The rise of computational biology

Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation

Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.

Ten simple rules for managing laboratory information

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

What keeps nanopores boiling

The liquid-to-vapor transition can occur under unexpected conditions in nanopores, opening the door to fundamental questions and new technologies. The physics of boiling in confinement is progressively introduced, starting from classical nucleation theory, passing through nanoscale effects, and terminating with the material and external parameters that affect the boiling conditions. The relevance of boiling in specific nanoconfined systems is discussed, focusing on heterogeneous lyophobic systems, chromatographic columns, and ion channels. The current level of control of boiling in nanopores enabled by microporous materials such as metal organic frameworks and biological nanopores paves the way to thrilling theoretical challenges and to new technological opportunities in the fields of energy, neuromorphic computing, and sensing.

Emergence in Artificial Life

Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement "life is complex." Thus understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. But how useful is this to understanding living systems? Artificial Life (ALife) has been developed in recent decades to study life using a synthetic approach: Build it to understand it. ALife systems are not so complex, be they soft (simulations), hard (robots), or wet(protocells). Thus, we can aim at first understanding emergence in ALife, to then use this knowledge in biology. I argue that to understand emergence and life, it becomes useful to use information as a framework. In a general sense, I define emergence as information that is not present at one scale but present at another. This perspective avoids problems of studying emergence from a materialist framework and can also be useful in the study of self-organization and complexity.

Digitalization and Uncertainty in the University: Coherence and Collegiality Through a Metacurriculum

Recent initiatives to promote ‘digitalization’ in education exhorting increased digital literacy and ‘computational thinking’ have invited implementation research methods to transform curricula, teaching, and learning. While conceived as a movement from the present of education to an imagined future where envisaged curricula embrace data-oriented and computational practices across subjects, we ask: whose present? Whose future? We revisit the concept of a metacurriculum (first conceived in the 1990s) as a way of addressing this question, while not avoiding the challenges inherent in the adaptation of education to an increasingly complex postdigital environment. We argue that the principal challenge facing institutional and individual adaptation is increasing environmental uncertainty produced by technology, not deficiency in individual skills. Using the uncertainty concept, we present a practical co-designed and dialogical approach supporting the student and teacher journeys towards the transdisciplinary opportunities opened out by technology, based on a cybernetic model of intersubjectivity. We discuss the explanatory power of uncertainty in this context, focusing on the ways it can encourage dialogue, collegiality, and experimentation. Evidence for this is presented in a case study from a Russian University Business School where a large group of teachers co-designed and delivered a dialogical module on digitalization and interdisciplinarity over a period of 4 years—a collaboration ended by recent geopolitical events. In analyzing data from one of the central activities of this course, we focus on the teacher collegiality and the students’ mechanisms of selection in navigating the transdisciplinary space, and how these mechanisms may provide deeper insight into the dialogical underpinnings of education.

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.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

Impact of explicit area scaling on kinetic models involving multiple compartments

BACKGROUND

Computational modelling of cell biological processes is a frequently used technique to analyse the underlying mechanisms and to generally understand the behaviour of these processes in the context of a pathway, network or even the whole cell. The most common technique in this context is the usage of ordinary differential equations that describe the kinetics of the relevant processes in mechanistic detail. Here, it is usually assumed that the content of the cell is well-stirred and thus homogeneous - which is of course an over-simplification, but often worked in the past. However, many processes happen at membranes and thus not in 3D, but in 2D. The scaling of the rates of these processes poses a special problem, if volumes of compartments are changed. They will typically scale with an area, but not with the volume of the involved compartment. However, commonly, this is neglected when setting up models and/or volume scaling also sometimes automatically happens when using modelling software in the field.

RESULTS

Here, we investigate generic as well as specific, realistic cases to find out, how strong the impact of the wrong scaling is for the outcome of simulations. We show that the importance of correct area scaling depends on the architecture of the reaction site and its changes upon volume alterations and it is hard to foresee, if it has a significant impact or not just by looking at the original model set-up. Moreover, scaled rates might exhibit more or less control over the behaviour of the system and therefore, accordingly, incorrect scaling will have more or less influence.

CONCLUSIONS

Working with multi-compartment reactions requires a careful consideration of the correct scaling of the rates when changing the volumes of the involved compartments. The error following incorrect scaling - often done by scaling with the volume of the respective compartments can lead to significant aberrations of model behaviour.

