Using models of the myocyte for functional interpretation of cardiac proteomic data

Kinetic Mathematical Modeling of Oxidative Phosphorylation in Cardiomyocyte Mitochondria

Oxidative phosphorylation (OXPHOS) is an oxygen-dependent process that consumes catabolized nutrients to produce adenosine triphosphate (ATP) to drive energy-dependent biological processes such as excitation-contraction coupling in cardiomyocytes. In addition to in vivo and in vitro experiments, in silico models are valuable for investigating the underlying mechanisms of OXPHOS and predicting its consequences in both physiological and pathological conditions. Here, we compare several prominent kinetic models of OXPHOS in cardiomyocytes. We examine how their mathematical expressions were derived, how their parameters were obtained, the conditions of their experimental counterparts, and the predictions they generated. We aim to explore the general landscape of energy production mechanisms in cardiomyocytes for future in silico models.

Computational Modeling of Mitochondrial Function from a Systems Biology Perspective

The advent of "big data" in biology (e.g., genomics, proteomics, metabolomics), holding the promise to reveal the nature of the formidable complexity in cellular and organ makeup and function, has highlighted the compelling need for analytical and integrative computational methods to interpret and make sense of the patterns and changes in those complex networks. Computational models need to be built on sound physicochemical mechanistic principles in order to integrate, interpret, and simulate high-throughput experimental data. Energy transduction processes have been traditionally studied with thermodynamic, kinetic, or thermo-kinetic models, with the latter proving superior to understand the control and regulation of mitochondrial energy metabolism and its interactions with cytoplasmic and other cellular compartments. In this work, we survey the methods to be followed to build a computational model of mitochondrial energetics in isolation or integrated into a network of cellular processes. We describe the use of analytical tools such as elementary flux modes, linear optimization of metabolic models, and control analysis, to help refine our grasp of biologically meaningful behaviors and model reliability. The use of these tools should improve the design, building, and interpretation of steady-state behaviors of computational models while assessing validation criteria and paving the way to prediction.

Modeling of action potential generation in NG108-15 cells

In order to explore the possibility of identifying toxins based on their effect on the shape of action potentials, we created a computer model of the action potential generation in NG108-15 cells (a neuroblastoma/glioma hybrid cell line). To generate the experimental data for model validation, voltage-dependent sodium, potassium and high-threshold calcium currents, as well as action potentials, were recorded from NG108-15 cells with conventional whole-cell patch-clamp methods. Based on the classic Hodgkin-Huxley formalism and the linear thermodynamic description of the rate constants, ion-channel parameters were estimated using an automatic fitting method. Utilizing the established parameters, action potentials were generated using the Hodgkin-Huxley formalism and were fitted to the recorded action potentials. To demonstrate the applicability of the method for toxin detection and discrimination, the effect of tetrodotoxin (a sodium channel blocker) and tefluthrin (a pyrethroid that is a sodium channel opener) were studied. The two toxins affected the shape of the action potentials differently, and their respective effects were identified based on the predicted changes in the fitted parameters.

Computational modeling of mitochondrial function

The advent of techniques with the ability to scan massive changes in cellular makeup (genomics, proteomics, etc.) has revealed the compelling need for analytical methods to interpret and make sense of those changes. Computational models built on sound physico-chemical mechanistic basis are unavoidable at the time of integrating, interpreting, and simulating high-throughput experimental data. Another powerful role of computational models is predicting new behavior provided they are adequately validated.Mitochondrial energy transduction has been traditionally studied with thermodynamic models. More recently, kinetic or thermo-kinetic models have been proposed, leading the path toward an understanding of the control and regulation of mitochondrial energy metabolism and its interaction with cytoplasmic and other compartments. In this work, we outline the methods, step-by-step, that should be followed to build a computational model of mitochondrial energetics in isolation or integrated to a network of cellular processes. Depending on the question addressed by the modeler, the methodology explained herein can be applied with different levels of detail, from the mitochondrial energy producing machinery in a network of cellular processes to the dynamics of a single enzyme during its catalytic cycle.

Cardiac models in drug discovery and development: a review

Cardiovascular diseases are among the leading causes of death in the developed world. Developing novel therapies for diseases like heart failure is crucial, but this is hampered by the high attrition rate in drug development. The withdrawal of drugs at the final hurdle of approval is mostly because of their unpredictable effects on normal cardiac rhythm. The advent of cardiac computational modeling in the last 5 decades has aided the understanding of heart function significantly. Recently, these models increasingly have been applied toward designing and understanding therapies for cardiac disease. This article will discuss how cellular models of electrophysiology, cell signaling, and metabolism have been used to investigate pharmacologic therapies for cardiac diseases including arrhythmia, ischemia, and heart failure.

