Computational Systems Biology Research Articles

Engaging Students Equitably in Biological Systems Modeling

It has been my goal as an educator to engage students in meaningful and innovative assessments that are hands‐on, engage their creativity and improve their interdisciplinary understanding. At the same time, the engagement need to be equitable as well, to recognize each student for their individuality and creativity. To this end, I created my Pathophysiology course with the objective of teaching students how to model biological systems. The course is a one semester course that is based on homeostasis maintenance in the body and how homeostatic disruptions result in symptoms of pathologies. Students are required to understand how homeostasis is maintained in individual organ systems and encouraged to model their understanding of homeostatic disruptions in ways other than biology, such as computer science and mathematics. This gives them an opportunity to use their creativity as well as apply their learning in other disciplines to create an interdisciplinary model of a biological system. Students have indicated that they appreciate being able to use their creative talents and non‐biological skills to model biological systems. Being able to use their non‐biological skills in a biology elective both encourages their problem solving skills and gives them confidence. Student learning is assessed through pre and post assessments and shows significant improvement in student understanding.Promoting equity in assessments has been challenging. There is a need to recognize the individual skills and learning patterns of each student and allow for this in creating assessments. I decided to replace two written assessments with hands on projects such as the heart model project and the graphic novels project. My purpose was to minimize memorization and promote integrative learning in a fun way. The heart model project replaced the cardiovascular system unit test. A variety of materials are provided to students for making life sized heart models, and instructor approval of student blueprints is required before beginning construction. Extra credit is offered if the model “works”. A second assessment, the graphic novel project, replaced student presentations and were much preferred by the students for the opportunities offered to demonstrate their drawing and creative skills. Every student felt included (as indicated on their surveys) and enjoyed the interdisciplinary integration of modeling in the course, an exercise in logical thinking that is applicable to any other discipline as well.

Endothelial Regulation of Microvascular Growth and Stability by Ang‐Tie and VEGF Signaling Pathways: A Mechanistic Computational Systems Biology Model

The Angiopoietin‐Tie (Ang‐Tie) pathway is a key signaling pathway regulating vascular stability and permeability, and it significantly intersects and crosstalk with the vascular endothelial growth factor (VEGF) signaling pathway, a major signaling pathway regulating angiogenesis and vascular permeability. Disrupted Ang‐Tie and VEGF signaling is linked to vascular dysfunction related to cancer, systemic inflammation, cardiovascular disease, and diabetic macular edema. Ang‐Tie pathway is regulated by various molecular mechanisms, including ligand‐receptor interactions, receptor multimerization, binding of co‐receptor Tie1, inhibition by vascular endothelial protein tyrosine phosphatase (VE‐PTP), receptor extracellular domain shedding, junctional localization, trafficking, and turnover. VEGF pathway is regulated by ligand binding, homo‐ and heterodimerization of VEGF receptors VEGFR1 and VEGFR2, interaction with neuropilin, thrombospondin‐1/CD47, receptor internalization, recycling, and degradation. Ang‐Tie and VEGF signaling pathways also share crosstalk mechanisms and integrative downstream signaling through phosphoinositide 3‐kinases (PI3K)/Akt, sphingosine‐dependent Erk activation, Src sequestration via RhoA/mDia, and calcium cycling, forming a complex reaction network that requires an integrative model to study the signaling outcomes on a systems level. The present study uses a mechanistic computational model of the Ang‐Tie and VEGF signaling pathways, their regulatory mechanisms, crosstalk, and integrative downstream signaling to quantitatively characterize the molecular control of vascular growth, permeability, and stability by endothelial cells. The model captures and reproduces key experimental observations of the Ang‐Tie/VEGF signaling network with detailed molecular mechanisms. The model significantly expands our previous computational models of the Ang‐Tie signaling pathway [1,2] and integrates our systems biology understanding of the VEGF pathway and their crosstalk. The model serves as a platform for quantitatively predicting the signaling outcome of varying ligand concentrations and receptor expressions in physiological and pathological conditions, identifying molecular mechanisms and targets for therapeutic interventions targeting the signaling network, and simulating the effects of drugs and their combinations on the signaling outcome on a network level.