Modelling of processes in nerve fibres at the interface of physiology and mathematics

The in silico simulations are widely used in contemporary systems biology including the analysis of nerve pulse propagation. As known from numerous experiments, the propagation of an action potential is accompanied by mechanical and thermal effects. This calls for an analysis at the interface of physics, physiology and mathematics. In this paper, the background of the model equations governing the effects in nerve fibres is analysed from a physical viewpoint and then discussed how to unite them into a system by using the coupling forces. The leading hypothesis associates the coupling to the changes of variables, not to their values or amplitudes. This hypothesis models actually the physiological mechanisms of energy transductions in a fibre. The general assumptions in modelling the processes and the properties of the coupled system of equations are described. The dimensionless mathematical model which couples the action potential with mechanical waves together with temperature effects is presented in "Appendix". This model generates an ensemble of waves including the electrical signal and mechanical and thermal effects.

Multi-scale approaches for the simulation of cardiac electrophysiology: II - Tissue-level structure and function

Computational models of the heart, from cell-level models, through one-, two- and three-dimensional tissue-level simplifications, to biophysically-detailed three-dimensional models of the ventricles, atria or whole heart, allow the simulation of excitation and propagation of this excitation, and have provided remarkable insight into the normal and pathological functioning of the heart. In this article we present equations for modelling cellular excitation (i.e. the cell action potential) from both a phenomenological and a biophysical perspective. Hodgkin-Huxley formalism is discussed, along with the current generation of biophysically-detailed cardiac cell models. Alternative Markovian formulations for modelling ionic currents are also presented. Equations describing propagation of this cellular excitation, through one-, two- and three-dimensional idealised or realistic tissues, are then presented. For all types of model, from cell to tissue, methods for discretisation and integration of the underlying equations are discussed. The article finishes with a discussion of two tissue-level experimental imaging techniques - diffusion tensor magnetic resonance imaging and optical imaging - that can be used to provide data for parameterisation and validation of cell- and tissue-level cardiac models.

Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders

The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the discovery of disease mechanisms that would link genetic findings to clinical symptoms and behavior. This applies also to schizophrenia, for which genome-wide association studies have identified a large number of genetic risk loci, spanning hundreds of genes with diverse functionalities. Importantly, the multitude of the associated variants and their prevalence in the healthy population limit the potential of a reductionist functional genetics approach as a stand-alone solution to discover the disease pathology. In this review, we outline the key concepts of a "biophysical psychiatry," an approach that employs large-scale mechanistic, biophysics-founded computational modelling to increase transdisciplinary understanding of the pathophysiology and strive toward robust predictions. We discuss recent scientific advances that allow a synthesis of previously disparate fields of psychiatry, neurophysiology, functional genomics, and computational modelling to tackle open questions regarding the pathophysiology of heritable mental disorders. We argue that the complexity of the increasing amount of genetic data exceeds the capabilities of classical experimental assays and requires computational approaches. Biophysical psychiatry, based on modelling diseased brain networks using existing and future knowledge of basic genetic, biochemical, and functional properties on a single neuron to a microcircuit level, may allow a leap forward in deriving interpretable biomarkers and move the field toward novel treatment options.

The Clock-Free Asynchronous Receiver Design for Molecular Timing Channels in Diffusion-Based Molecular Communications

In diffusion-based molecular communications, time synchronization is a major reason for the increase of system structure complexity. In this paper, we consider the asynchronous receiver design for molecular communications with information symbols conveyed in the time of released molecules. The main contribution of this paper is that we develop a detector called clock-free asynchronous receiver design (CFARD), in which the receiver recovers the information symbols without measuring the arrival time of molecules. The theoretical analysis indicates that compared with the synchronous receiver designs, the proposed scheme considerably lowers the structure complexity for information demodulation, which is of great significance to the feasibility of nano-scale molecular communications systems with the limitation of energy and size. The numerical results show that in the comparison of bit error ratio (BER) performance, the proposed asynchronous receiver design outperforms the synchronous linear average filter (LAF) detector and approaches to the synchronous maximum likelihood (ML) detector and first arrival (FA) detector.

The Asymmetric-Distance Metrics for Decoding of Convolutional Codes in Diffusion-Based Molecular Communications

In this paper, we focus on the channel coding for diffusion-based molecular communications (MC) with information conveyed in the time of molecules released. The symbol error probability (SEP) is theoretically derived for multiple channel use. The conception called asymmetric-distance is introduced as the metric from the transmitted codeword to the received codeword. A new decoding scheme based on asymmetric-distance is proposed for convolutional codes, which significantly enhances the communication reliability. Biological circuits for the implementation of the proposed channel coding scheme in MC systems are designed by bio-nanomachines. The theoretical analysis indicates that the proposed decoding scheme provides an approximately maximum likelihood estimation. Compared with uncoded systems, Hamming codes, convolutional codes based on Hamming distance (CCHD) and some new coding schemes for molecular communications, numerical results show that the proposed convolutional codes based on asymmetric-distance (CCAD) offer better bit error ratio (BER) performance with the same throughput. The simulation results illustrate that the proposed channel coding scheme is efficient for a larger range of channel environment than other conventional channel coding schemes.