An overview of cardiac systems biology

The cardiac system has been a major target for intensive studies in the multi-scale modeling field for many years. Reproduction of the action potential and the ionic currents of single cardiomyocytes, as well as the construction of a whole organ model is well established. Still, there are major hurdles to overcome in creating a realistic and predictive functional cardiac model due to the lack of a profound understanding of the complex molecular interactions and their outcomes controlling both normal and pathological cardiophysiology. The recent advent of systems biology offers the conceptual and practical frameworks to tackle such biological complexities. This review provides an overview of major themes in the developing field of cardiac systems biology, summarizing some of the high-throughput experiments and strategies used to integrate the datasets, and various types of computational approaches used for developing useful quantitative models capable of predicting complex biological behavior.

Multiscale computational analysis of Xenopus laevis morphogenesis reveals key insights of systems-level behavior

Background

Tissue morphogenesis is a complex process whereby tissue structures self-assemble by the aggregate behaviors of independently acting cells responding to both intracellular and extracellular cues in their environment. During embryonic development, morphogenesis is particularly important for organizing cells into tissues, and although key regulatory events of this process are well studied in isolation, a number of important systems-level questions remain unanswered. This is due, in part, to a lack of integrative tools that enable the coupling of biological phenomena across spatial and temporal scales. Here, we present a new computational framework that integrates intracellular signaling information with multi-cell behaviors in the context of a spatially heterogeneous tissue environment.

Results

We have developed a computational simulation of mesendoderm migration in the Xenopus laevis explant model, which is a well studied biological model of tissue morphogenesis that recapitulates many features of this process during development in humans. The simulation couples, via a JAVA interface, an ordinary differential equation-based mass action kinetics model to compute intracellular Wnt/β-catenin signaling with an agent-based model of mesendoderm migration across a fibronectin extracellular matrix substrate. The emergent cell behaviors in the simulation suggest the following properties of the system: maintaining the integrity of cell-to-cell contact signals is necessary for preventing fractionation of cells as they move, contact with the Fn substrate and the existence of a Fn gradient provides an extracellular feedback loop that governs migration speed, the incorporation of polarity signals is required for cells to migrate in the same direction, and a delicate balance of integrin and cadherin interactions is needed to reproduce experimentally observed migratory behaviors.

Conclusion

Our computational framework couples two different spatial scales in biology: intracellular with multicellular. In our simulation, events at one scale have quantitative and dynamic impact on events at the other scale. This integration enables the testing and identification of key systems-level hypotheses regarding how signaling proteins affect overall tissue-level behavior during morphogenesis in an experimentally verifiable system. Applications of this approach extend to the study of tissue patterning processes that occur during adulthood and disease, such as tumorgenesis and atherogenesis.

Building the power house: recent advances in mitochondrial studies through proteomics and systems biology

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.

Transient outward potassium current, 'Ito', phenotypes in the mammalian left ventricle: underlying molecular, cellular and biophysical mechanisms

At least two functionally distinct transient outward K(+) current (I(to)) phenotypes can exist across the free wall of the left ventricle (LV). Based upon their voltage-dependent kinetics of recovery from inactivation, these two phenotypes are designated 'I(to,fast)' (recovery time constants on the order of tens of milliseconds) and 'I(to,slow)' (recovery time constants on the order of thousands of milliseconds). Depending upon species, either I(to,fast), I(to,slow) or both current phenotypes may be expressed in the LV free wall. The expression gradients of these two I(to) phenotypes across the LV free wall are typically heterogeneous and, depending upon species, may consist of functional phenotypic gradients of both I(to,fast) and I(to,slow) and/or density gradients of either phenotype. We review the present evidence (molecular, biophysical, electrophysiological and pharmacological) for Kv4.2/4.3 alpha subunits underlying LV I(to,fast) and Kv1.4 alpha subunits underlying LV I(to,slow) and speculate upon the potential roles of each of these currents in determining frequency-dependent action potential characteristics of LV subepicardial versus subendocardial myocytes in different species. We also review the possible functional implications of (i) ancillary subunits that regulate Kv1.4 and Kv4.2/4.3 (Kvbeta subunits, DPPs), (ii) KChIP2 isoforms, (iii) spider toxin-mediated block of Kv4.2/4.3 (Heteropoda toxins, phrixotoxins), and (iv) potential mechanisms of modulation of I(to,fast) and I(to,slow) by cellular redox state, [Ca(2)(+)](i) and kinase-mediated phosphorylation. I(to) phenotypic activation and state-dependent gating models and molecular structure-function relationships are also discussed.

Systems modeling: a pathway to drug discovery

Systems modeling is emerging as a valuable tool in therapeutics. This is seen by the increasing use of clinically relevant computational models and a rise in systems biology companies working with the pharmaceutical industry. Systems models have helped understand the effects of pharmacological intervention at receptor, intracellular and intercellular communication stages of cell signaling. For instance, angiogenesis models at the ligand-receptor interaction level have suggested explanations for the failure of therapies for cardiovascular disease. Intracellular models of myeloma signaling have been used to explore alternative drug targets and treatment schedules. Finally, modeling has suggested novel approaches to treating disorders of intercellular communication, such as diabetes. Systems modeling can thus fill an important niche in therapeutics by making drug discovery a faster and more systematic process.