Hebbian plasticity: the elusive missing link at the heart of Alzheimer's disease pathogenesis?

Hebbian plasticity: the elusive missing link at the heart of Alzheimer's disease pathogenesis?

ScSGL: kernelized signed graph learning for single-cell gene regulatory network inference.

Elucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, a characteristic feature of GRNs, which are capable of accounting for both activating and inhibitory relationships in the gene network. They are also incapable of handling high proportion of zero values present in the single cell datasets. To this end, we propose a novel signed GL approach, scSGL, that learns GRNs based on the assumption of smoothness and non-smoothness of gene expressions over activating and inhibitory edges, respectively. scSGL is then extended with kernels to account for non-linearity of co-expression and for effective handling of highly occurring zero values. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. Performance assessment using simulated datasets demonstrates the superior performance of kernelized scSGL over existing state of the art methods in GRN recovery. The performance of scSGL is further investigated using human and mouse embryonic datasets. The scSGL code and analysis scripts are available on https://github.com/Single-Cell-Graph-Learning/scSGL. Supplementary data are available at Bioinformatics online.

BioSANS: A software package for symbolic and numeric biological simulation.

Modeling biochemical systems can provide insights into behaviors that are difficult to observe or understand. It requires software, programming, and understanding of the system to build a model and study it. Softwares exist for systems biology modeling, but most support only certain types of modeling tasks. Desirable features including ease in preparing input, symbolic or analytical computation, parameter estimation, graphical user interface, and systems biology markup language (SBML) support are not seen concurrently in one software package. In this study, we developed a python-based software that supports these features, with both deterministic and stochastic propagations. The software can be used by graphical user interface, command line, or as a python import. We also developed a semi-programmable and intuitively easy topology input method for the biochemical reactions. We tested the software with semantic and stochastic SBML test cases. Tests on symbolic solution and parameter estimation were also included. The software we developed is reliable, well performing, convenient to use, and compliant with most of the SBML tests. So far it is the only systems biology software that supports symbolic, deterministic, and stochastic modeling in one package that also features parameter estimation and SBML support. This work offers a comprehensive set of tools and allows for better availability and accessibility for studying kinetics and dynamics in biochemical systems.

Development of microfluidic chip for entrapping tobacco BY-2 cells.

The tobacco BY-2 cell line has been used widely as a model system in plant cell biology. BY-2 cells are nearly transparent, which facilitates cell imaging using fluorescent markers. As cultured cells are drifted in the medium, therefore, it was difficult to observe them for a long period. Hence, we developed a microfluidic device that traps BY-2 cells and fixes their positions to allow monitoring the physiological activity of cells. The device contains 112 trap zones, with parallel slots connected in series at three levels in the flow channel. BY-2 cells were cultured for 7 days and filtered using a sieve and a cell strainer before use to isolate short cell filaments consisting of only a few cells. The isolated cells were introduced into the flow channel, resulting in entrapment of cell filaments at 25 out of 112 trap zones (22.3%). The cell numbers increased through cell division from 1 to 4 days after trapping with a peak of mitotic index on day 2. Recovery experiments of fluorescent proteins after photobleaching confirmed cell survival and permeability of plasmodesmata. Thus, this microfluidic device and one-dimensional plant cell samples allowed us to observe cell activity in real time under controllable conditions.

Chloroquine and COVID-19-A systems biology model uncovers the drug's detrimental effect on autophagy and explains its failure.

