Computational Models Of Biological Systems Research Articles

Calibration methods to fit parameters within complex biological models.

Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.

Advancements in computational modelling of biological systems: seventh annual SysMod meeting

Abstract Summary The Computational Modelling of Systems Biology (SysMod) Community of Special Interest (COSI) convenes annually at the Intelligent Systems for Molecular Biology (ISMB) conference to facilitate knowledge dissemination and exchange of research findings on systems modelling from interdisciplinary domains. The SysMod meeting 2022 was held in a hybrid mode in Madison, Wisconsin, spanning a 1-day duration centred on modelling techniques, applications, and single-cell technology implementations. The meeting showcased innovative approaches to modelling biological systems using cell-specific and multiscale modelling, multiomics data integration, and novel tools to develop systems models using single-cell and multiomics technology. The meeting also recognized outstanding research by awarding the three best posters. This report summarizes the key highlights and outcomes of the meeting. Availability and implementation All resources and further information are freely accessible at https://sysmod.info.

Computational modeling of DLBCL predicts response to BH3-mimetics

In healthy cells, pro- and anti-apoptotic BCL2 family and BH3-only proteins are expressed in a delicate equilibrium. In contrast, this homeostasis is frequently perturbed in cancer cells due to the overexpression of anti-apoptotic BCL2 family proteins. Variability in the expression and sequestration of these proteins in Diffuse Large B cell Lymphoma (DLBCL) likely contributes to variability in response to BH3-mimetics. Successful deployment of BH3-mimetics in DLBCL requires reliable predictions of which lymphoma cells will respond. Here we show that a computational systems biology approach enables accurate prediction of the sensitivity of DLBCL cells to BH3-mimetics. We found that fractional killing of DLBCL, can be explained by cell-to-cell variability in the molecular abundances of signaling proteins. Importantly, by combining protein interaction data with a knowledge of genetic lesions in DLBCL cells, our in silico models accurately predict in vitro response to BH3-mimetics. Furthermore, through virtual DLBCL cells we predict synergistic combinations of BH3-mimetics, which we then experimentally validated. These results show that computational systems biology models of apoptotic signaling, when constrained by experimental data, can facilitate the rational assignment of efficacious targeted inhibitors in B cell malignancies, paving the way for development of more personalized approaches to treatment.

Injury Biomechanics Evaluation of a Driver with Disabilities during a Road Accident-A Numerical Approach.

Numerical methods are often a robust way to predict how external mechanical loads affect individual biological structures. Computational models of biological systems have been developed over the years, reaching high levels of detail, complexity, and precision. In this study, two cases were analysed, differing in the airbag operation; in the first, the airbag was normally activated, and in the second case, the airbag was disabled. We analysed a model of a disabled person without a left leg who steers a vehicle using a specialized knob on the steering wheel. In both cases, a head-on collision between a car moving at an initial speed of 50 km/h and a rigid obstacle was analysed. We concluded that the activated airbag for a person with disabilities reduces the effects of asymmetries in the positioning of the belts and body support points. Moreover, all the biomechanical parameters, analysed on the 50th percentile dummy, i.e., HIC, seat belt contact force and neck injury criterion (Nij) support the use of an airbag. The resulting accelerations, measured in the head of the dummy, were induced into a finite element head model (YEAHM) to kinematically drive the head and simulate both accidents, with and without the airbag. In the latter, the subsequent head injury prediction revealed a form of contrecoup injury, more specifically cerebral contusion based on the intracranial pressure levels that were achieved. Therefore, based on the in-depth investigation, a frontal airbag can significantly lower the possibility of injuries for disabled drivers, including cerebral contusions.

From Microphone to Phoneme: An End-to-End Computational Neural Model for Predicting Speech Perception With Cochlear Implants.