Modeling persistence of motion in a crowded environment: The diffusive limit of excluding velocity-jump processes

Persistence of motion is the tendency of an object to maintain motion in a direction for short time scales without necessarily being biased in any direction in the long term. One of the most appropriate mathematical tools to study this behavior is an agent-based velocity-jump process. In the absence of agent-agent interaction, the mean-field continuum limit of the agent-based model (ABM) gives rise to the well known hyperbolic telegraph equation. When agent-agent interaction is included in the ABM, a strictly advective system of partial differential equations (PDEs) can be derived at the population level. However, no diffusive limit of the ABM has been obtained from such a model. Connecting the microscopic behavior of the ABM to a diffusive macroscopic description is desirable, since it allows the exploration of a wider range of scenarios and establishes a direct connection with commonly used statistical tools of movement analysis. In order to connect the ABM at the population level to a diffusive PDE at the population level, we consider a generalization of the agent-based velocity-jump process on a two-dimensional lattice with three forms of agent interaction. This generalization allows us to take a diffusive limit and obtain a faithful population-level description. We investigate the properties of the model at both the individual and population levels and we elucidate some of the models' key characteristic features. In particular, we show an intrinsic anisotropy inherent to the models and we find evidence of a spontaneous form of aggregation at both the micro- and macroscales.

Investigating Mutations to Reduce Huntingtin Aggregation by Increasing Htt-N-Terminal Stability and Weakening Interactions with PolyQ Domain

Huntington's disease is a fatal autosomal genetic disorder characterized by an expanded glutamine-coding CAG repeat sequence in the huntingtin (Htt) exon 1 gene. The Htt protein associated with the disease misfolds into toxic oligomers and aggregate fibril structures. Competing models for the misfolding and aggregation phenomena have suggested the role of the Htt-N-terminal region and the CAG trinucleotide repeats (polyQ domain) in affecting aggregation propensities and misfolding. In particular, one model suggests a correlation between structural stability and the emergence of toxic oligomers, whereas a second model proposes that molecular interactions with the extended polyQ domain increase aggregation propensity. In this paper, we computationally explore the potential to reduce Htt aggregation by addressing the aggregation causes outlined in both models. We investigate the mutation landscape of the Htt-N-terminal region and explore amino acid residue mutations that affect its structural stability and hydrophobic interactions with the polyQ domain. Out of the millions of 3-point mutation combinations that we explored, the (L4K E12K K15E) was the most promising mutation combination that addressed aggregation causes in both models. The mutant structure exhibited extreme alpha-helical stability, low amyloidogenicity potential, a hydrophobic residue replacement, and removal of a solvent-inaccessible intermolecular side chain that assists oligomerization.

Mathematics in modern immunology

Mathematical and statistical methods enable multidisciplinary approaches that catalyse discovery. Together with experimental methods, they identify key hypotheses, define measurable observables and reconcile disparate results. We collect a representative sample of studies in T-cell biology that illustrate the benefits of modelling–experimental collaborations and that have proven valuable or even groundbreaking. We conclude that it is possible to find excellent examples of synergy between mathematical modelling and experiment in immunology, which have brought significant insight that would not be available without these collaborations, but that much remains to be discovered.

Anti-oxidative cellular protection effect of fasting-induced autophagy as a mechanism for hormesis

The aim of this investigation was to test the hypothesis that fasting-induced augmented lysosomal autophagic turnover of cellular proteins and organelles will reduce potentially harmful lipofuscin (age-pigment) formation in cells by more effectively removing oxidatively damaged proteins. An animal model (marine snail--common periwinkle, Littorina littorea) was used to experimentally test this hypothesis. Snails were deprived of algal food for 7 days to induce an augmented autophagic response in their hepatopancreatic digestive cells (hepatocyte analogues). This treatment resulted in a 25% reduction in the cellular content of lipofuscin in the digestive cells of the fasting animals in comparison with snails fed ad libitum on green alga (Ulva lactuca). Similar findings have previously been observed in the digestive cells of marine mussels subjected to copper-induced oxidative stress. Additional measurements showed that fasting significantly increased cellular health based on lysosomal membrane stability, and reduced lipid peroxidation and lysosomal/cellular triglyceride. These findings support the hypothesis that fasting-induced augmented autophagic turnover of cellular proteins has an anti-oxidative cytoprotective effect by more effectively removing damaged proteins, resulting in a reduction in the formation of potentially harmful proteinaceous aggregates such as lipofuscin. The inference from this study is that autophagy is important in mediating hormesis. An increase was demonstrated in physiological complexity with fasting, using graph theory in a directed cell physiology network (digraph) model to integrate the various biomarkers. This was commensurate with increased health status, and supportive of the hormesis hypothesis. The potential role of enhanced autophagic lysosomal removal of damaged proteins in the evolutionary acquisition of stress tolerance in intertidal molluscs is discussed and parallels are drawn with the growing evidence for the involvement of autophagy in hormesis and anti-ageing processes.