The COVID-19 pandemic caused by SARS-CoV-2 has resulted in an urgent need for identifying potential therapeutic drugs. In the first half of 2020 tropic antimalarial drugs, such as chloroquine (CQ) or hydroxochloroquine (HCQ) were the focus of tremendous public attention. In the initial periods of the pandemic, many scientific results pointed out that CQ/HCQ could be very effective for patients with severe COVID. While CQ and HCQ have successfully been used against several diseases (such as malaria, autoimmune disease and rheumatic illnesses); long term use of these agents are associated with serious adverse effects (i.e. inducing acute kidney injury, among many others) due to their role in blocking autophagy-dependent self-degradation. Recent experimental and clinical trial data also confirmed that there is no sufficient evidence about the efficient usage of CQ/HCQ against COVID-19. By using systems biology techniques, here we show that the cellular effect of CQ/HCQ on autophagy during endoplasmic reticulum (ER) stress or following SARS-CoV-2 infection results in upregulation of ER stress. By presenting a simple mathematical model, we claim that although CQ/HCQ might be able to ameliorate virus infection, the permanent inhibition of autophagy by CQ/HCQ has serious negative effects on the cell. Since CQ/HCQ promotes apoptotic cell death, here we confirm that addition of CQ/HCQ cannot be really effective even in severe cases. Only a transient treatment seemed to be able to avoid apoptotic cell death, but this type of therapy could not limit virus replication in the infected host. The presented theoretical analysis clearly points out the utility and applicability of systems biology modelling to test the cellular effect of a drug targeting key major processes, such as autophagy and apoptosis. Applying these approaches could decrease the cost of pre-clinical studies and facilitate the selection of promising clinical trials in a timely fashion.

Hybrid modelling of biological systems: current progress and future prospects.

Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems.

Efficient quantitative assessment of robot swarms: coverage and targeting Lévy strategies

Biologically inspired strategies have long been adapted to swarm robotic systems, including biased random walks, reaction to chemotactic cues and long-range coordination. In this paper we apply analysis tools developed for modeling biological systems, such as continuum descriptions, to the efficient quantitative characterization of robot swarms. As an illustration, both Brownian and Lévy strategies with a characteristic long-range movement are discussed. As a result we obtain computationally fast methods for the optimization of robot movement laws to achieve a prescribed collective behavior. We show how to compute performance metrics like coverage and hitting times, and illustrate the accuracy and efficiency of our approach for area coverage and search problems. Comparisons between the continuum model and robotic simulations confirm the quantitative agreement and speed up by a factor of over 100 of our approach. Results confirm and quantify the advantage of Lévy strategies over Brownian motion for search and area coverage problems in swarm robotics.

Digital Marine: An online platform for blended learning in a marine experimental biology module, the Schmid Training Course.

For over 20 years, the Schmid Training Course (STC) has offered unique opportunities for marine biology students from European universities to learn about marine model organisms. While the topics of the course have continuously changed over the years with the advent of new research techniques and discoveries, the pedagogical approach has remained largely the same - a combination of lectures, lab practicals, and field excursions. Several life science researchers, who have taught in the STC for many years, sought to bring the course's pedagogical approach into the 21st century, and with the support of Erasmus+ Programme of the European Community funding, the Digital Marine project was developed. Digital Marine began in 2018 as an international partnership between the six research centers from which the STC instructors hail, and its main objective was to introduce a flipped, blended approach to learning and teaching with respect to established and emerging marine biological model systems. The Digital Marine platform, which covers 12 marine model organisms, is now publicly available.

Toxicological Impact of Rare Earth Elements (REEs) on the Reproduction and Development of Aquatic Organisms Using Sea Urchins as Biological Models.

The growing presence of lanthanides in the environment has drawn the attention of the scientific community on their safety and toxicity. The sources of lanthanides in the environment include diagnostic medicine, electronic devices, permanent magnets, etc. Their exponential use and the poor management of waste disposal raise serious concerns about the quality and safety of the ecosystems at a global level. This review focused on the impact of lanthanides in marine organisms on reproductive fitness, fertilization and embryonic development, using the sea urchin as a biological model system. Scientific evidence shows that exposure to lanthanides triggers a wide variety of toxic insults, including reproductive performance, fertilization, redox metabolism, embryogenesis, and regulation of embryonic gene expression. This was thoroughly demonstrated for gadolinium, the most widely used lanthanide in diagnostic medicine, whose uptake in sea urchin embryos occurs in a time- and concentration-dependent manner, correlates with decreased calcium absorption and primarily affects skeletal growth, with incorrect regulation of the skeletal gene regulatory network. The results collected on sea urchin embryos demonstrate a variable sensitivity of the early life stages of different species, highlighting the importance of testing the effects of pollution in different species. The accumulation of lanthanides and their emerging negative effects make risk assessment and consequent legislative intervention on their disposal mandatory.

