Modeling biology spanning different scales: an open challenge

Effects of Far-Red Light and Ultraviolet Light-A on Growth, Photosynthesis, Transcriptome, and Metabolome of Mint (Mentha haplocalyx Briq.)

To investigate the effects of different light qualities on the growth, photosynthesis, transcriptome, and metabolome of mint, three treatments were designed: (1) 7R3B (70% red light and 30% blue light, CK); (2) 7R3B+ far-red light (FR); (3) 7R3B+ ultraviolet light A (UVA). The results showed that supplemental FR significantly promoted the growth and photosynthesis of mint, as evidenced by the increase in plant height, plant width, biomass, effective quantum yield of PSII photochemistry (Fv'/Fm'), maximal quantum yield of PSII (Fv/Fm), and performance index (PI). UVA and CK exhibited minimal differences. Transcriptomic and metabolomic analysis indicated that a total of 788 differentially expressed genes (DEGs) and 2291 differential accumulated metabolites (DAMs) were identified under FR treatment, mainly related to plant hormone signal transduction, phenylpropanoid biosynthesis, and flavonoid biosynthesis. FR also promoted the accumulation of phenylalanine, sinapyl alcohol, methylchavicol, and anethole in the phenylpropanoid biosynthesis pathway, and increased the levels of luteolin and leucocyanidin in the flavonoid biosynthesis pathway, which may perhaps be applied in practical production to promote the natural antibacterial and antioxidant properties of mint. An appropriate increase in FR radiation might alter transcript reprogramming and redirect metabolic flux in mint, subsequently regulating its growth and secondary metabolism. Our study uncovered the regulation of FR and UVA treatments on mint in terms of growth, physiology, transcriptome, and metabolome, providing reference for the cultivation of mint and other horticultural plants.

A decade of thermostatted kinetic theory models for complex active matter living systems

In the last decade, the thermostatted kinetic theory has been proposed as a general paradigm for the modeling of complex systems of the active matter and, in particular, in biology. Homogeneous and inhomogeneous frameworks of the thermostatted kinetic theory have been employed for modeling phenomena that are the result of interactions among the elements, called active particles, composing the system. Functional subsystems contain heterogeneous active particles that are able to perform the same task, called activity. Active matter living systems usually operate out-of-equilibrium; accordingly, a mathematical thermostat is introduced in order to regulate the fluctuations of the activity of particles. The time evolution of the functional subsystems is obtained by introducing the conservative and the nonconservative interactions which represent activity-transition, natural birth/death, induced proliferation/destruction, and mutation of the active particles. This review paper is divided in two parts: In the first part the review deals with the mathematical frameworks of the thermostatted kinetic theory that can be found in the literature of the last decade and a unified approach is proposed; the second part of the review is devoted to the specific mathematical models derived within the thermostatted kinetic theory presented in the last decade for complex biological systems, such as wound healing diseases, the recognition process and the learning dynamics of the human immune system, the hiding-learning dynamics and the immunoediting process occurring during the cancer-immune system competition. Future research perspectives are discussed from the theoretical and application viewpoints, which suggest the important interplay among the different scholars of the applied sciences and the desire of a multidisciplinary approach or rather a theory for the modeling of every active matter system.

Unveiling Forkhead-mediated regulation of yeast cell cycle and metabolic networks

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Findings from genome-wide ChIP studies on budding yeast Forkheads are interpreted.

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Power, challenges and limitation of ChIP studies are presented by target gene analysis.

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Forkheads regulate metabolic targets through which cell division may be coordinated.