The role of markup for enabling interoperability in health informatics

Interoperability is the faculty of making information systems work together. In this paper we will distinguish a number of different forms that interoperability can take and show how they are realized on a variety of physiological and health care use cases. The last 15 years has seen the rise of very cheap digital storage both on and off site. With the advent of the Internet of Things people's expectations are for greater interconnectivity and seamless interoperability. The potential impact these technologies have on healthcare are dramatic: from improved diagnoses through immediate access to a patient's electronic health record, to in silico modeling of organs and early stage drug trials, to predictive medicine based on top-down modeling of disease progression and treatment. We will begin by looking at the underlying technology, classify the various kinds of interoperability that exist in the field, and discuss how they are realized. We conclude with a discussion on future possibilities that big data and further standardizations will enable.

Systems biology perspectives on minimal and simpler cells

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells.

Cancer systems biology and modeling: microscopic scale and multiscale approaches

Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.

Dealing with diversity in computational cancer modeling

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.

Application of cardiac electrophysiology simulations to pro-arrhythmic safety testing

Concerns over cardiac side effects are the largest single cause of compound attrition during pharmaceutical drug development. For a number of years, biophysically detailed mathematical models of cardiac electrical activity have been used to explore how a compound, interfering with specific ion-channel function, may explain effects at the cell-, tissue- and organ-scales. With the advent of high-throughput screening of multiple ion channels in the wet-lab, and improvements in computational modelling of their effects on cardiac cell activity, more reliable prediction of pro-arrhythmic risk is becoming possible at the earliest stages of drug development. In this paper, we review the current use of biophysically detailed mathematical models of cardiac myocyte electrical activity in drug safety testing, and suggest future directions to employ the full potential of this approach.

Bioinformatics applied to gene transcription regulation

Understanding regulation of gene transcription is central to molecular biology as well as being of great interest in medicine. The molecular syntax of the concerted transcriptional activation/repression of gene networks in mammal cells, which shape the physiological response to the molecular signals, is often unknown or not completely understood. Combining genome-wide experiments with in silico approaches opens the way to a more systematic comprehension of the molecular mechanisms of transcription regulation. Diverse bioinformatics tools have been developed to help unravel these mechanisms, by handling and processing data at different stages: from data collection and storage to the identification of molecular targets and from the detection of DNA motif signatures in the regulatory sequences of functionally related genes to the identification of relevant regulatory networks. Moreover, the large amount of genome-wide scale data recently produced has attracted professionals from diverse backgrounds to this cutting-edge realm of molecular biology. This mini-review is intended as an orientation for multidisciplinary professionals, introducing a streamlined workflow in gene transcription regulation with emphasis on sequence analysis. It provides an outlook on tools and methods, selected from a host of bioinformatics resources available today. It has been designed for the benefit of students, investigators, and professionals who seek a coherent yet quick introduction to in silico approaches to analyzing regulation of gene transcription in the post-genomic era.

Cancer cell growth and survival as a system-level property sustained by enhanced glycolysis and mitochondrial metabolic remodeling

Systems Biology holds that complex cellular functions are generated as system-level properties endowed with robustness, each involving large networks of molecular determinants, generally identified by “omics” analyses. In this paper we describe four basic cancer cell properties that can easily be investigated in vitro: enhanced proliferation, evasion from apoptosis, genomic instability, and inability to undergo oncogene-induced senescence. Focusing our analysis on a K-ras dependent transformation system, we show that enhanced proliferation and evasion from apoptosis are closely linked, and present findings that indicate how a large metabolic remodeling sustains the enhanced growth ability. Network analysis of transcriptional profiling gives the first indication on this remodeling, further supported by biochemical investigations and metabolic flux analysis (MFA). Enhanced glycolysis, down-regulation of TCA cycle, decoupling of glucose and glutamine utilization, with increased reductive carboxylation of glutamine, so to yield a sustained production of growth building blocks and glutathione, are the hallmarks of enhanced proliferation. Low glucose availability specifically induces cell death in K-ras transformed cells, while PKA activation reverts this effect, possibly through at least two mitochondrial targets. The central role of mitochondria in determining the two investigated cancer cell properties is finally discussed. Taken together the findings reported herein indicate that a system-level property is sustained by a cascade of interconnected biochemical pathways that behave differently in normal and in transformed cells.

