Systems Biology: An Approach

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

Sensitivity analysis of biochemical systems using bond graphs

The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. In particular, a sensitivity system is derived which re-expresses parameter variation as additional system inputs. The sensitivity system is then linearized with respect to these new inputs to derive a linear system which can be used to give local sensitivity to parameters in terms of linear system properties such as gain and time constant. This linear system can also be used to find so-called sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.

The lost art of mathematical modelling

We provide a critique of mathematical biology in light of rapid developments in modern machine learning. We argue that out of the three modelling activities - (1) formulating models; (2) analysing models; and (3) fitting or comparing models to data - inherent to mathematical biology, researchers currently focus too much on activity (2) at the cost of (1). This trend, we propose, can be reversed by realising that any given biological phenomenon can be modelled in an infinite number of different ways, through the adoption of an pluralistic approach, where we view a system from multiple, different points of view. We explain this pluralistic approach using fish locomotion as a case study and illustrate some of the pitfalls - universalism, creating models of models, etc. - that hinder mathematical biology. We then ask how we might rediscover a lost art: that of creative mathematical modelling.

Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system

BACKGROUND AND OBJECTIVES

The use of machine learning methods for modelling bio-systems is becoming prominent which can further improve bio-medical technologies. Physics-informed neural networks (PINNs) can embed the knowledge of physical laws that govern a system during the model training process. PINNs utilise differential equations in the model which traditionally used numerical methods that are computationally complex.

METHODS

We integrate PINNs with an entangled ladder network for modelling respiratory systems by considering a lungs conduction zone to evaluate the respiratory impedance for different initial conditions. We evaluate the respiratory impedance for the inhalation phase of breathing for a symmetric model of the human lungs using entanglement and continued fractions.

RESULTS

We obtain the impedance of the conduction zone of the lungs pulmonary airways using PINNs for nine different combinations of velocity and pressure of inhalation. We compare the results from PINNs with the finite element method using the mean absolute error and root mean square error. The results show that the impedance obtained with PINNs contrasts with the conventional forced oscillation test used for deducing the respiratory impedance. The results show similarity with the impedance plots for different respiratory diseases.

CONCLUSION

We find a decrease in impedance when the velocity of breathing is lowered gradually by 20%. Hence, the methodology can be used to design smart ventilators to the improve flow of breathing.

Neurodegenerative disorders: From clinicopathology convergence to systems biology divergence

Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders.

The Craft of Generalism clinical skills and attitudes for whole person care

RATIONALE, AIMS AND OBJECTIVES

Generalists manage a broad range of biomedical and biographical knowledge as part of each clinical encounter, often in multiple encounters over time. The sophistication of this broad integrative work is often misunderstood by those schooled in reductionist or constructivist approaches to evidence. There is a need to describe the practical and philosophically robust ways that understanding about the whole person is formed. In this paper we describe first principles of generalist approaches to knowledge formation in clinical practice. We name the Craft of Generalism.

METHODS

The newly described methodology of Transdisciplinary Generalism is examined by skilled generalist clinicians and translated into skills and attitudes useful for everyday generalist person-centred practice and research.

RESULTS

The Craft of Generalism defines the required scope, process, priorities, and knowledge management skills of all generalists seeking to care for the whole person. These principles are Whole Person Scope, Relational Process, Healing Orientation, and Integrative Wisdom. These skills and attitudes are required for whole person care. If any element of these first principles is left out, the resultant knowledge is incomplete and philosophically incoherent.

CONCLUSIONS

Naming the Craft of Generalism defines the generalist gaze and protects generalism from the colonization of a narrowed medical gaze that excludes all but reductionist evidence or constructivist experience. Defining the Craft of Generalism enables clear teaching of the sophisticated skills and attitudes of the generalist clinician. These philosophically robust principles encourage and defend the use of generalist approaches to knowledge in settings across the community - including health policy, education, and research.