Symmetries in Dynamic Models of Biological Systems: Mathematical Foundations and Implications

Symmetries are ubiquitous in nature. Almost all organisms have some kind of “symmetry”, meaning that their shape does not change under some geometric transformation. This geometrical concept of symmetry is intuitive and easy to recognize. On the other hand, the behavior of many biological systems over time can be described with ordinary differential equations. These dynamic models may also possess “symmetries”, meaning that the time courses of some variables remain invariant under certain transformations. Unlike the previously mentioned symmetries, the ones present in dynamic models are not geometric, but infinitesimal transformations. These mathematical symmetries can be related to certain features of the system’s dynamic behavior, such as robustness or adaptation capabilities. However, they can also arise from questionable modeling choices, which may lead to non-identifiability and non-observability. This paper provides an overview of the types of symmetries that appear in dynamic models, the mathematical tools available for their analyses, the ways in which they are related to system properties, and the implications for biological modeling.

Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles.

Dynamic behavior of biological systems is commonly represented by non-linear models such as ordinary differential equations. A frequently encountered task in such systems is the estimation of model parameters based on measurement of biochemical compounds. Non-linear models require special techniques to estimate the uncertainty of the obtained model parameters and predictions, e.g. by exploiting the concept of the profile likelihood. Model parameters with significant uncertainty associated with their estimates hinder the interpretation of model results. Informing these model parameters by optimal experimental design minimizes the additional amount of data and therefore resources required in experiments. However, existing techniques of experimental design either require prior parameter distributions in Bayesian approaches or do not adequately deal with the non-linearity of the system in frequentist approaches. For identification of optimal experimental designs, we propose a two-dimensional profile likelihood approach, providing a design criterion which meaningfully represents the expected parameter uncertainty after measuring data for a specified experimental condition. The described approach is implemented into the open source toolbox Data2Dynamics in Matlab. The applicability of the method is demonstrated on an established systems biology model. For this demonstration, available data has been censored to simulate a setting in which parameters are not yet well determined. After determining the optimal experimental condition from the censored ones, a realistic evaluation was possible by re-introducing the censored data point corresponding to the optimal experimental condition. This provided a validation that our method is feasible in real-world applications. The approach applies to, but is not limited to, models in systems biology.

Computational Systems Biology of Alfalfa - Bacterial Blight Host-Pathogen Interactions: Uncovering the Complex Molecular Networks for Developing Durable Disease Resistant Crop.

Medicago sativa (also known as alfalfa), a forage legume, is widely cultivated due to its high yield and high-value hay crop production. Infectious diseases are a major threat to the crops, owing to huge economic losses to the agriculture industry, worldwide. The protein-protein interactions (PPIs) between the pathogens and their hosts play a critical role in understanding the molecular basis of pathogenesis. Pseudomonas syringae pv. syringae ALF3 suppresses the plant’s innate immune response by secreting type III effector proteins into the host cell, causing bacterial stem blight in alfalfa. The alfalfa-P. syringae system has little information available for PPIs. Thus, to understand the infection mechanism, we elucidated the genome-scale host-pathogen interactions (HPIs) between alfalfa and P. syringae using two computational approaches: interolog-based and domain-based method. A total of ∼14 M putative PPIs were predicted between 50,629 alfalfa proteins and 2,932 P. syringae proteins by combining these approaches. Additionally, ∼0.7 M consensus PPIs were also predicted. The functional analysis revealed that P. syringae proteins are highly involved in nucleotide binding activity (GO:0000166), intracellular organelle (GO:0043229), and translation (GO:0006412) while alfalfa proteins are involved in cellular response to chemical stimulus (GO:0070887), oxidoreductase activity (GO:0016614), and Golgi apparatus (GO:0005794). According to subcellular localization predictions, most of the pathogen proteins targeted host proteins within the cytoplasm and nucleus. In addition, we discovered a slew of new virulence effectors in the predicted HPIs. The current research describes an integrated approach for deciphering genome-scale host-pathogen PPIs between alfalfa and P. syringae, allowing the researchers to better understand the pathogen’s infection mechanism and develop pathogen-resistant lines.