Design of a framework for modeling, integration and simulation of physiological models

Multiscale modeling and integration of physiological models carry challenges due to the complex nature of physiological processes. High coupling within and among scales present a significant challenge in constructing and integrating multiscale physiological models. In order to deal with such challenges in a systematic way, there is a significant need for an information technology framework together with related analytical and computational tools that will facilitate integration of models and simulations of complex biological systems. Physiological Model Simulation, Integration and Modeling Framework (Phy-SIM) is an information technology framework providing the tools to facilitate development, integration and simulation of integrated models of human physiology. Phy-SIM brings software level solutions to the challenges raised by the complex nature of physiological systems. The aim of Phy-SIM, and this paper is to lay some foundation with the new approaches such as information flow and modular representation of the physiological models. The ultimate goal is to enhance the development of both the models and the integration approaches of multiscale physiological processes and thus this paper focuses on the design approaches that would achieve such a goal.

Computational modelling of maternal interactions with spermatozoa: potentials and prospects

Understanding the complex interactions between gametes, embryos and the maternal tract is required knowledge for combating infertility and developing new methods of contraception. Here we present some main aspects of spermatozoa interactions with the mammalian oviduct before fertilisation and discuss how computational modelling can be used as an invaluable aid to experimental investigation in this field. A complete predictive computational model of gamete and embryo interactions with the female reproductive tract is a long way off. However, the enormity of this task should not discourage us from working towards it. Computational modelling allows us to investigate aspects of maternal communication with gametes and embryos, which are financially, ethically or practically difficult to look at experimentally. In silico models of maternal communication with gametes and embryos can be used as tools to complement in vivo experiments, in the same way as in vitro and in situ models.

A computational model to predict the effects of class I anti-arrhythmic drugs on ventricular rhythms

A long-sought, and thus far elusive, goal has been to develop drugs to manage diseases of excitability. One such disease that affects millions each year is cardiac arrhythmia, which occurs when electrical impulses in the heart become disordered, sometimes causing sudden death. Pharmacological management of cardiac arrhythmia has failed because it is not possible to predict how drugs that target cardiac ion channels, and have intrinsically complex dynamic interactions with ion channels, will alter the emergent electrical behavior generated in the heart. Here, we applied a computational model, which was informed and validated by experimental data, that defined key measurable parameters necessary to simulate the interaction kinetics of the anti-arrhythmic drugs flecainide and lidocaine with cardiac sodium channels. We then used the model to predict the effects of these drugs on normal human ventricular cellular and tissue electrical activity in the setting of a common arrhythmia trigger, spontaneous ventricular ectopy. The model forecasts the clinically relevant concentrations at which flecainide and lidocaine exacerbate, rather than ameliorate, arrhythmia. Experiments in rabbit hearts and simulations in human ventricles based on magnetic resonance images validated the model predictions. This computational framework initiates the first steps toward development of a virtual drug-screening system that models drug-channel interactions and predicts the effects of drugs on emergent electrical activity in the heart.

Genotype-Phenotype Map Characteristics of an In silico Heart Cell

Understanding the causal chain from genotypic to phenotypic variation is a tremendous challenge with huge implications for personalized medicine. Here we argue that linking computational physiology to genetic concepts, methodology, and data provides a new framework for this endeavor. We exemplify this causally cohesive genotype–phenotype (cGP) modeling approach using a detailed mathematical model of a heart cell. In silico genetic variation is mapped to parametric variation, which propagates through the physiological model to generate multivariate phenotypes for the action potential and calcium transient under regular pacing, and ion currents under voltage clamping. The resulting genotype-to-phenotype map is characterized using standard quantitative genetic methods and novel applications of high-dimensional data analysis. These analyses reveal many well-known genetic phenomena like intralocus dominance, interlocus epistasis, and varying degrees of phenotypic correlation. In particular, we observe penetrance features such as the masking/release of genetic variation, so that without any change in the regulatory anatomy of the model, traits may appear monogenic, oligogenic, or polygenic depending on which genotypic variation is actually present in the data. The results suggest that a cGP modeling approach may pave the way for a computational physiological genomics capable of generating biological insight about the genotype–phenotype relation in ways that statistical-genetic approaches cannot.

Modeling formalisms in Systems Biology

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.

