Dimensions of systems biology

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

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

Electrostatic Surface Potential as a Key Parameter in Virus Transmission and Evolution: How to Manage Future Virus Pandemics in the Post-COVID-19 Era

Virus-cell interactions involve fundamental parameters that need to be considered in strategies implemented to control viral outbreaks. Among these, the surface electrostatic potential can give valuable information to deal with new epidemics. In this article, we describe the role of this key parameter in the hemagglutination of red blood cells and in the co-evolution of synaptic receptors and neurotransmitters. We then establish the functional link between lipid rafts and the electrostatic potential of viruses, with special emphasis on gangliosides, which are sialic-acid-containing, electronegatively charged plasma membrane components. We describe the common features of ganglioside binding domains, which include a wide variety of structures with little sequence homology but that possess key amino acids controlling ganglioside recognition. We analyze the role of the electrostatic potential in the transmission and intra-individual evolution of HIV-1 infections, including gatekeeper and co-receptor switch mechanisms. We show how to organize the epidemic surveillance of influenza viruses by focusing on mutations affecting the hemagglutinin surface potential. We demonstrate that the electrostatic surface potential, by modulating spike-ganglioside interactions, controls the hemagglutination properties of coronaviruses (SARS-CoV-1, MERS-CoV, and SARS-CoV-2) as well as the structural dynamics of SARS-CoV-2 evolution. We relate the broad-spectrum antiviral activity of repositioned molecules to their ability to disrupt virus-raft interactions, challenging the old concept that an antibiotic or anti-parasitic cannot also be an antiviral. We propose a new concept based on the analysis of the electrostatic surface potential to develop, in real time, therapeutic and vaccine strategies adapted to each new viral epidemic.

Integrating Mass Spectrometry with Microphysiological Systems for Improved Neurochemical Studies

Microphysiological systems, often referred to as "organs-on-chips", are in vitro platforms designed to model the spatial, chemical, structural, and physiological elements of in vivo cellular environments. They enhance the evaluation of complex engineered biological systems and are a step between traditional cell culture and in vivo experimentation. As neurochemists and measurement scientists studying the molecules involved in intercellular communication in the nervous system, we focus here on recent advances in neuroscience using microneurological systems and their potential to interface with mass spectrometry. We discuss a number of examples - microfluidic devices, spheroid cultures, hydrogels, scaffolds, and fibers - highlighting those that would benefit from mass spectrometric technologies to obtain improved chemical information.

How implementation of systems biology into clinical trials accelerates understanding of diseases

Systems biology comprises a series of concepts and approaches that have been used successfully both to delineate novel biological mechanisms and to drive translational advances. The goal of systems biology is to re-integrate putatively critical elements extracted from multi-modality datasets in order to understand how interactions among multiple components form functional networks at the organism/patient-level, and how dysfunction of these networks underlies a particular disease. Due to the genetic and environmental diversity of human subjects, identification of critical elements related to a particular disease process from cross-sectional studies requires prohibitively large cohorts. Alternatively, implementation of systems biology principles to interventional clinical trials represents a unique opportunity to gain predictive understanding of complex diseases in comparatively small cohorts of patients. This paper reviews systems biology principles applicable to translational research, focusing on lessons from systems approaches to inflammation applied to multiple sclerosis. We suggest that employing systems biology methods in the design and execution of biomarker-supported, proof-of-principle clinical trials provides a singular opportunity to merge therapeutic development with a basic understanding of disease processes. The ultimate goal is to develop predictive computational models of the disease, which will revolutionize diagnostic process and provide mechanistic understanding necessary for personalized therapeutic approaches. Added, biologically meaningful information can be derived from diagnostic tests, if they are interpreted in functional relationships, rather than as independent measurements. Such systems biology based diagnostics will transform disease taxonomies from phenotypical to molecular and will allow physicians to select optimal therapeutic regimens for individual patients.