Cutting the Gordian knot of heterogeneity: Can integrated systems biology modelling rescue critical care syndromes?

Critical care is perhaps the principal custodian of sepsis and the acute respiratory distress syndrome (ARDS). Both syndromes are beset by multifactorial heterogeneity owing to their respective broad and non-specific clinical diagnostic criteria. Coupled with the heterogeneity of host responses (genetic and acquired) and timing and severity of the insult, it is perhaps unsurprising that critical care has been fertile grounds for negative clinical trials. Even in COVID-19, where aetiology is uniform and sample sizes unwantedly generous, findings of clinical trials for immunomodulatory drugs and antivirals have been uncertain, again alluding to underlying heterogeneity.

A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach.

ObjectivesComputational modeling of biological systems is a powerful tool to clarify diverse processes contributing to cancer. The aim is to clarify the complex biochemical and mechanical interactions between cells, the relevance of intracellular signaling pathways in tumor progression and related events to the cancer treatments, which are largely ignored in previous studies.Materials and MethodsA three‐dimensional multiscale cell‐based model is developed, covering multiple time and spatial scales, including intracellular, cellular, and extracellular processes. The model generates a realistic representation of the processes involved from an implementation of the signaling transduction network.ResultsConsidering a benign tumor development, results are in good agreement with the experimental ones, which identify three different phases in tumor growth. Simulating tumor vascular growth, results predict a highly vascularized tumor morphology in a lobulated form, a consequence of cells' motile behavior. A novel systematic study of chemotherapy intervention, in combination with targeted therapy, is presented to address the capability of the model to evaluate typical clinical protocols. The model also performs a dose comparison study in order to optimize treatment efficacy and surveys the effect of chemotherapy initiation delays and different regimens.ConclusionsResults not only provide detailed insights into tumor progression, but also support suggestions for clinical implementation. This is a major step toward the goal of predicting the effects of not only traditional chemotherapy but also tumor‐targeted therapies.

Cacatoo: building, exploring, and sharing spatially structured models of biological systems

Cacatoo: building, exploring, and sharing spatially structured models of biological systems

The Biological Robots Are Coming! But Note They Have Been Here for ∼3.5 Billion Years

The Biological Robots Are Coming! But Note They Have Been Here for ∼3.5 Billion Years

Sum of Certainties with the Product of Reasons: Neural Network with Fuzzy Aggregators

The paper attempts to bridge the gap between widely accepted models of biological systems based on the Tsetlin automata acting in random environment and traditional artificial neural networks that consist of the McCalloch and Pitts neurons. Using recently developed algebra with uninorm and absorbing norm aggregators, we consider the neurons as extended Tsetlin automata that implement multi-valued not - xor operator applied to the aggregated inputs and internal states, and then construct the network using these neurons. The inputs of the neurons are specified by the synapses that implement multi-valued joined and and or operations. We demonstrate that for favorable (in the sense of learning) states the suggested neurons act similarly to the traditional neurons, while for unfavorable states they immediately change their activity to the reverse one. Such properties of the neurons both results in the correct activity of the network and demonstrates better correspondence with the logics of natural neural networks.

Quantification of Fractional Dynamical Stability of EEG Signals as a Bio-Marker for Cognitive Motor Control

Assessing the stability of biological system models has aided in uncovering a plethora of new insights in genetics, neuroscience, and medicine. In this paper, we focus on analyzing the stability of neurological signals, including electroencephalogram (EEG) signals. Interestingly, spatiotemporal discrete-time linear fractional-order systems (DTLFOS) have been shown to accurately and efficiently represent a variety of neurological and physiological signals. Here, we leverage the conditions for stability of DTLFOS to assess a real-world EEG data set. By analyzing the stability of EEG signals during movement and rest tasks, we provide evidence of the usefulness of the quantification of stability as a bio-marker for cognitive motor control.