Transcription factors are regulators of the cell’s genomic landscape. By switching single genes or entire molecular pathways on or off, transcription factors modulate the precise timing of their activation. The Forkhead (Fkh) transcription factors are evolutionarily conserved to regulate organismal physiology and cell division. In addition to molecular biology and biochemical efforts, genome-wide studies have been conducted to characterize the genomic landscape potentially regulated by Forkheads in eukaryotes. Here, we discuss and interpret findings reported in six genome-wide Chromatin ImmunoPrecipitation (ChIP) studies, with a particular focus on ChIP-chip and ChIP-exo. We highlight their power and challenges to address Forkhead-mediated regulation of the cellular landscape in budding yeast. Expression changes of the targets identified in the binding assays are investigated by taking expression data for Forkhead deletion and overexpression into account. Forkheads are revealed as regulators of the metabolic network through which cell cycle dynamics may be temporally coordinated further, in addition to their well-known role as regulators of the gene cluster responsible for cell division.

A new hierarchical approach to multi-level model abstraction for simplifying ODE models of biological networks and a case study: The G1/S Checkpoint/DNA damage signalling pathways of mammalian cell cycle

Model reduction is an important topic in studies of biological systems. By reducing the complexity of large models through multi-level models while keeping the essence (biological meaning) of the model, model reduction can help answer many important questions about these systems. In this paper, we present a new reduction method based on hierarchical representation and a lumping approach. We used G1/S checkpoint pathway represented in Ordinary Differential Equations (ODE) in Iwamoto et al. (2011) as a case study to present this reduction method. The approach consists of two parts; the first part represents the biological network as a hierarchy (multiple levels) based on protein binding relations, which allowed us to model the biological network at different levels of abstraction. The second part applies different levels (level 1, 2 and 3) of lumping the species together depending on the level of the hierarchy, resulting in a reduced and transformed model for each level. The model at each level is a representation of the whole system and can address questions pertinent to that level. We develop and simulate reduced models for levels-1, 2 and 3 of lumping for the G1/S checkpoint pathway and evaluate the biological plausibility of the proposed method by comparing the results with the original ODE model of Iwamoto et al. (2011). The results for continuous dynamics of the G1/S checkpoint pathway with or without DNA-damage for reduced models of level- 1, 2 and 3 of lumping are in very good agreement and consistent with the original model results and with biological findings. Therefore, the reduced models (level-1, 2 and 3) can be used to study cell cycle progression in G1 and the effects of DNA damage on it. It is suitable for reducing complex ODE biological network models while retaining accurate continuous dynamics of the system. The 3 levels of the reduced models respectively achieved 20%, 26% and 31% reduction of the base model. Moreover, the reduced model is more efficient to run (30%, 44% and 52% time reduction for the three levels) and generate solutions than the original ODE model. Simplification of complex mathematical models is possible and the proposed reduction method has the potential to make an impact across many fields of biomedical research.

Atomistic Basis of Microtubule Dynamic Instability Assessed Via Multiscale Modeling

Microtubule “dynamic instability,” the abrupt switching from assembly to disassembly caused by the hydrolysis of GTP to GDP within the β subunit of the αβ-tubulin heterodimer, is necessary for vital cellular processes such as mitosis and migration. Despite existing high-resolution structural data, the key mechanochemical differences between the GTP and GDP states that mediate dynamic instability behavior remain unclear. Starting with a published atomic-level structure as an input, we used multiscale modeling to find that GTP hydrolysis results in both longitudinal bond weakening (~ 4 k_BT) and an outward bending preference (~ 1.5 k_BT) to both drive dynamic instability and give rise to the microtubule tip structures previously observed by light and electron microscopy. More generally, our study provides an example where atomic level structural information is used as the sole input to predict cellular level dynamics without parameter adjustment.

Generation of digital patients for the simulation of tuberculosis with UISS-TB

BACKGROUND

The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial.

RESULTS

One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject.

CONCLUSIONS

We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration.