Inferring network dynamics and neuron properties from population recordings

Understanding the computational capabilities of the nervous system means to “identify” its emergent multiscale dynamics. For this purpose, we propose a novel model-driven identification procedure and apply it to sparsely connected populations of excitatory integrate-and-fire neurons with spike frequency adaptation (SFA). Our method does not characterize the system from its microscopic elements in a bottom-up fashion, and does not resort to any linearization. We investigate networks as a whole, inferring their properties from the response dynamics of the instantaneous discharge rate to brief and aspecific supra-threshold stimulations. While several available methods assume generic expressions for the system as a black box, we adopt a mean-field theory for the evolution of the network transparently parameterized by identified elements (such as dynamic timescales), which are in turn non-trivially related to single-neuron properties. In particular, from the elicited transient responses, the input–output gain function of the neurons in the network is extracted and direct links to the microscopic level are made available: indeed, we show how to extract the decay time constant of the SFA, the absolute refractory period and the average synaptic efficacy. In addition and contrary to previous attempts, our method captures the system dynamics across bifurcations separating qualitatively different dynamical regimes. The robustness and the generality of the methodology is tested on controlled simulations, reporting a good agreement between theoretically expected and identified values. The assumptions behind the underlying theoretical framework make the method readily applicable to biological preparations like cultured neuron networks and in vitro brain slices.

Cell growth and cell cycle in Saccharomyces cerevisiae: basic regulatory design and protein-protein interaction network

In this review we summarize the major connections between cell growth and cell cycle in the model eukaryote Saccharomyces cerevisiae. In S. cerevisiae regulation of cell cycle progression is achieved predominantly during a narrow interval in the late G1 phase known as START (Pringle and Hartwell, 1981). At START a yeast cell integrates environmental and internal signals (such as nutrient availability, presence of pheromone, attainment of a critical size, status of the metabolic machinery) and decides whether to enter a new cell cycle or to undertake an alternative developmental program. Several signaling pathways, that act to connect the nutritional status to cellular actions, are briefly outlined. A Growth & Cycle interaction network has been manually curated. More than one fifth of the edges within the Growth & Cycle network connect Growth and Cycle proteins, indicating a strong interconnection between the processes of cell growth and cell cycle. The backbone of the Growth & Cycle network is composed of middle-degree nodes suggesting that it shares some properties with HOT networks. The development of multi-scale modeling and simulation analysis will help to elucidate relevant central features of growth and cycle as well as to identify their system-level properties. Confident collaborative efforts involving different expertises will allow to construct consensus, integrated models effectively linking the processes of cell growth and cell cycle, ultimately contributing to shed more light also on diseases in which an altered proliferation ability is observed, such as cancer.

Could there be a Synthesis between Western and Oriental Medicine, and with Sasang Constitutional Medicine in Particular?

Attitudes towards oriental medicine are changing for two major reasons. The first is that many patients, even in the West, are choosing to use its practitioners and methods. The second is that the rise of Systems Biology may offer a better basis for dialogue, and even for synthesis, between the oriental and Western traditions. However, a lot of work is needed to clear the way for such dialogue and synthesis. Much of this work should be devoted to clarifying the meanings of the terms used, and the framework of theory and practice within which oriental methods operate. But it is also necessary for Systems Biology itself to mature as a discipline, particularly at the higher levels of biological organization since it is at these levels that oriental medicine derives its ideas and practice. Higher level Systems Biology could be a basis for interpretation of the Korean version of oriental medicine: Sasang constitutional medicine since it seeks patient specific analysis and treatment, and the mathematical methods of systems biology could be used to analyze the central concept of balance in Sasang.

Experiment-model interaction for analysis of epicardial activation during human ventricular fibrillation with global myocardial ischaemia

We describe a combined experiment-modelling framework to investigate the effects of ischaemia on the organisation of ventricular fibrillation in the human heart. In a series of experimental studies epicardial activity was recorded from 10 patients undergoing routine cardiac surgery. Ventricular fibrillation was induced by burst pacing, and recording continued during 2.5 min of global cardiac ischaemia followed by 30 s of coronary reflow. Modelling used a 2D description of human ventricular tissue. Global cardiac ischaemia was simulated by (i) decreased intracellular ATP concentration and subsequent activation of an ATP sensitive K⁺ current, (ii) elevated extracellular K⁺ concentration, and (iii) acidosis resulting in reduced magnitude of the L-type Ca²⁺ current I(Ca,L). Simulated ischaemia acted to shorten action potential duration, reduce conduction velocity, increase effective refractory period, and flatten restitution. In the model, these effects resulted in slower re-entrant activity that was qualitatively consistent with our observations in the human heart. However, the flattening of restitution also resulted in the collapse of many re-entrant waves to several stable re-entrant waves, which was different to the overall trend we observed in the experimental data. These findings highlight a potential role for other factors, such as structural or functional heterogeneity in sustaining wavebreak during human ventricular fibrillation with global myocardial ischaemia.