The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD)

Abstract:

Drug design and discovery is a process that requires high financial costs and is timeconsuming. For many years, this process focused on empirical pharmacology. However, over the years, the target-based approach allowed a significant discovery in this field, initiating the rational design era. In view, to decrease the time and financial cost, rational drug design is benefited by increasing computer engineering and software development, and computer-aided drug design (CADD) emerges as a promising alternative. Since the 1970s, this approach has been able to identify many important and revolutionary compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified through this approach have shown their importance, being CADD essential in any drug discovery campaign. Thus, this perspective will present the prominent successful cases utilizing this approach and entering into the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics, using high-performance computing with molecular dynamics protocols faster and more effectively. In addition, artificial intelligence and machine learning will be the next process in the rational design of new drugs. Here, we hope that this paper generates new ideas and instigates research groups worldwide to use these methods and stimulate progress in drug design.

Network thermodynamics of biological systems: A bond graph approach

Edmund Crampin (1973-2021) was at the forefront of Systems Biology research and his work will influence the field for years to come. This paper brings together and summarises the seminal work of his group in applying energy-based bond graph methods to biological systems. In particular, this paper: (a) motivates the need to consider energy in modelling biology; (b) introduces bond graphs as a methodology for achieving this; (c) describes extensions to modelling electrochemical transduction; (d) outlines how bond graph models can be constructed in a modular manner and (e) describes stoichiometric approaches to deriving fundamental properties of reaction networks. These concepts are illustrated using a new bond graph model of photosynthesis in chloroplasts.

A complex systems approach to aging biology

Having made substantial progress understanding molecules, cells, genes and pathways, aging biology research is now moving toward integration of these parts, attempting to understand how their joint dynamics may contribute to aging. Such a shift of perspective requires the adoption of a formal complex systems framework, a transition being facilitated by large-scale data collection and new analytical tools. Here, we provide a theoretical framework to orient researchers around key concepts for this transition, notably emergence, interaction networks and resilience. Drawing on evolutionary theory, network theory and principles of homeostasis, we propose that organismal function is accomplished by the integration of regulatory mechanisms at multiple hierarchical scales, and that the disruption of this ensemble causes the phenotypic and functional manifestations of aging. We present key examples at scales ranging from sub-organismal biology to clinical geriatrics, outlining how this approach can potentially enrich our understanding of aging.

Bacterial Metabolomics: Sample Preparation Methods

Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important "omics" tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.

Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases

Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased.

Analysing and simulating energy-based models in biology using BondGraphTools

Like all physical systems, biological systems are constrained by the laws of physics. However, mathematical models of biochemistry frequently neglect the conservation of energy, leading to unrealistic behaviour. Energy-based models that are consistent with conservation of mass, charge and energy have the potential to aid the understanding of complex interactions between biological components, and are becoming easier to develop with recent advances in experimental measurements and databases. In this paper, we motivate the use of bond graphs (a modelling tool from engineering) for energy-based modelling and introduce, BondGraphTools, a Python library for constructing and analysing bond graph models. We use examples from biochemistry to illustrate how BondGraphTools can be used to automate model construction in systems biology while maintaining consistency with the laws of physics.

Towards organism-level systems biology by next-generation genetics and whole-organ cell profiling

The system-level identification and analysis of molecular and cellular networks in mammals can be accelerated by "next-generation" genetics, which is defined as genetics that can achieve desired genetic makeup in a single generation without any animal crossing. We recently established a highly efficient procedure for producing knock-out (KO) mice using the "Triple-CRISPR" method, which targets a single gene by triple gRNAs in the CRISPR/Cas9 system. This procedure achieved an almost perfect KO efficiency (96-100%). We also established a highly efficient procedure, the "ES-mouse" method, for producing knock-in (KI) mice within a single generation. In this method, ES cells were treated with three inhibitors to keep their potency and then injected into 8-cell-stage embryos. These procedures dramatically shortened the time required to produce KO or KI mice from years down to about 3 months. The produced KO and KI mice can also be systematically profiled at a single-cell resolution by the "whole-organ cell profiling," which was realized by tissue-clearing methods, such as CUBIC, and an advanced light-sheet microscopy. The review describes the establishment and application of these technologies above in analyzing the three states (NREM sleep, REM sleep, and awake) of mammalian brains. It also discusses the role of calcium and muscarinic receptors in these states as well as the current challenges and future opportunities in the next-generation mammalian genetics and whole-organ cell profiling for organism-level systems biology.