Development of a kinetic model expressing anomalous phenomena in human induced pluripotent stem cell culture

During culture with feeder cells, deviation from the undifferentiated state of human induced pluripotent stem cells (hiPSCs) occurs at a very low frequency. Anomalous cell migration in central and peripheral regions of hiPSC colonies has been suggested to be the trigger for this phenomenon. To confirm this hypothesis, sequential cell migration prior to deviation must be demonstrated. This has been difficult using in vitro methods. We therefore developed a kinetic model with a proposed definition of anomalous cell migration as continuous relatively fast or slow cell migration. The developed model was validated via in silico reproduction of deviation phenomenon observed in vitro, such as the positions of deviated cells in a colony and the frequency of deviation in culture. This model suggests that anomalous cell migration-driven hiPSC deviation can be explained by two factors: a mechanical stimulus, represented by cell migration, and duration of the mechanical stimulus. The factor "duration of mechanical stimulus" sets our model apart from others, and helps to realize the ultra-rare trigger (approximately 10^-5) of deviation from the undifferentiated state in hiPSC culture.

CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Complex Biological Models

Introduction

Mathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short.

Methods

Herein, we develop CaliPro, an iterative, model-agnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro.

Results

We illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals.

Conclusions

We present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis.

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

In silico clinical trials: concepts and early adoptions

Innovations in information and communication technology infuse all branches of science, including life sciences. Nevertheless, healthcare is historically slow in adopting technological innovation, compared with other industrial sectors. In recent years, new approaches in modelling and simulation have started to provide important insights in biomedicine, opening the way for their potential use in the reduction, refinement and partial substitution of both animal and human experimentation. In light of this evidence, the European Parliament and the United States Congress made similar recommendations to their respective regulators to allow wider use of modelling and simulation within the regulatory process. In the context of in silico medicine, the term 'in silico clinical trials' refers to the development of patient-specific models to form virtual cohorts for testing the safety and/or efficacy of new drugs and of new medical devices. Moreover, it could be envisaged that a virtual set of patients could complement a clinical trial (reducing the number of enrolled patients and improving statistical significance), and/or advise clinical decisions. This article will review the current state of in silico clinical trials and outline directions for a full-scale adoption of patient-specific modelling and simulation in the regulatory evaluation of biomedical products. In particular, we will focus on the development of vaccine therapies, which represents, in our opinion, an ideal target for this innovative approach.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

Morpho-Physiological Responses of Pisum sativum L. to Different Light-Emitting Diode (LED) Light Spectra in Combination with Biochar Amendment

Light quality and nutrient availability are the primary factors that influence plant growth and development. In a research context of improving indoor plant cultivation while lowering environmental impact practices, we investigated the effect of different light spectra, three provided by light-emitting diodes (LEDs), and one by a fluorescent lamp, on the morpho-physiology of Pisum sativum L. seedlings grown in the presence/absence of biochar. We found that all morpho-physiological traits are sensitive to changes in the red-to-far-red light (R:FR) ratio related to the light spectra used. In particular, seedlings that were grown with a LED type characterized by the lowest R:FR ratio (~2.7; AP67), showed good plant development, both above- and belowground, especially when biochar was present. Biochar alone did not affect the physiological traits, which were influenced by the interplay with lighting type. AP67 LED type had a negative impact only on leaf fluorescence emission in light conditions, which was further exacerbated by the addition of biochar to the growing media. However, we found that the combination of biochar with a specific optimal light spectrum may have a synergetic effect enhancing pea seedling physiological performances and fruit yield and fostering desired traits. This is a promising strategy for indoor plant production while respecting the environment.

The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach

BACKGROUND AND LIMITATIONS

Impaired wound healing (WH) and chronic inflammation are hallmarks of non-communicable diseases (NCDs). However, despite WH being a recognized player in NCDs, mainstream therapies focus on (un)targeted damping of the inflammatory response, leaving WH largely unaddressed, owing to three main factors. The first is the complexity of the pathway that links inflammation and wound healing; the second is the dual nature, local and systemic, of WH; and the third is the limited acknowledgement of genetic and contingent causes that disrupt physiologic progression of WH.