Advances in computational models of biological systems and artificial neural networks enable rapid virtual prototyping of neuroprostheses, accelerating innovation in the field. Here, we present an end-to-end computational model for predicting speech perception with cochlear implants (CI), the most widely-used neuroprosthesis. The model integrates CI signal processing, a finite element model of the electrically-stimulated cochlea, and an auditory nerve model to predict neural responses to speech stimuli. An automatic speech recognition neural network is then used to extract phoneme-level speech perception from these neural response patterns. Compared to human CI listener data, the model predicts similar patterns of speech perception and misperception, captures between-phoneme differences in perceptibility, and replicates effects of stimulation parameters and noise on speech recognition. Information transmission analysis at different stages along the CI processing chain indicates that the bottleneck of information flow occurs at the electrode-neural interface, corroborating studies in CI listeners. An end-to-end model of CI speech perception replicated phoneme-level CI speech perception patterns, and was used to quantify information degradation through the CI processing chain. This type of model shows great promise for developing and optimizing new and existing neuroprostheses.

Computational modelling in health and disease: highlights of the 6th annual SysMod meeting

Abstract Summary The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod) brings together interdisciplinary scientists interested in combining data-driven computational modelling, multi-scale mechanistic frameworks, large-scale -omics data and bioinformatics. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, a meeting for computer scientists, biologists, mathematicians, engineers and computational and systems biologists. The 2021 SysMod meeting was conducted virtually due to the ongoing COVID-19 pandemic (coronavirus disease 2019). During the 2-day meeting, the development of computational tools, approaches and predictive models was discussed, along with their application to biological systems, emphasizing disease mechanisms. This report summarizes the meeting. Availability and implementation All resources and further information are freely accessible at https://sysmod.info.

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

Computational Methods to Identify Cell-Fate Determinants, Identity Transcription Factors, and Niche-Induced Signaling Pathways for Stem Cell Research.

The large-scale development of high-throughput sequencing technologies has not only allowed the generation of reliable omics data related to various regulatory layers but also the development of novel computational models in the field of stem cell research. These computational approaches have enabled the disentangling of a complex interplay between these interrelated layers of regulation by interpreting large quantities of biomedical data in a systematic way. In the context of stem cell research, network modeling of complex gene-gene interactions has been successfully used for understanding the mechanisms underlying stem cell differentiation and cellular conversion. Notably, it has proven helpful for predicting cell-fate determinants and signaling molecules controlling such processes. This chapter will provide an overview of various computational approaches that rely on single-cell and/or bulk RNA sequencing data for elucidating the molecular underpinnings of cell subpopulation identities, lineage specification, and the process of cell-fate decisions. Furthermore, we discuss how these computational methods provide the right framework for computational modeling of biological systems in order to address long-standing challenges in the stem cell field by guiding experimental efforts in stem cell research and regenerative medicine.

SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems.

Computational models of biological systems can exploit a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community, as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings and a YouTube channel with talks and lectures of interest for the modelling community. For more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has inspired and supported the SysMod community. Its members get progressively more involved and actively contribute to the annual COSI meeting and several related community workshops and meetings, focusing on specific topics, including particular techniques for computational modelling or standardisation efforts.

Systems approaches meet biology and physiology: why do we need yet another journal?

Modern biological science in the past 50 years has made a spectacular journey providing us fascinating insights into the nature of living organisms and leading to accumulation of incredible amount of information. Nowadays, we observe introduction of the new systems approaches that allow us to supplement the qualitative nature of the life sciences by quantitative and mechanism-driven analysis. More and more scientists are becoming attracted by the unique opportunities that are provided by the novel synthetic approaches, encompassed by the systems biology, systems physiology and systems pharmacology in various fields. Right now, it is not simply mathematical or computational modelling of biological systems: a wide range of tools, including various approaches from big data field, progressive statistical methods, omics and others, are used to get insight into the mechanism of health and disease.

Mathematical and computational modeling of biological systems: advances and perspectives

<abstract> <p>The recent developments in the fields of mathematics and computer sciences have allowed a more accurate description of the dynamics of some biological systems. On the one hand new mathematical frameworks have been proposed and employed in order to gain a complete description of a biological system thus requiring the definition of complicated mathematical structures; on the other hand computational models have been proposed in order to give both a numerical solution of a mathematical model and to derive computation models based on cellular automata and agents. Experimental methods are developed and employed for a quantitative validation of the modeling approaches. This editorial article introduces the topic of this special issue which is devoted to the recent advances and future perspectives of the mathematical and computational frameworks proposed in biosciences.</p> </abstract>

Differential Drug Target Selection in Blood Coagulation: What can we get from Computational Systems Biology Models?

