cascade to allow an earlier and more reliable diagnosis and classification of PH. Indeed, with the help of super-fast computing, it became possible to simultaneously consider the levels of thousands of potential biomarkers to find patterns specific for clinically suspected disease. The omics cascade is an invaluable source of information. However, while the genome can be perceived as providing possibilities, transcriptome-as carving them this is metabolome that may tell us 'what is really going on' in an individual living organism. Metabolomics research requires blinded search for characteristic patterns of discreet changes in the levels of detectable metabolites. Since as many as 40,000 various substances are produced as a 'side effect of staying alive', metabolite profiling can be compared to fishing up for organized signals in a universe of chaos. Although difficult, such search for metabolic patterns that might lead to replacing the term biomarker by metabolic fingerprinting in the area of pulmonary circulation has already begun.

The 2nd SY-Stem Symposium - a symposium for 'the next generation of stem cell researchers' - was held on the 21-23 March 2019 at the Vienna BioCenter in Austria. After the great success of the initial SY-Stem meeting in 2018, this year's event again focused on the work of young scientists. Here, we summarize the impressive amount of new research covering stem cell-related fields that was discussed at the meeting., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2019. Published by The Company of Biologists Ltd.)

Introduction: Adjuvants form an integral component in most of the inactivated and subunit vaccine formulations. Careful and proper selection of adjuvants helps in promoting appropriate immune responses against target pathogens at both innate and adaptive levels such that protective immunity can be elicited. Areas covered: Herein, we describe the recent progress in our understanding of the mode of action of adjuvants that are licensed for use in human vaccines or in clinical or pre-clinical stages at both innate and adaptive levels. Different pathogens have distinct characteristics, which require the host to mount an appropriate immune response against them. Adjuvants can be selected to elicit a tailor-made immune response to specific pathogens based on their unique properties. Identification of biomarkers of adjuvanticity for several candidate vaccines using omics-based technologies can unravel the mechanism of action of modern and experimental adjuvants. Expert opinion: Adjuvant technology has been revolutionized over the last two decades. In-depth understanding of the role of adjuvants in activating the innate immune system, combined with systems vaccinology approaches, have led to the development of next-generation, novel adjuvants that can be used in vaccines against challenging pathogens and in specific target populations.

Background: Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. Characterised by abnormal fat accumulation in the liver, NAFLD presents high degree of comorbidity with disorders of the metabolic syndrome, including type 2 diabetes, obesity and cardiovascular disease. These comorbidities have strong negative impact on the natural course of NAFLD and vice versa, whereby the presence of NAFLD substantially modifies the course and prognosis of metabolic syndrome-associated diseases., Aim: To use systems biology strategies to interrogate disease mechanisms that are common to NAFLD and metabolic syndrome., Methods: We mapped shared gene/protein-disease interaction networks, we performed gene-disease enrichment analysis to assess pleiotropy, and we created a gene-drug connectivity network., Results: We found that a shared network of genes/proteins is overrepresented by immune response-related pathways, post-translational modifications of nuclear receptors, and platelet-related processes, including activation and platelet signalling. Likewise, gene-based disease-enrichment analysis suggested underlying molecular effectors that are shared with major systemic disorders, including diverse autoimmune diseases, kidney, respiratory and nervous system disorders, cancer and infectious diseases. The shared list of genes/proteins was enriched in drug targets for anti-inflammatory therapy, drugs used to treat cardiovascular diseases, antimicrobial agents and phytochemicals, among many other approved pharmaceutical compounds. By leveraging on publicly available OMICs data, we were able to show that shared loci are not necessarily affected by reverse causality., Conclusion: We provide evidence indicating that NAFLD treatment, including severe histological traits, cannot be limited to the use of a single drug, as it rather requires a multi-target therapeutic approach., (© 2019 John Wiley & Sons Ltd.)

Gene regulatory networks (GRNs) and gene expression data form a core element of systems biology-based phenotyping. Changes in the expression of transcription factors are commonly believed to have a causal effect on the expression of their targets. Here we evaluated in the best researched model organism, Escherichia coli, the consistency between a GRN and a large gene expression compendium. Surprisingly, a modest correlation was observed between the expression of transcription factors and their targets and, most noteworthy, both activating and repressing interactions were associated with positive correlation. When evaluated using a sign consistency model we found the regulatory network was not more consistent with measured expression than random network models. We conclude that, at least in E. coli, one cannot expect a causal relationship between the expression of transcription and factors their targets, and that the current static GRN does not adequately explain transcriptional regulation. The implications of this are profound as they question what we consider established knowledge of the systemic biology of cells and point to methodological limitations with respect to single omics analysis, static networks and temporality.

Given current high market competitiveness, it is necessary to differentiate between products that perform the same function. For this objective, designer can recur to various sources of inspiration in the searching of the more attractive form during the conceptual design stage. One of these sources can be nature, which offers a large number of geometries and textures that can be used from a shape point of view to help the designer in the creative process. This work presents an agent-based approach for a design-aided tool to provide users with some ideas, beginning with simple parts/concepts, and then increasing the complexity level according to the answers offered by designer. The proposed paradigm was implemented using the JADE agent-based platform. In order to validate the platform, several product categories were offered to fifteen different users and a total of sixty design proposals were obtained with the aid of the platform. After evaluating all the proposals, twelve of the sixty designs were finally selected and modelled by a Computer-Aided Design software. The obtained results demonstrate the feasibility of using an agent-based approach to obtain an adaptive intelligent solution to the product conceptual design problem., Competing Interests: The authors have declared that no competing interests exist.

Biological systems are dynamical, constantly exchanging energy and matter with the environment in order to maintain the non-equilibrium state synonymous with living. Developments in observational techniques have allowed us to study biological dynamics on increasingly small scales. Such studies have revealed evidence of quantum mechanical effects, which cannot be accounted for by classical physics, in a range of biological processes. Quantum biology is the study of such processes, and here we provide an outline of the current state of the field, as well as insights into future directions., (© 2018 The Author(s).)

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking)., (Published by Elsevier B.V.)