Systems biology of the functional and dysfunctional endothelium

This review provides an overview of the effect of blood flow on endothelial cell (EC) signalling pathways, applying microarray technologies to cultured cells, and in vivo studies of normal and atherosclerotic animals. It is found that in cultured ECs, 5-10% of genes are up- or down-regulated in response to fluid flow, whereas only 3-6% of genes are regulated by varying levels of fluid flow. Of all genes, 90% are regulated by the steady part of fluid flow and 10% by pulsatile components. The associated gene profiles show high variability from experiment to experiment depending on experimental conditions, and importantly, the bioinformatical methods used to analyse the data. Despite this high variability, the current data sets can be summarized with the concept of endothelial priming. In this concept, fluid flows confer protection by an up-regulation of anti-atherogenic, anti-thrombotic, and anti-inflammatory gene signatures. Consequently, predilection sites of atherosclerosis, which are associated with low-shear stress, confer low protection for atherosclerosis and are, therefore, more sensitive to high cholesterol levels. Recent studies in intact non-atherosclerotic animals confirmed these in vitro studies, and suggest that a spatial component might be present. Despite the large variability, a few signalling pathways were consistently present in the majority of studies. These were the MAPK, the nuclear factor-κB, and the endothelial nitric oxide synthase-NO pathways.

Prediction of postharvest firmness of apple using biological switch model

In this paper we present a model that predicts the softening of apple during ripening in the postharvest phase. Apple ripening starts with an autocatalytic production of ethylene, which triggers a multitude of biochemical processes like the degradation of cell wall material. This triggering of the ripening process has been modelled as a biological switch-using the activator-depleted substrate model, which is proposed earlier by Meinhardt in the field of developmental biology. The model has been calibrated using storage experiments using various apple cultivars. Furthermore, the model is proven to be valid using independent experimental data of Elstar apple under dynamic storage conditions.

A nonlinear dynamical theory of cell injury

Multifactorial injuries, such as ischemia, trauma, etc., have proven stubbornly elusive to clinical therapeutics, in spite of the binary outcome of recovery or death. This may be due, in part, to the lack of formal approaches to cell injury. We present a minimal system of nonlinear ordinary differential equations describing a theory of cell injury dynamics. A mutual antagonism between injury-driven total damage and total induced stress responses gives rise to attractors representing recovery or death. Solving across a range of injury magnitudes defines an 'injury course' containing a well-defined tipping point between recovery and death. Via the model, therapeutics is the diverting of a system on a pro-death trajectory to a pro-survival trajectory on bistable phase planes. The model plausibly explains why laboratory-based therapies have tended to fail clinically. A survival outcome is easy to achieve when lethal injury is close to the tipping point, but becomes progressively difficult as injury magnitudes increase, and there is an upper limit to salvageable injuries. The model offers novel insights into cell injury that may assist in overcoming barriers that have prevented development of clinically effective therapies for multifactorial conditions, as exemplified by brain ischemia.

The molecular and mathematical basis of Waddington's epigenetic landscape: a framework for post-Darwinian biology?

The Neo-Darwinian concept of natural selection is plausible when one assumes a straightforward causation of phenotype by genotype. However, such simple 1:1 mapping must now give place to the modern concepts of gene regulatory networks and gene expression noise. Both can, in the absence of genetic mutations, jointly generate a diversity of inheritable randomly occupied phenotypic states that could also serve as a substrate for natural selection. This form of epigenetic dynamics challenges Neo-Darwinism. It needs to incorporate the non-linear, stochastic dynamics of gene networks. A first step is to consider the mathematical correspondence between gene regulatory networks and Waddington's metaphoric 'epigenetic landscape', which actually represents the quasi-potential function of global network dynamics. It explains the coexistence of multiple stable phenotypes within one genotype. The landscape's topography with its attractors is shaped by evolution through mutational re-wiring of regulatory interactions - offering a link between genetic mutation and sudden, broad evolutionary changes.

Systems biology of stem cells: three useful perspectives to help overcome the paradigm of linear pathways

Stem cell behaviours, such as stabilization of the undecided state of pluripotency or multipotency, the priming towards a prospective fate, binary fate decisions and irreversible commitment, must all somehow emerge from a genome-wide gene-regulatory network. Its unfathomable complexity defies the standard mode of explanation that is deeply rooted in molecular biology thinking: the reduction of observables to linear deterministic molecular pathways that are tacitly taken as chains of causation. Such culture of proximate explanation that uses qualitative arguments, simple arrow-arrow schemes or metaphors persists despite the ceaseless accumulation of 'omics' data and the rise of systems biology that now offers precise conceptual tools to explain emergent cell behaviours from gene networks. To facilitate the embrace of the principles of physics and mathematics that underlie such systems and help to bridge the gap between the formal description of theorists and the intuition of experimental biologists, we discuss in qualitative terms three perspectives outside the realm of their familiar linear-deterministic view: (i) state space (ii), high-dimensionality and (iii) heterogeneity. These concepts jointly offer a new vista on stem cell regulation that naturally explains many novel, counterintuitive observations and their inherent inevitability, obviating the need for ad hoc explanations of their existence based on natural selection. Hopefully, this expanded view will stimulate novel experimental designs.