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

A Mechanistic Site-Of-Action Model: A Tool for Informing Right Target, Right Compound, And Right Dose for Therapeutic Antagonistic Antibody Programs

Quantitative modeling is increasingly utilized in the drug discovery and development process, from the initial stages of target selection, through clinical studies. The modeling can provide guidance on three major questions–is this the right target, what are the right compound properties, and what is the right dose for moving the best possible candidate forward. In this manuscript, we present a site-of-action modeling framework which we apply to monoclonal antibodies against soluble targets. We give a comprehensive overview of how we construct the model and how we parametrize it and include several examples of how to apply this framework for answering the questions postulated above. The utilities and limitations of this approach are discussed.

Hierarchical semantic composition of biosimulation models using bond graphs

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.

Dynamic and thermodynamic models of adaptation

The concept of biological adaptation was closely connected to some mathematical, engineering and physical ideas from the very beginning. Cannon in his "The wisdom of the body" (1932) systematically used the engineering vision of regulation. In 1938, Selye enriched this approach by the notion of adaptation energy. This term causes much debate when one takes it literally, as a physical quantity, i.e. a sort of energy. Selye did not use the language of mathematics systematically, but the formalization of his phenomenological theory in the spirit of thermodynamics was simple and led to verifiable predictions. In 1980s, the dynamics of correlation and variance in systems under adaptation to a load of environmental factors were studied and the universal effect in ensembles of systems under a load of similar factors was discovered: in a crisis, as a rule, even before the onset of obvious symptoms of stress, the correlation increases together with variance (and volatility). During 30 years, this effect has been supported by many observations of groups of humans, mice, trees, grassy plants, and on financial time series. In the last ten years, these results were supplemented by many new experiments, from gene networks in cardiology and oncology to dynamics of depression and clinical psychotherapy. Several systems of models were developed: the thermodynamic-like theory of adaptation of ensembles and several families of models of individual adaptation. Historically, the first group of models was based on Selye's concept of adaptation energy and used fitness estimates. Two other groups of models are based on the idea of hidden attractor bifurcation and on the advection-diffusion model for distribution of population in the space of physiological attributes. We explore this world of models and experiments, starting with classic works, with particular attention to the results of the last ten years and open questions.

EvoDevo: An Ongoing Revolution?

Since its appearance, Evolutionary Developmental Biology (EvoDevo) has been called an emerging research program, a new paradigm, a new interdisciplinary field, or even a revolution. Behind these formulas, there is the awareness that something is changing in biology. EvoDevo is characterized by a variety of accounts and by an expanding theoretical framework. From an epistemological point of view, what is the relationship between EvoDevo and previous biological tradition? Is EvoDevo the carrier of a new message about how to conceive evolution and development? Furthermore, is it necessary to rethink the way we look at both of these processes? EvoDevo represents the attempt to synthesize two logics, that of evolution and that of development, and the way we conceive one affects the other. This synthesis is far from being fulfilled, but an adequate theory of development may represent a further step towards this achievement. In this article, an epistemological analysis of EvoDevo is presented, with particular attention paid to the relations to the Extended Evolutionary Synthesis (EES) and the Standard Evolutionary Synthesis (SET).

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.

Turning your victim into a collaborator: exploitation of insect behavioral control systems by parasitic manipulators

Some parasites manipulate host behavior by exploiting the host's behavioral control networks. This review explores two examples of this approach using parasites from opposite ends of the size spectrum, that is, viruses and parasitic insects. The first example explores the use of the gene (egt) by some baculoviruses to deactivate the hormone 20-hydroxyecdysone. Suppressing this chemical signal prevents the expression of behaviors that could reduce viral transmission. The second example explores how a parasitic wasp uses the host's immune/neural communication system to control host behavior. When a host's manipulated behavior requires complex neural coordination, exploitation of host behavioral control systems is likely to be involved. Simpler host behaviors can be induced by damage to host tissues.