PROPOSED APPROACH

Here, in the frame of Predictive, Preventive, and Personalized Medicine (PPPM), we integrate and revisit current literature to offer a novel systemic view on the cues that can impact on the fate (acute or chronic inflammation) of WH, beyond the compartmentalization of medical disciplines and with the support of advanced computational biology.

CONCLUSIONS

This shall open to a broader understanding of the causes for WH going awry, offering new operational criteria for patients' stratification (prediction and personalization). While this may also offer improved options for targeted prevention, we will envisage new therapeutic strategies to reboot and/or boost WH, to enable its progression across its physiological phases, the first of which is a transient acute inflammatory response versus the chronic low-grade inflammation characteristic of NCDs.

Patient-Specific Bone Multiscale Modelling, Fracture Simulation and Risk Analysis-A Survey

This paper provides a starting point for researchers and practitioners from biology, medicine, physics and engineering who can benefit from an up-to-date literature survey on patient-specific bone fracture modelling, simulation and risk analysis. This survey hints at a framework for devising realistic patient-specific bone fracture simulations. This paper has 18 sections: Section 1 presents the main interested parties; Section 2 explains the organzation of the text; Section 3 motivates further work on patient-specific bone fracture simulation; Section 4 motivates this survey; Section 5 concerns the collection of bibliographical references; Section 6 motivates the physico-mathematical approach to bone fracture; Section 7 presents the modelling of bone as a continuum; Section 8 categorizes the surveyed literature into a continuum mechanics framework; Section 9 concerns the computational modelling of bone geometry; Section 10 concerns the estimation of bone mechanical properties; Section 11 concerns the selection of boundary conditions representative of bone trauma; Section 12 concerns bone fracture simulation; Section 13 presents the multiscale structure of bone; Section 14 concerns the multiscale mathematical modelling of bone; Section 15 concerns the experimental validation of bone fracture simulations; Section 16 concerns bone fracture risk assessment. Lastly, glossaries for symbols, acronyms, and physico-mathematical terms are provided.

ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast

The understanding of the multi-scale nature of molecular networks represents a major challenge. For example, regulation of a timely cell cycle must be coordinated with growth, during which changes in metabolism occur, and integrate information from the extracellular environment, e.g. signal transduction. Forkhead transcription factors are evolutionarily conserved among eukaryotes, and coordinate a timely cell cycle progression in budding yeast. Specifically, Fkh1 and Fkh2 are expressed during a lengthy window of the cell cycle, thus are potentially able to function as hubs in the multi-scale cellular environment that interlocks various biochemical networks. Here we report on a novel ChIP-exo dataset for Fkh1 and Fkh2 in both logarithmic and stationary phases, which is analyzed by novel and existing software tools. Our analysis confirms known Forkhead targets from available ChIP-chip studies and highlights novel ones involved in the cell cycle, metabolism and signal transduction. Target genes are analyzed with respect to their function, temporal expression during the cell cycle, correlation with Fkh1 and Fkh2 as well as signaling and metabolic pathways they occur in. Furthermore, differences in targets between Fkh1 and Fkh2 are presented. Our work highlights Forkhead transcription factors as hubs that integrate multi-scale networks to achieve proper timing of cell division in budding yeast.

Non-invasive Imaging and Modeling of Liver Regeneration After Partial Hepatectomy