Discovery and selection of the potential targets are some of the important issues in pharmacology. Even when all the reactions and the proteins in a biological network are known, how does one choose the optimal target? Here, we review and discuss the application of the computational methods to address this problem using the blood coagulation cascade as an example. The problem of correct antithrombotic targeting is critical for this system because, although several anticoagulants are currently available, all of them are associated with bleeding risks. The advantages and the drawbacks of different sensitivity analysis strategies are considered, focusing on the approaches that emphasize: 1) the functional modularity and the multi-tasking nature of this biological network; and 2) the need to normalize hemostasis during the anticoagulation therapy rather than completely suppress it. To illustrate this effect, we show the possibility of the differential regulation of lag time and endogenous thrombin potential in the thrombin generation. These methods allow to identify the elements in the blood coagulation cascade that may serve as the targets for the differential regulation of this system.

Silencing trust: confidence and familiarity in re-engineering knowledge infrastructures

In this paper, we tell the story of efforts currently underway, on diverse fronts, to build digital knowledge repositories (‘knowledge-bases’) to support research in the life sciences. If successful, knowledge bases will be part of a new knowledge infrastructure—capable of facilitating ever-more comprehensive, computational models of biological systems. Such an infrastructure would, however, represent a sea-change in the technological management and manipulation of complex data, inducing a generational shift in how questions are asked and answered and results published and circulated. Integrating such knowledge bases into the daily workflow of the lab thus destabilizes a number of well-established habits which biologists rely on to ensure the quality of the knowledge they produce, evaluate, communicate and exploit. As the story we tell here shows, such destabilization introduces a situation of unfamiliarity, one that carries with it epistemic risks. It should elicit—to use Niklas Luhmann’s terms—the question of trust: a shared recognition that the reliability of research practices is being risked, but that such a risk is worth taking in view of what may be gained. And yet, the problem of trust is being unexpectedly silenced. How that silencing has come about, why it matters, and what might yet be done forms the heart of this paper.

Computational Systems Biology Models for the Identification of Metabolic Vulnerabilities in Multiple Myeloma