Systems biology approaches and tools for analysis of interactomes and multi-target drugs

Systems biology is essentially a proteomic and epigenetic exercise because the relatively condensed information of genomes unfolds on the level of proteins. The flexibility of cellular architectures is not only mediated by a dazzling number of proteinaceous species but moreover by the kinetics of their molecular changes: The time scales of posttranslational modifications range from milliseconds to years. The genetic framework of an organism only provides the blue print of protein embodiments which are constantly shaped by external input. Indeed, posttranslational modifications of proteins represent the scope and velocity of these inputs and fulfil the requirements of integration of external spatiotemporal signal transduction inside an organism. The optimization of biochemical networks for this type of information processing and storage results in chemically extremely fine tuned molecular entities. The huge dynamic range of concentrations, the chemical diversity and the necessity of synchronisation of complex protein expression patterns pose the major challenge of systemic analysis of biological models. One further message is that many of the key reactions in living systems are essentially based on interactions of moderate affinities and moderate selectivities. This principle is responsible for the enormous flexibility and redundancy of cellular circuitries. In complex disorders such as cancer or neurodegenerative diseases, which initially appear to be rooted in relatively subtle dysfunctions of multimodal physiologic pathways, drug discovery programs based on the concept of high affinity/high specificity compounds ("one-target, one-disease"), which has been dominating the pharmaceutical industry for a long time, increasingly turn out to be unsuccessful. Despite improvements in rational drug design and high throughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade, and the treatment of "complex diseases" remains a most pressing medical need. Currently, a change of paradigm can be observed with regard to a new interest in agents that modulate multiple targets simultaneously, essentially "dirty drugs." Targeting cellular function as a system rather than on the level of the single target, significantly increases the size of the drugable proteome and is expected to introduce novel classes of multi-target drugs with fewer adverse effects and toxicity. Multiple target approaches have recently been used to design medications against atherosclerosis, cancer, depression, psychosis and neurodegenerative diseases. A focussed approach towards "systemic" drugs will certainly require the development of novel computational and mathematical concepts for appropriate modelling of complex data. But the key is the extraction of relevant molecular information from biological systems by implementing rigid statistical procedures to differential proteomic analytics.

Towards monitoring real-time cellular response using an integrated microfluidics-matrix assisted laser desorption ionisation/nanoelectrospray ionisation-ion mobility-mass spectrometry platform

The combination of microfluidic cell trapping devices with ion mobility-mass spectrometry offers the potential for elucidating in real time the dynamic responses of small populations of cells to paracrine signals, changes in metabolite levels and delivery of drugs and toxins. Preliminary experiments examining peptides in methanol and recording the interactions of yeast and Jurkat cells with their superfusate have identified instrumental set-up and control parameters and online desalting procedures. Numerous initial experiments demonstrate and validate this new instrumental platform. Future outlooks and potential applications are addressed, specifically how this instrumentation may be used for fully automated systems biology studies of the significantly interdependent, dynamic internal workings of cellular metabolic and signalling pathways.