The liver has a unique regenerative capability upon injury or partial resection. The regeneration process comprises a complex interplay between parenchymal and non-parenchymal cells and is tightly regulated at different scales. Thus, we investigated liver regeneration using multi-scale methods by combining non-invasive imaging with immunohistochemical analyses. In this context, non-invasive imaging can provide quantitative data of processes involved in liver regeneration at organ and body scale. We quantitatively measured liver volume recovery after 70% partial hepatectomy (PHx) by micro computed tomography (μCT) and investigated changes in the density of CD68⁺ macrophages by fluorescence-mediated tomography (FMT) combined with μCT using a newly developed near-infrared fluorescent probe. In addition, angiogenesis and tissue-resident macrophages were analyzed by immunohistochemistry. Based on the results, a model describing liver regeneration and the interactions between different cell types was established. In vivo analysis of liver volume regeneration over 21 days after PHx by μCT imaging demonstrated that the liver volume rapidly increased after PHx reaching a maximum at day 14 and normalizing until day 21. An increase in CD68⁺ macrophage density in the liver was detected from day 4 to day 8 by combined FMT-μCT imaging, followed by a decline towards control levels between day 14 and day 21. Immunohistochemistry revealed the highest angiogenic activity at day 4 after PHx that continuously declined thereafter, whereas the density of tissue-resident CD169⁺ macrophages was not altered. The simulated time courses for volume recovery, angiogenesis and macrophage density reflect the experimental data describing liver regeneration after PHx at organ and tissue scale. In this context, our study highlights the importance of non-invasive imaging for acquiring quantitative organ scale data that enable modeling of liver regeneration.

Modeling the Effect of High Calorie Diet on the Interplay between Adipose Tissue, Inflammation, and Diabetes

BACKGROUND

Type 2 diabetes (T2D) is a chronic metabolic disease potentially leading to serious widespread tissue damage. Human organism develops T2D when the glucose-insulin control is broken for reasons that are not fully understood but have been demonstrated to be linked to the emergence of a chronic inflammation. Indeed such low-level chronic inflammation affects the pancreatic production of insulin and triggers the development of insulin resistance, eventually leading to an impaired control of the blood glucose concentration. On the contrary, it is well-known that obesity and inflammation are strongly correlated.

AIM

In this study, we investigate in silico the effect of overfeeding on the adipose tissue and the consequent set up of an inflammatory state. We model the emergence of the inflammation as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high calorie intake.

RESULTS

The model reproduces the fat accumulation due to excessive caloric intake observed in two clinical studies. Moreover, while showing consistent weight gains over long periods of time, it reveals a drift of the macrophage population toward the proinflammatory phenotype, thus confirming its association with fatness.

Studying microbial functionality within the gut ecosystem by systems biology

Humans are not autonomous entities. We are all living in a complex environment, interacting not only with our peers, but as true holobionts; we are also very much in interaction with our coexisting microbial ecosystems living on and especially within us, in the intestine. Intestinal microorganisms, often collectively referred to as intestinal microbiota, contribute significantly to our daily energy uptake by breaking down complex carbohydrates into simple sugars, which are fermented to short-chain fatty acids and subsequently absorbed by human cells. They also have an impact on our immune system, by suppressing or enhancing the growth of malevolent and beneficial microbes. Our lifestyle can have a large influence on this ecosystem. What and how much we consume can tip the ecological balance in the intestine. A "western diet" containing mainly processed food will have a different effect on our health than a balanced diet fortified with pre- and probiotics. In recent years, new technologies have emerged, which made a more detailed understanding of microbial communities and ecosystems feasible. This includes progress in the sequencing of PCR-amplified phylogenetic marker genes as well as the collective microbial metagenome and metatranscriptome, allowing us to determine with an increasing level of detail, which microbial species are in the microbiota, understand what these microorganisms do and how they respond to changes in lifestyle and diet. These new technologies also include the use of synthetic and in vitro systems, which allow us to study the impact of substrates and addition of specific microbes to microbial communities at a high level of detail, and enable us to gather quantitative data for modelling purposes. Here, we will review the current state of microbiome research, summarizing the computational methodologies in this area and highlighting possible outcomes for personalized nutrition and medicine.

GEMMER: GEnome-wide tool for Multi-scale Modeling data Extraction and Representation for Saccharomyces cerevisiae

Motivation

Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner.

Results

We present GEMMER (GEnome-wide tool for Multi-scale Modeling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae. GEMMER creates network visualizations that integrate information on function, temporal expression, localization and abundance from various existing databases. GEMMER supports modeling efforts by effortlessly gathering this information and providing convenient export options for images and their underlying data.