Multiple myeloma (MM) is a hematological malignancy characterized by an abnormal accumulation of clonal plasma cells in the bone marrow. MM heterogeneity is associated to the presence of different genomic and transcriptomic profiles that have a clear impact on the prognosis of the disease. Metabolism has been deeply studied in cancer research, unveiling several vulnerabilities in different tumors. However, information regarding the role of metabolism in the pathogenesis of MM has not been explored in detail. Previous studies from our group using Systems Biology approach, explained the essentiality of metabolic gene RRM1 in several MM cell lines. The aim of our current study was to identify metabolic vulnerabilities in MM based on the application of a system biology approach focus on metabolic networks and trascriptomic data from MM patients. Our hypothesis being that changes in the metabolic landscape of MM could be exploited to uncover novel targets for prognosis and treatment in MM patients. We have analyzed the strand specific RNA-seq data from 35 samples from different subpopulations of B cells (Naïve, Centroblast, Centrocyte, Memory, Tonsilar and Bone Marrow Plasma cells (PCs)) and PCs of 37 MM patient samples. Using only the expression of 3287 metabolic genes included in Recon3D, the latest human metabolic reconstruction, we identified metabolic transcription patterns that clearly differentiate the different B cells from MM plasma cells (Figure 1A). MM samples were more similar to the normal PCs than to other B cells, which suggest that they maintain part of the metabolic pattern of the PCs, but in turn showed significant differences in the expression of metabolic genes. Interestingly, differential expression analysis of metabolic genes in MM PCs revealed a decrease in the expression of genes involved in mitochondrial activity and an increase in those that participate in metabolic proliferation, indicating that these alterations could probably play an important role in the development of this tumor. Using our novel systems biology approach, based on Recon3D and transcriptomic profiles, we predicted essential genes and synthetic lethals (involving two or more genes) that were specific for MM patients and not for the rest of the B cell subpopulations. Our approach makes use of the concept of genetic Minimal Cut Sets, previously introduced by our group in cancer research, which defines subsets of genes that if knocked out at the same time, induce a blockage of cellular proliferation. A metabolic vulnerability is found when only one gene is highly expressed in one of these gMCSs. We also analyzed the essentiality of these genes in more than 500 MM patients samples included in CoMMpass project and MM cell lines analyzed in Cancer Cell Line encyclopedia (CCLE). Using this computational strategy, we detected 8 essential genes involved in 42 gMCS specific for MM patients. From those candidates, GNPAT was our most promising target gene, as it was predicted to be essential for more than 40% of MM patients in our group, approximately 10% of patients of CoMMpass and in the majority of MM cell lines (Figure 1B). Validation of GNPAT expression and other 18 genes involved in the GNPAT gMCS was carried out by RT-qPCR showing similar results that were obtained with our RNA-seq data. Interestingly, the expression of the partner genes included in the GNPAT gMCS, such as DGK gene family, could also be indicators of the effectiveness of knocking-out, where their high expression is an indicator of resistance and their low expression an indicator of sensitivity. In conclusion, our findings suggest that our systems biology computational approach, driven by RNA-seq data, identifies metabolic vulnerabilities (defined as essential genes or synthetic lethal genes) providing pairs of new targets (essential gene) and their associated biomarkers (gMCS) in patients with MM. Figure 1: Metabolic genes expression analysis in human humoral immune response and MM patient samples. A) Standardized PCA result using the expression of metabolic genes included in Recon3D, in distinct subpopulations of B cells and MM samples. B) GNPAT gMCS showing the expression (in TPM) of GNPAT and associated 18 genes in MM cell lines. Figure 1 Disclosures Paiva: Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche, and Sanofi; unrestricted grants from Celgene, EngMab, Sanofi, and Takeda; and consultancy for Celgene, Janssen, and Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau. Melnick:Epizyme: Consultancy; KDAc Therapeutics: Membership on an entity's Board of Directors or advisory committees; Constellation Pharmaceuticals: Consultancy; Janssenn: Research Funding. San-Miguel:Amgen, Bristol-Myers Squibb, Celgene, Janssen, MSD, Novartis, Roche, Sanofi, and Takeda: Consultancy, Honoraria.

Epithelial Modelling Platform: A Tool for Investigating Hypothesis through Discovery and Assembly of Computational Models of Epithelial Transport

Scientists often leverage computational models of biological systems to investigate hypotheses which are difficult or prohibitively expensive to achieve experimentally. Such investigations are best achieved by utilizing suitable computational models, reusing existing validated models where possible and creating novel models consistently as needed. This requires tools which enable the discovery and exploration of existing models matched with assistance in constructing and testing new models. Enabling scientists or clinicians to use such a tool by describing their requirements in a manner familiar to themselves greatly improves the accessibility of the tool. We have developed a web-based tool, the Epithelial Modelling Platform, for scientists and clinicians to discover relevant models and then assemble these into a novel model customized for investigating their hypotheses. While our tool specifically focuses on epithelial transport, by utilizing relevant community standards and publicly accessible knowledge repositories, it is extensible to other areas of application. The platform abstracts underlying mathematics of the computational models and provides a visual environment which mimics biological phenomena of an epithelial cell. Beyond the mathematical models, we have implemented a feature to discover existing simulation experiments which match the features of the novel models users create. By executing these simulation experiments with the novel models and comparing to previous model predictions and/or experimental or clinical observations we are able to provide the user with some measure of verification that their model matches, or doesn't match, existing knowledge captured in the various repositories utilized. Support or Funding Information Supported by the Medical Technologies Center of Research Excellence (MedTech CoRE), the Aotearoa Foundation, and the Auckland Bioengineering Institute Model discovery interface to search for models in the Physiome Model Repository (PMR) which are relevant to the query “flux of sodium”. By querying the biological knowledge stored in PMR the Epithelial Modelling Paltform retrieves components from the weinstein, mackenzie, and chang fujita models. Illustrating a similarity between weinstein and mackenzie model. In this case both models have similar compartment labeled with a unique color. An instance of the Epithelial Modelling Platform. It consists of five compartments and three membranes. Biological processes have been represented with various shapes: fluxes with circles; channels with polygons; and diffusive fluxes with a text, a line, and an arrow. On the right, check boxes have been generated for the visualized shapes on the membranes to allow the user to drag and drop. Concentration of the biological description of CellML variables float in the respective compartments. A suggestion window based on the biological description in the Physiome Model Repository when a circle, represented as flux of Na+ in the weinstein model, is moved from apical to basolateral membrane. A model verification instance for testing components of an assembled epithelial model reproduce the same simulation experiment. A model verification instance for testing components of an assembled epithelial model reproduce the same simulation experiment. A spider chart to find similarity of models with respect to an assembled epithelial model. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