Successes and failures in modeling heart cell electrophysiology

Mathematical models of the electrical activity of the heart using equations for protein ion channels and other transporters began with the Noble 1962 model. These models then developed over a period of about 50 years. Cell types in all regions have been modeled and now are available for download from the CellML website (www.cellml.org). Simulation is a necessary tool of analysis in attempting to understand biological complexity. We often learn as much from the failures as from the successes of mathematical models. It is the iterative interaction between experiment and simulation that is important.

Novel therapies in childhood heart failure: today and tomorrow

Heart failure is an important cause of morbidity and mortality in individuals of all ages. The many-faceted nature of the clinical heart failure syndrome has historically frustrated attempts to develop an overarching explanative theory. However, much useful information has been gained by basic and clinical investigation, even though a comprehensive understanding of heart failure has been elusive. Heart failure is a growing problem, in both adult and pediatric populations, for which standard medical therapy, as of 2010, can have positive effects, but these are usually limited and progressively diminish with time in most patients. If we want curative or near-curative therapy that will return patients to a normal state of health at a feasible cost, much better diagnostic and therapeutic technologies need to be developed. This review addresses the vexing group of heart failure etiologies that include cardiomyopathies and other ventricular dysfunctions of various types, for which current therapy is only modestly effective. Although there are many unique aspects to heart failure in patients with pediatric and congenital heart disease, many of the innovative approaches that are being developed for the care of adults with heart failure will be applicable to heart failure in childhood.

Network inference from time-dependent Omics data

We provide a commented overview of the available databases for the systematic collection of pathway information and biological models essential for the interpretation of Omics data. Then, we present both the state of the art and the future challenges of network inference, a research area dealing with the deduction of reaction mechanisms from experimental Omics data. This approach represents one of the most challenging instances for making use of the huge amount of information gathered in the Omics era.

Network modelling of gene regulation

Gene regulatory network (GRN) modelling has gained increasing attention in the past decade. Many computational modelling techniques have been proposed to facilitate the inference and analysis of GRN. However, there is often confusion about the aim of GRN modelling, and how a gene network model can be fully utilised as a tool for systems biology. The aim of the present article is to provide an overview of this rapidly expanding subject. In particular, we review some fundamental concepts of systems biology and discuss the role of network modelling in understanding complex biological systems. Several commonly used network modelling paradigms are surveyed with emphasis on their practical use in systems biology research.

Cloud computing and validation of expandable in silico livers

BACKGROUND

In Silico Livers (ISLs) are works in progress. They are used to challenge multilevel, multi-attribute, mechanistic hypotheses about the hepatic disposition of xenobiotics coupled with hepatic responses. To enhance ISL-to-liver mappings, we added discrete time metabolism, biliary elimination, and bolus dosing features to a previously validated ISL and initiated re-validated experiments that required scaling experiments to use more simulated lobules than previously, more than could be achieved using the local cluster technology. Rather than dramatically increasing the size of our local cluster we undertook the re-validation experiments using the Amazon EC2 cloud platform. So doing required demonstrating the efficacy of scaling a simulation to use more cluster nodes and assessing the scientific equivalence of local cluster validation experiments with those executed using the cloud platform.

RESULTS

The local cluster technology was duplicated in the Amazon EC2 cloud platform. Synthetic modeling protocols were followed to identify a successful parameterization. Experiment sample sizes (number of simulated lobules) on both platforms were 49, 70, 84, and 152 (cloud only). Experimental indistinguishability was demonstrated for ISL outflow profiles of diltiazem using both platforms for experiments consisting of 84 or more samples. The process was analogous to demonstration of results equivalency from two different wet-labs.

CONCLUSIONS

The results provide additional evidence that disposition simulations using ISLs can cover the behavior space of liver experiments in distinct experimental contexts (there is in silico-to-wet-lab phenotype similarity). The scientific value of experimenting with multiscale biomedical models has been limited to research groups with access to computer clusters. The availability of cloud technology coupled with the evidence of scientific equivalency has lowered the barrier and will greatly facilitate model sharing as well as provide straightforward tools for scaling simulations to encompass greater detail with no extra investment in hardware.

Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson's, Huntington's, Alzheimer's, prions, bactericides, chemical toxicology and others as examples

Exposure to a variety of toxins and/or infectious agents leads to disease, degeneration and death, often characterised by circumstances in which cells or tissues do not merely die and cease to function but may be more or less entirely obliterated. It is then legitimate to ask the question as to whether, despite the many kinds of agent involved, there may be at least some unifying mechanisms of such cell death and destruction. I summarise the evidence that in a great many cases, one underlying mechanism, providing major stresses of this type, entails continuing and autocatalytic production (based on positive feedback mechanisms) of hydroxyl radicals via Fenton chemistry involving poorly liganded iron, leading to cell death via apoptosis (probably including via pathways induced by changes in the NF-κB system). While every pathway is in some sense connected to every other one, I highlight the literature evidence suggesting that the degenerative effects of many diseases and toxicological insults converge on iron dysregulation. This highlights specifically the role of iron metabolism, and the detailed speciation of iron, in chemical and other toxicology, and has significant implications for the use of iron chelating substances (probably in partnership with appropriate anti-oxidants) as nutritional or therapeutic agents in inhibiting both the progression of these mainly degenerative diseases and the sequelae of both chronic and acute toxin exposure. The complexity of biochemical networks, especially those involving autocatalytic behaviour and positive feedbacks, means that multiple interventions (e.g. of iron chelators plus antioxidants) are likely to prove most effective. A variety of systems biology approaches, that I summarise, can predict both the mechanisms involved in these cell death pathways and the optimal sites of action for nutritional or pharmacological interventions.

Predicting metabolic adaptation, body weight change, and energy intake in humans

Complex interactions between carbohydrate, fat, and protein metabolism underlie the body's remarkable ability to adapt to a variety of diets. But any imbalances between the intake and utilization rates of these macronutrients will result in changes in body weight and composition. Here, I present the first computational model that simulates how diet perturbations result in adaptations of fuel selection and energy expenditure that predict body weight and composition changes in both obese and nonobese men and women. No model parameters were adjusted to fit these data other than the initial conditions for each subject group (e.g., initial body weight and body fat mass). The model provides the first realistic simulations of how diet perturbations result in adaptations of whole body energy expenditure, fuel selection, and various metabolic fluxes that ultimately give rise to body weight change. The validated model was used to estimate free-living energy intake during a long-term weight loss intervention, a variable that has never previously been measured accurately.

Reduced excitability in the dentate gyrus network of betaIV-spectrin mutant mice in vivo

The submembrane cytoskeletal meshwork of the axon contains the scaffolding protein betaIV-spectrin. It provides mechanical support for the axon and anchors membrane proteins. Quivering (qv(3j)) mice lack functional betaIV-spectrin and have reduced voltage-gated sodium channel (VGSC) immunoreactivity at the axon initial segment and nodes of Ranvier. Because VGSCs are critically involved in action potential generation and conduction, we hypothesized that qv(3j) mice should also show functional deficits at the network level. To test this hypothesis, we investigated granule cell function in the dentate gyrus of anesthetized qv(3j) mice after electrical stimulation of the perforant path in vivo. This revealed an impaired input-output relationship between stimulus intensity and granule cell population spikes and an enhanced paired-pulse inhibition of population spikes, indicating a reduced ability of granule cells to generate action potentials and decreased network excitability. In contrast, the input-output curve for evoked field excitatory postsynaptic potentials (fEPSPs) and paired-pulse facilitation of fEPSPs were unchanged, suggesting normal excitatory synaptic transmission at perforant path-granule cell synapses in qv(3j) mutants. To corroborate our findings, we analyzed the influence of VGSC density reduction on dentate network activity using an established computational model of the dentate gyrus network. This in silico approach confirmed that the loss of VGSCs is sufficient to explain the electrophysiological changes observed in qv(3j) mice. Taken together, our findings demonstrate that betaIV-spectrin is required for normal granule cell firing and for physiological levels of network excitability in the mouse dentate gyrus in vivo.

The Virtual Kidney: an eScience interface and Grid portal

The Virtual Kidney uses a web interface and distributed computing to provide experimental scientists and analysts with access to computational simulations and knowledge databases hosted in geographically separated laboratories. Users can explore a variety of complex models without requiring the specific programming environment in which applications have been developed. This initiative exploits high-bandwidth communication networks for collaborative research and for shared access to knowledge resources. The Virtual Kidney has been developed within a specialist community of renal scientists but is transferable to other areas of research requiring interaction between published literature and databases, theoretical models and simulations and the formulation of effective experimental designs. A web-based three-dimensional interface provides access to experimental data, a parameter database and mathematical models. A multi-scale kidney reconstruction includes blood vessels and serially sectioned nephrons. Selection of structures provides links to the database, returning parameter values and extracts from the literature. Models are run locally or remotely with a Grid resource broker managing scheduling, monitoring and visualization of simulation results and application, credential and resource allocation. Simulation results are viewed graphically or as scaled colour gradients on the Virtual Kidney structures, allowing visual and quantitative appreciation of the effects of simulated parameter changes.