In-silico comparison of two induction regimens (7 + 3 vs 7 + 3 plus additional bone marrow evaluation) in acute myeloid leukemia treatment

BACKGROUND

Clinical integration of systems biology approaches is gaining in importance in the course of digital revolution in modern medicine. We present our results of the analysis of an extended mathematical model describing abnormal human hematopoiesis. The model is able to describe the course of an acute myeloid leukemia including its treatment. In first-line treatment of acute myeloid leukemia, the induction chemotherapy aims for a rapid leukemic cell reduction. We consider combinations of cytarabine and anthracycline-like chemotherapy. Both substances are widely used as standard treatment to achieve first remission. In particular, we compare two scenarios: a single-induction course with 7 days cytarabine and 3 day of anthracycline-like treatment (7 + 3) with a 7 + 3 course and a bone marrow evaluation that leads, in case of insufficient leukemic cell reduction, to the provision of a second chemotherapy course. Three scenarios, based on the leukemias growth kinetics (slow, intermediate, fast), were analyzed. We simulated different intensity combinations for both therapy schemata (7 + 3 and 7 + 3 + evaluation).

RESULTS

Our model shows that within the 7 + 3 regimen a wider range of intensity combinations result in a complete remission (CR), compared to 7 + 3 + evaluation (fast: 64.3% vs 46.4%; intermediate: 63.7% vs 46.7%; slow: 0% vs 0%). Additionally, the number of simulations resulting in a prolonged CR was higher within the standard regimen (fast: 59.8% vs 40.1%; intermediate: 48.6% vs 31.0%; slow: 0% vs 0%). On the contrary, the 7 + 3 + evaluation regimen allows CR and prolonged CR by lower chemotherapy intensities compared to 7 + 3. Leukemic pace has a strong impact on treatment response and especially on specific effective doses. As a result, faster leukemias are characterized by superior treatment outcomes and can be treated effectively with lower treatment intensities.

CONCLUSIONS

We could show that 7 + 3 treatment has considerable more chemotherapy combinations leading to a first CR. However, the 7 + 3 + evaluation regimen leads to CR for lower therapy intensity and presumably less side effects. An additional evaluation can be considered beneficial to control therapy success, especially in low dose settings. The treatment success is dependent on leukemia growth dynamics. The determination of leukemic pace should be a relevant part of a personalized medicine.

An assessment of the scientific status of anthroposophic medicine, applying criteria from the philosophy of science

OBJECTIVES

The objective was to evaluate the scientific status of anthroposophic medicine (AM) according to demarcation criteria proposed in contemporary philosophy of science.

DESIGN

Criteria for what is science were retrieved from eight publications in the philosophy of science, focusing either on science in medicine or on the demarcation between science and pseudoscience or non-science. Criteria were combined, redundancies were excluded, and the final set of criteria was ordered in a logical sequence. The analysis yielded 11 demarcation criteria (community, domain, problems, goals, axiomatic basis, conceptual basis, quality of concepts, methodology, deontic basis, research products, tradition).

RESULTS

Assessing the scientific status of AM according to the 11 criteria, all criteria were fulfilled by AM.

DISCUSSION

AM is grounded on the notion that specific non-atomistic holistic formative forces exist and can be empirically and rationally assessed. From a position claiming that such holistic forces cannot possibly exist or cannot be empirically and rationally assessed, the axiomatic and conceptual basis of AM can be contested. However, such an a priori rejection is problematic in the presence of empirical evidence supporting the validity of holistic concepts, as discussed in the paper. Future research should therefore focus on the tenability of the ontological reductionist position in science and on the further validation of AM non-atomistic holistic concepts, methods and practices.

CONCLUSION

In this analysis, using criteria from philosophy of science, AM fulfilled all 11 criteria for what is science.

Active State Organization of Spontaneous Behavioral Patterns

We report the development and validation of a principled analytical approach to reveal the manner in which diverse mouse home cage behaviors are organized. We define and automate detection of two mutually-exclusive low-dimensional spatiotemporal units of behavior: “Active” and “Inactive” States. Analyses of these features using a large multimodal 16-strain behavioral dataset provide a series of novel insights into how feeding, drinking, and movement behaviors are coordinately expressed in Mus Musculus. Moreover, we find that patterns of Active State expression are exquisitely sensitive to strain, and classical supervised machine learning incorporating these features provides 99% cross-validated accuracy in genotyping animals using behavioral data alone. Altogether, these findings advance understanding of the organization of spontaneous behavior and provide a high-throughput phenotyping strategy with wide applicability to behavioral neuroscience and animal models of disease.