What has de novo protein design taught us about protein folding and biophysics?

Recent progress in de novo protein design has led to an explosion of new protein structures, functions and assemblies. In this essay, I consider how the successes and failures in this new area inform our understanding of the proteins in nature and, more generally, the predictive computational modeling of biological systems.

Role of Conformational Flexibility in Monte Carlo Simulations of Many-Protein Systems.

Efficient computational modeling of biological systems characterized by high concentrations of macromolecules often relies on rigid-body Brownian Dynamics or Monte Carlo (MC) simulations. However, the accuracy of rigid-body models is limited by the fixed conformation of the simulated biomolecules. Multi-conformation Monte Carlo (mcMC) simulations of protein solutions incorporate conformational flexibility via a conformational swap trial move within a predetermined library of discrete protein structures, thereby alleviating artifacts arising from the use of a single protein conformation. Here, we investigate the impact of the number of distinct protein structures in the conformational library and the extent of conformational sampling used in its generation on structural observables computed from simulations of hen egg white lysozyme (HEWL), human γD-Crystallin, and bovine γB-Crystallin solutions. We find that the importance of specific protocols for the construction of the protein structure library is strongly dependent on the nature of the simulated system.

Peeking Inside the Black Box: A New Kind of Scientific Visualization

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization (observed in a qualitative study of a systems biology laboratory) that was developed to address just this sort of epistemic opacity. The visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity.

A general framework dedicated to computational morphogenesis Part I – Constitutive equations

In order to understand living organisms, considerable experimental efforts and resources have been devoted to correlate genes and their expressions with cell, tissue, organ and whole organisms’ phenotypes. This data driven approach to knowledge discovery has led to many breakthrough in our understanding of healthy and diseased states, and is paving the way to improve the diagnosis and treatment of diseases. Complementary to this data-driven approach, computational models of biological systems based on first principles have been developed in order to deepen our understanding of the multi-scale dynamics that drives normal and pathological biological functions. In this paper we describe the biological, physical and mathematical concepts that led to the design of a Computational Morphogenesis (CM) platform baptized Generic Modeling and Simulating Platform (GMSP). Its role is to generate realistic 3D multi-scale biological tissues from virtual stem cells and the intended target applications include in virtuo studies of normal and abnormal tissue (re)generation as well as the development of complex diseases such as carcinogenesis. At all space-scales of interest, biological agents interact with each other via biochemical, bioelectrical, and mechanical fields that operate in concert during embryogenesis, growth and adult life. The spatio-temporal dependencies of these fields can be modeled by physics-based constitutive equations that we propose to examine in relation to the landmark biological events that occur during embryogenesis.

Overcoming the lack of kinetic information in biochemical reactions networks

A main aspect in computational modelling of biological systems is the determination of model structure and model parameters. Due to economical and technical reasons, only part of these details are well characterized, while the rest are unknown. To deal with this difficulty, many reverse engineering and parameter estimation methods have been proposed in the literature, however these methods often need an amount of experimental data not always available. In this paper we propose an alternative approach, which overcomes model indetermination solving an Optimization Problem (OP) with an objective function that, similarly to Flux Balance Analysis, is derived from an empirical biological knowledge and does not require large amounts of data. The system behaviour is described by a set of Ordinary Differential Equations (ODE). Model indetermination is resolved selecting time-varying coefficients that maximize/ minimize the objective function at each ODE integration step. Moreover, to facilitate the modelling phase we provide a graphical formalism, based on Petri Nets, which can be used to derive the corresponding ODEs and OP. Finally, the approach is illustrated on a case study focused on cancer metabolism.