The cycling hair follicle as an ideal systems biology research model

In the postgenomic era, systems biology has rapidly emerged as an exciting field predicted to enhance the molecular understanding of complex biological systems by the use of quantitative experimental and mathematical approaches. Systems biology studies how the components of a biological system (e.g. genes, transcripts, proteins, metabolites) interact to bring about defined biological function or dysfunction. Living systems may be divided into five dimensions of complexity: (i) molecular; (ii) structural; (iii) temporal; (iv) abstraction and emergence; and (v) algorithmic. Understanding the details of these dimensions in living systems is the challenge that systems biology aims to address. Here, we argue that the hair follicle (HF), one of the signature features of mammals, is a perfect and clinically relevant model for systems biology research. The HF represents a stem cell-rich, essentially autonomous mini-organ, whose cyclic transformations follow a hypothetical intrafollicular "hair cycle clock" (HCC). This prototypic neuroectodermal-mesodermal interaction system, at the cross-roads of systems and chronobiology, encompasses various levels of complexity as it is subject to both intrafollicular and extrafollicular inputs (e.g. intracutaneous timing mechanisms with neural and systemic stimuli). Exploring how the cycling HF addresses the five dimensions of living systems, we argue that a systems biology approach to the study of hair growth and cycling, in man and mice, has great translational medicine potential. Namely, the easily accessible human HF invites preclinical and clinical testing of novel hypotheses generated with this approach.

Unraveling human complexity and disease with systems biology and personalized medicine

We are all perplexed that current medical practice often appears maladroit in curing our individual illnesses or disease. However, as is often the case, a lack of understanding, tools and technologies are the root cause of such situations. Human individuality is an often-quoted term but, in the context of human biology, it is poorly understood. This is compounded when there is a need to consider the variability of human populations. In the case of the former, it is possible to quantify human complexity as determined by the 35,000 genes of the human genome, the 1-10 million proteins (including antibodies) and the 2000-3000 metabolites of the human metabolome. Human variability is much more difficult to assess, since many of the variables, such as the definition of race, are not even clearly agreed on. In order to accommodate human complexity, variability and its influence on health and disease, it is necessary to undertake a systematic approach. In the past decade, the emergence of analytical platforms and bioinformatics tools has led to the development of systems biology. Such an approach offers enormous potential in defining key pathways and networks involved in optimal human health, as well as disease onset, progression and treatment. The tools and technologies now available in systems biology analyses offer exciting opportunities to exploit the emerging areas of personalized medicine. In this article, we discuss the current status of human complexity, and how systems biology and personalized medicine can impact at the individual and population level.

Translational research and biomedical informatics

A critical need exists to address real issues that appear when a physician is faced with a patient and the need to make clinical decisions that will impact the patient, their quality life, and those of the patient's family. Bridging this gap between the clinical need and the available technologies, clinical data, and clinician input is the role that Biomedical Informatics can play in driving the evolution of patient care in the post-genome era.

Non-genetic heterogeneity--a mutation-independent driving force for the somatic evolution of tumours

Clonal populations of mammalian cells are inherently heterogeneous. They contain cells that display non-genetic variability resulting from gene expression noise and the fact that gene networks have multiple stable states. These stable, heritable variants within one cell type can exhibit different levels of responsiveness to environmental conditions. Hence, they could in principle serve as a temporary substrate for natural selection in the absence of mutations. We suggest that such ubiquitous but non-genetic variability can contribute to the somatic evolution of cancer cells, hence accelerating tumour progression independently of genetic mutations.

Characterizing ion mobility-mass spectrometry conformation space for the analysis of complex biological samples

The conformation space occupied by different classes of biomolecules measured by ion mobility-mass spectrometry (IM-MS) is described for utility in the characterization of complex biological samples. Although the qualitative separation of different classes of biomolecules on the basis of structure or collision cross section is known, there is relatively little quantitative cross-section information available for species apart from peptides. In this report, collision cross sections are measured for a large suite of biologically salient species, including oligonucleotides (n = 96), carbohydrates (n = 192), and lipids (n = 53), which are compared to reported values for peptides (n = 610). In general, signals for each class are highly correlated, and at a given mass, these correlations result in predicted collision cross sections that increase in the order oligonucleotides < carbohydrates < peptides < lipids. The specific correlations are described by logarithmic regressions, which best approximate the theoretical trend of increasing collision cross section as a function of increasing mass. A statistical treatment of the signals observed within each molecular class suggests that the breadth of conformation space occupied by each class increases in the order lipids < oligonucleotides < peptides < carbohydrates. The utility of conformation space analysis in the direct analysis of complex biological samples is described, both in the context of qualitative molecular class identification and in fine structure examination within a class. The latter is demonstrated in IM-MS separations of isobaric oligonucleotides, which are interpreted by molecular dynamics simulations.