Availability and implementation

GEMMER is freely available at http://gemmer.barberislab.com. Source code, written in Python, JavaScript library D3js, PHP and JSON, is freely available at https://github.com/barberislab/GEMMER.

Supplementary information

Supplementary data are available at Bioinformatics online.

A complete categorization of multiscale models of infectious disease systems

Modelling of infectious disease systems has entered a new era in which disease modellers are increasingly turning to multiscale modelling to extend traditional modelling frameworks into new application areas and to achieve higher levels of detail and accuracy in characterizing infectious disease systems. In this paper we present a categorization framework for categorizing multiscale models of infectious disease systems. The categorization framework consists of five integration frameworks and five criteria. We use the categorization framework to give a complete categorization of host-level immuno-epidemiological models (HL-IEMs). This categorization framework is also shown to be applicable in categorizing other types of multiscale models of infectious diseases beyond HL-IEMs through modifying the initial categorization framework presented in this study. Categorization of multiscale models of infectious disease systems in this way is useful in bringing some order to the discussion on the structure of these multiscale models.

Artificial cell mimics as simplified models for the study of cell biology

Living cells are hugely complex chemical systems composed of a milieu of distinct chemical species (including DNA, proteins, lipids, and metabolites) interconnected with one another through a vast web of interactions: this complexity renders the study of cell biology in a quantitative and systematic manner a difficult task. There has been an increasing drive towards the utilization of artificial cells as cell mimics to alleviate this, a development that has been aided by recent advances in artificial cell construction. Cell mimics are simplified cell-like structures, composed from the bottom-up with precisely defined and tunable compositions. They allow specific facets of cell biology to be studied in isolation, in a simplified environment where control of variables can be achieved without interference from a living and responsive cell. This mini-review outlines the core principles of this approach and surveys recent key investigations that use cell mimics to address a wide range of biological questions. It will also place the field in the context of emerging trends, discuss the associated limitations, and outline future directions of the field. Impact statement Recent years have seen an increasing drive to construct cell mimics and use them as simplified experimental models to replicate and understand biological phenomena in a well-defined and controlled system. By summarizing the advances in this burgeoning field, and using case studies as a basis for discussion on the limitations and future directions of this approach, it is hoped that this minireview will spur others in the experimental biology community to use artificial cells as simplified models with which to probe biological systems.

Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions

Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels

In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.

Computational modeling in melanoma for novel drug discovery

INTRODUCTION

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches.

AREAS COVERED

This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials.

EXPERT OPINION

Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer

BACKGROUND

Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease.

RESULT

Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive.

Modeling-Enabled Systems Nutritional Immunology

This review highlights the fundamental role of nutrition in the maintenance of health, the immune response, and disease prevention. Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, metabolism, genetic predisposition, and the immune system interact to delineate health and disease. The review sets an unconventional path to apply complex science methodologies to nutritional immunology research, discovery, and development through “use cases” centered around the impact of nutrition on the gut microbiome and immune responses. Our systems nutritional immunology analyses, which include modeling and informatics methodologies in combination with pre-clinical and clinical studies, have the potential to discover emerging systems-wide properties at the interface of the immune system, nutrition, microbiome, and metabolism.

Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient's autoregulatory capacity?

Bioregulatory systems medicine (BrSM) is a paradigm that aims to advance current medical practices. The basic scientific and clinical tenets of this approach embrace an interconnected picture of human health, supported largely by recent advances in systems biology and genomics, and focus on the implications of multi-scale interconnectivity for improving therapeutic approaches to disease. This article introduces the formal incorporation of these scientific and clinical elements into a cohesive theoretical model of the BrSM approach. The authors review this integrated body of knowledge and discuss how the emergent conceptual model offers the medical field a new avenue for extending the armamentarium of current treatment and healthcare, with the ultimate goal of improving population health.

Multiscale modelling in immunology: a review

One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.

Computational Studies of the Intestinal Host-Microbiota Interactome

A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.