Adaptive and Behavioral Changes in Kynurenine 3-Monooxygenase Knockout Mice: Relevance to Psychotic Disorders

BACKGROUND

Kynurenine 3-monooxygenase converts kynurenine to 3-hydroxykynurenine, and its inhibition shunts the kynurenine pathway-which is implicated as dysfunctional in various psychiatric disorders-toward enhanced synthesis of kynurenic acid, an antagonist of both α7 nicotinic acetylcholine and N-methyl-D-aspartate receptors. Possibly as a result of reduced kynurenine 3-monooxygenase activity, elevated central nervous system levels of kynurenic acid have been found in patients with psychotic disorders, including schizophrenia.

METHODS

In the present study, we investigated adaptive-and possibly regulatory-changes in mice with a targeted deletion of Kmo (Kmo^-/-) and characterized the kynurenine 3-monooxygenase-deficient mice using six behavioral assays relevant for the study of schizophrenia.

RESULTS

Genome-wide differential gene expression analyses in the cerebral cortex and cerebellum of these mice identified a network of schizophrenia- and psychosis-related genes, with more pronounced alterations in cerebellar tissue. Kynurenic acid levels were also increased in these brain regions in Kmo^-/- mice, with significantly higher levels in the cerebellum than in the cerebrum. Kmo^-/- mice exhibited impairments in contextual memory and spent less time than did controls interacting with an unfamiliar mouse in a social interaction paradigm. The mutant animals displayed increased anxiety-like behavior in the elevated plus maze and in a light/dark box. After a D-amphetamine challenge (5 mg/kg, intraperitoneal), Kmo^-/- mice showed potentiated horizontal activity in the open field paradigm.

CONCLUSIONS

Taken together, these results demonstrate that the elimination of Kmo in mice is associated with multiple gene and functional alterations that appear to duplicate aspects of the psychopathology of several neuropsychiatric disorders.

Precision medicine in airway diseases: moving to clinical practice

On February 21, 2017, a European Respiratory Society research seminar held in Barcelona discussed how to best apply precision medicine to chronic airway diseases such as asthma and chronic obstructive pulmonary disease. It is now clear that both are complex and heterogeneous diseases, that often overlap and that both require individualised assessment and treatment. This paper summarises the presentations and discussions that took place during the seminar. Specifically, we discussed the need for a new taxonomy of human diseases, the role of different players in this scenario (exposome, genes, endotypes, phenotypes, biomarkers and treatable traits) and a number of unanswered key questions in the field. We also addressed how to deploy airway precision medicine in clinical practice today, both in primary and specialised care. Finally, we debated the type of research needed to move the field forward.

What does endotyping mean for treatment in chronic obstructive pulmonary disease?

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease, both at the clinical and biological level. However, COPD is still diagnosed and treated according to simple clinical measures (level of airflow limitation, symptoms, and frequency of previous exacerbations). To address this clinical and biological complexity and to move towards precision medicine in COPD, we need to integrate (bioinformatics) and interpret (clinical science) the vast amount of high-throughput information that existing technology provides (systems biology and network medicine) so diagnosis, stratification, and treatment of patients with COPD can occur on the basis of their pathobiological mechanism (ie, endotypes). Therefore, this Series paper discusses a possible new taxonomy of COPD, the role of endotypes and associated biomarkers and phenotypes, the gaps (and opportunities) in existing knowledge of COPD pathobiology, how systems biology and network medicine can improve understanding of the disease and help to identify relevant endotypes and their specific biomarkers, and how endotypes and their biomarkers can improve the precision, effectiveness, and safety of the treatment of patients with COPD.