Protein kinases: starting a molecular systems view of endocytosis

The field of endocytosis is in strong need of formal biophysical modeling and mathematical analysis. At the same time, endocytosis must be much better integrated into cellular physiology to understand the former's complex behavior in such a wide range of phenotypic variations. Furthermore, the concept that endocytosis provides the space-time for signal transduction can now be experimentally addressed. In this review, we discuss these principles and argue for a systematic and top-down approach to study the endocytic membrane system. We provide a summary of published observations on protein kinases regulating endocytic machinery components and discuss global unbiased approaches to further map out kinase regulatory networks. In particular, protein phosphorylation is at the heart of controlling the physical properties of endocytosis and of integrating these physical properties into the signal transduction networks of the cell to allow a fine-tuned response to the continuously varying physiological conditions of a cell.

The systems view in addiction research

The wealth of information accessible on the molecular level after completion of sequencing of the human and other genomes and in conjunction with new high-throughput technologies like microarrays has paved the way for research on whole systems rather than on single components. Here we describe exemplarily the construction of a rather complex molecular network involved in alcohol addiction by using information from DNA-microarray studies in alcohol-dependent animals. In this network, haemoglobin downregulation in different parts of the brain reward system plays a central role in affecting synaptic plasticity, circadian rhythmicity and opioid receptors. Furthermore, we discuss the dynamic aspect of biological systems with respect to repeated and intermittent drug intake. This aspect can best be captured by the allostatic model on the molecular level. Using a molecular oscillator model where levels of oscillations are changed by repetitive drug administration, changes in set point adjustment are described that underlay allostatic shifts in drug reinforcement processes.

Intact neurogenesis is required for benefits of exercise on spatial memory but not motor performance or contextual fear conditioning in C57BL/6J mice

The mammalian hippocampus continues to generate new neurons throughout life. Experiences such as exercise, anti-depressants, and stress regulate levels of neurogenesis. Exercise increases adult hippocampal neurogenesis and enhances behavioral performance on rotarod, contextual fear and water maze in rodents. To directly test whether intact neurogenesis is required for gains in behavioral performance from exercise in C57BL/6J mice, neurogenesis was reduced using focal gamma irradiation (3 sessions of 5 Gy). Two months after treatment, mice (total n=42 males and 42 females) (Irradiated or Sham), were placed with or without running wheels (Runner or Sedentary) for 54 days. The first 10 days mice received daily injections of bromodeoxyuridine (BrdU) to label dividing cells. The last 14 days mice were tested on water maze (two trials per day for 5 days, then 1 h later probe test), rotarod (four trials per day for 3 days), and contextual fear conditioning (2 days), then measured for neurogenesis using immunohistochemical detection of BrdU and neuronal nuclear protein (NeuN) mature neuronal marker. Consistent with previous studies, in Sham animals, running increased neurogenesis fourfold and gains in performance were observed for the water maze (spatial learning and memory), rotarod (motor performance), and contextual fear (conditioning). These positive results provided the reference to determine whether gains in performance were blocked by irradiation. Irradiation reduced neurogenesis by 50% in both groups, Runner and Sedentary. Irradiation did not affect running or baseline performance on any task. Minimal changes in microglia associated with inflammation (using immunohistochemical detection of cd68) were detected at the time of behavioral testing. Irradiation did not reduce gains in performance on rotarod or contextual fear, however it eliminated gain in performance on the water maze. Results support the hypothesis that intact exercise-induced hippocampal neurogenesis is required for improved spatial memory, but not motor performance or contextual fear in C57BL/6J mice.

Integrating noninvasive molecular imaging into molecular medicine: an evolving paradigm

Molecular imaging is a rapidly emerging field, providing noninvasive visual quantitative representations of fundamental biological processes in intact living subjects. Fundamental biomedical research stands to benefit considerably from advances in molecular imaging, with improved molecular target selection, probe development and imaging instrumentation. The noninvasiveness of molecular imaging technologies will also provide benefit through improved patient care. Molecular imaging endpoints can be quantified, and therefore are particularly useful for translational research. Integration of the two disciplines of molecular imaging and molecular medicine, combined with systems-biology approaches to understanding disease complexity, promises to provide predictive, preventative and personalized medicine that will transform healthcare.