Emerging role for dysregulated decidualization in the genesis of preeclampsia

In normal human placentation, uterine invasion by trophoblast cells and subsequent spiral artery remodeling depend on cooperation among fetal trophoblasts and maternal decidual, myometrial, immune and vascular cells in the uterine wall. Therefore, aberrant function of anyone or several of these cell-types could theoretically impair placentation leading to the development of preeclampsia. Because trophoblast invasion and spiral artery remodeling occur during the first half of pregnancy, the molecular pathology of fetal placental and maternal decidual tissues following delivery may not be informative about the genesis of impaired placentation, which transpired months earlier. Therefore, in this review, we focus on the emerging prospective evidence supporting the concept that deficient or defective endometrial maturation in the late secretory phase and during early pregnancy, i.e., pre-decidualization and decidualization, respectively, may contribute to the genesis of preeclampsia. The first prospectively-acquired data directly supporting this concept were unexpectedly revealed in transcriptomic analyses of chorionic villous samples (CVS) obtained during the first trimester of women who developed preeclampsia 5 months later. Additional supportive evidence arose from investigations of Natural Killer cells in first trimester decidua from elective terminations of women with high resistance uterine artery indices, a surrogate for deficient trophoblast invasion. Last, circulating insulin growth factor binding protein-1, which is secreted by decidual stromal cells was decreased during early pregnancy in women who developed preeclampsia. We conclude this review by making recommendations for further prospectively-designed studies to corroborate the concept of endometrial antecedents of preeclampsia. These studies could also enable identification of women at increased risk for developing preeclampsia, unveil the molecular mechanisms of deficient or defective (pre)decidualization, and lead to preventative strategies designed to improve (pre)decidualization, thereby reducing risk for preeclampsia development.

Age-dependent diastolic heart failure in an in vivo Drosophila model

While the signals and complexes that coordinate the heartbeat are well established, how the heart maintains its electromechanical rhythm over a lifetime remains an open question with significant implications to human health. Reasoning that this homeostatic challenge confronts all pulsatile organs, we developed a high resolution imaging and analysis toolset for measuring cardiac function in intact, unanesthetized Drosophila melanogaster. We demonstrate that, as in humans, normal aging primarily manifests as defects in relaxation (diastole) while preserving contractile performance. Using this approach, we discovered that a pair of two-pore potassium channel (K2P) subunits, largely dispensable early in life, are necessary for terminating contraction (systole) in aged animals, where their loss culminates in fibrillatory cardiac arrest. As the pumping function of its heart is acutely dispensable for survival, Drosophila represents a uniquely accessible model for understanding the signaling networks maintaining cardiac performance during normal aging.

A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks

In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced parameters and protein concentrations similar to the original RNN system. Results thus demonstrated the reliability of the proposed RNN method for modelling relatively large networks by modularisation for practical settings. Advantages of the method are its ability to represent accurate continuous system dynamics and ease of: parameter estimation through training with data from a practical setting, model analysis (40% faster than ODE), fine tuning parameters when more data are available, sub-model extension when new elements and/or interactions come to light and model expansion with addition of sub-models.

A systems biology-led insight into the role of the proteome in neurodegenerative diseases

INTRODUCTION

Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases.

AREAS COVERED

Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer's disease, Parkinson's disease and other neurodegenerative disorders, as well as of affected human tissues. Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.

Electron tomography of rabbit cardiomyocyte three-dimensional ultrastructure

The field of cardiovascular research has benefitted from rapid developments in imaging technology over the last few decades. Accordingly, an ever growing number of large, multidimensional data sets have begun to appear, often challenging existing pre-conceptions about structure and function of biological systems. For tissue and cell structure imaging, the move from 2D section-based microscopy to true 3D data collection has been a major driver of new insight. In the sub-cellular domain, electron tomography is a powerful technique for exploration of cellular structures in 3D with unparalleled fidelity at nanometer resolution.

Electron tomography is particularly advantageous for studying highly compartmentalised cells such as cardiomyocytes, where elaborate sub-cellular structures play crucial roles in electrophysiology and mechanics. Although the anatomy of specific ultra-structures, such as dyadic couplons, has been extensively explored using 2D electron microscopy of thin sections, we still lack accurate, quantitative knowledge of true individual shape, volume and surface area of sub-cellular domains, as well as their 3D spatial interrelations; let alone of how these are reshaped during the cycle of contraction and relaxation. Here we discuss and illustrate the utility of ET for identification, visualisation, and analysis of 3D cardiomyocyte ultrastructures such as the T-tubular system, sarcoplasmic reticulum, mitochondria and microtubules.

Systems pharmacology - Towards the modeling of network interactions

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.

Could the gut microbiota reconcile the oral bioavailability conundrum of traditional herbs?

ETHNOPHARMACOLOGICAL RELEVANCE

A wealth of information is emerging about the impact of gut microbiota on human health and diseases such as cardiovascular diseases, obesity and diabetes. As we learn more, we find out the gut microbiota has the potential as new territory for drug targeting. Some novel therapeutic approaches could be developed through reshaping the commensal microbial structure using combinations of different agents. The gut microbiota also affects drug metabolism, directly and indirectly, particularly towards the orally administered drugs. Herbal products have become the basis of traditional medicines such as traditional Chinese medicine and also been being considered valuable materials in modern drug discovery. Of note, low oral bioavailability but high bioactivity is a conundrum not yet solved for some herbs. Since most of herbal products are orally administered, the herbs' constituents are inevitably exposed to the intestinal microbiota and the interplays between herbal constituents and gut microbiota are expected. Emerging explorations of herb-microbiota interactions have an opportunity to revolutionize the way we view herbal therapeutics. The present review aims to provide information regarding the health promotion and/or disease prevention by the interplay between traditional herbs with low bioavailability and gut microbiota through gut microbiota via two different types of mechanisms: (1) influencing the composition of gut microbiota by herbs and (2) metabolic reactions of herbal constituents by gut microbiota.

MATERIALS AND METHODS

The major data bases (PubMed and Web of Science) were searched using "gut microbiota", "intestinal microbiota", "gut flora", "intestinal flora", "gut microflora", "intestinal microflora", "herb", "Chinese medicine", "traditional medicine", or "herbal medicine" as keywords to find out studies regarding herb-microbiota interactions. The Chinese Pharmacopoeia (2010 edition, Volume I) was also used to collect the data of commonly used medicinal herbs and their quality control approaches.

RESULTS

Among the 474 monographs of herbs usually used in the Chinese Pharmacopoeia, the quality control approach of 284 monographs is recommended to use high-performance liquid chromatography approach. Notably, the major marker compounds (>60%) for quality control are polyphenols, polysaccharides and saponins, with significant oral bioavailability conundrum. Results from preclinical and clinical studies on herb-microbiota interactions showed that traditional herbs could exert heath promotion and disease prevention roles via influencing the gut microbiota structure. On the other hand, herb constituents such as ginsenoside C-K, hesperidin, baicalin, daidzin and glycyrrhizin could exert their therapeutic effects through gut microbiota-mediated bioconversion.

CONCLUSIONS

Herb-microbiota interaction studies provide novel mechanistic understanding of the traditional herbs that exhibit poor oral bioavailability. "Microbiota availability" could be taken consideration into describing biological measurements in the therapeutic assessment of herbal medicine. Our review should be of value in stimulating discussions among the scientific community on this relevant theme and prompting more efforts to complement herb-microbiota interactions studies.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

Modelling Blood Flow and Metabolism in the Preclinical Neonatal Brain during and Following Hypoxic-Ischaemia

Hypoxia-ischaemia (HI) is a major cause of neonatal brain injury, often leading to long-term damage or death. In order to improve understanding and test new treatments, piglets are used as preclinical models for human neonates. We have extended an earlier computational model of piglet cerebral physiology for application to multimodal experimental data recorded during episodes of induced HI. The data include monitoring with near-infrared spectroscopy (NIRS) and magnetic resonance spectroscopy (MRS), and the model simulates the circulatory and metabolic processes that give rise to the measured signals. Model extensions include simulation of the carotid arterial occlusion used to induce HI, inclusion of cytoplasmic pH, and loss of metabolic function due to cell death. Model behaviour is compared to data from two piglets, one of which recovered following HI while the other did not. Behaviourally-important model parameters are identified via sensitivity analysis, and these are optimised to simulate the experimental data. For the non-recovering piglet, we investigate several state changes that might explain why some MRS and NIRS signals do not return to their baseline values following the HI insult. We discover that the model can explain this failure better when we include, among other factors such as mitochondrial uncoupling and poor cerebral blood flow restoration, the death of around 40% of the brain tissue.