Exploring hypotheses of the actions of TGF-beta 1 in epidermal wound healing using a 3D computational multiscale model of the human epidermis

In vivo and in vitro studies give a paradoxical picture of the actions of the key regulatory factor TGF-beta 1 in epidermal wound healing with it stimulating migration of keratinocytes but also inhibiting their proliferation. To try to reconcile these into an easily visualized 3D model of wound healing amenable for experimentation by cell biologists, a multiscale model of the formation of a 3D skin epithelium was established with TGF-beta 1 literature-derived rule sets and equations embedded within it. At the cellular level, an agent-based bottom-up model that focuses on individual interacting units ( keratinocytes) was used. This was based on literature-derived rules governing keratinocyte behavior and keratinocyte/ECM interactions. The selection of these rule sets is described in detail in this paper. The agent-based model was then linked with a subcellular model of TGF-beta 1 production and its action on keratinocytes simulated with a complex pathway simulator. This multiscale model can be run at a cellular level only or at a combined cellular/subcellular level. It was then initially challenged ( by wounding) to investigate the behavior of keratinocytes in wound healing at the cellular level. To investigate the possible actions of TGF-beta 1, several hypotheses were then explored by deliberately manipulating some of these rule sets at subcellular levels. This exercise readily eliminated some hypotheses and identified a sequence of spatial-temporal actions of TGF-beta 1 for normal successful wound healing in an easy-to-follow 3D model. We suggest this multiscale model offers a valuable, easy-to-visualize aid to our understanding of the actions of this key regulator in wound healing, and provides a model that can now be used to explore pathologies of wound healing

DBSolve Optimum: a software package for kinetic modeling which allows dynamic visualization of simulation results

<p>Abstract</p> <p>Background</p> <p>Systems biology research and applications require creation, validation, extensive usage of mathematical models and visualization of simulation results by end-users. Our goal is to develop novel method for visualization of simulation results and implement it in simulation software package equipped with the sophisticated mathematical and computational techniques for model development, verification and parameter fitting.</p> <p>Results</p> <p>We present mathematical simulation workbench DBSolve Optimum which is significantly improved and extended successor of well known simulation software DBSolve5. Concept of "dynamic visualization" of simulation results has been developed and implemented in DBSolve Optimum. In framework of the concept graphical objects representing metabolite concentrations and reactions change their volume and shape in accordance to simulation results. This technique is applied to visualize both kinetic response of the model and dependence of its steady state on parameter. The use of the dynamic visualization is illustrated with kinetic model of the Krebs cycle.</p> <p>Conclusion</p> <p>DBSolve Optimum is a user friendly simulation software package that enables to simplify the construction, verification, analysis and visualization of kinetic models. Dynamic visualization tool implemented in the software allows user to animate simulation results and, thereby, present them in more comprehensible mode. DBSolve Optimum and built-in dynamic visualization module is free for both academic and commercial use. It can be downloaded directly from <url>http://www.insysbio.ru</url>.</p

Dynamic simulations on the mitochondrial fatty acid Beta-oxidation network

<p>Abstract</p> <p>Background</p> <p>The oxidation of fatty acids in mitochondria plays an important role in energy metabolism and genetic disorders of this pathway may cause metabolic diseases. Enzyme deficiencies can block the metabolism at defined reactions in the mitochondrion and lead to accumulation of specific substrates causing severe clinical manifestations. Ten of the disorders directly affecting mitochondrial fatty acid oxidation have been well-defined, implicating episodic hypoketotic hypoglycemia provoked by catabolic stress, multiple organ failure, muscle weakness, or hypertrophic cardiomyopathy. Additionally, syndromes of severe maternal illness (HELLP syndrome and AFLP) have been associated with pregnancies carrying a fetus affected by fatty acid oxidation deficiencies. However, little is known about fatty acids kinetics, especially during fasting or exercise when the demand for fatty acid oxidation is increased (catabolic stress).</p> <p>Results</p> <p>A computational kinetic network of 64 reactions with 91 compounds and 301 parameters was constructed to study dynamic properties of mitochondrial fatty acid β-oxidation. Various deficiencies of acyl-CoA dehydrogenase were simulated and verified with measured concentrations of indicative metabolites of screened newborns in Middle Europe and South Australia. The simulated accumulation of specific acyl-CoAs according to the investigated enzyme deficiencies are in agreement with experimental data and findings in literature. Investigation of the dynamic properties of the fatty acid β-oxidation reveals that the formation of acetyl-CoA – substrate for energy production – is highly impaired within the first hours of fasting corresponding to the rapid progress to coma within 1–2 hours. LCAD deficiency exhibits the highest accumulation of fatty acids along with marked increase of these substrates during catabolic stress and the lowest production rate of acetyl-CoA. These findings might confirm gestational loss to be the explanation that no human cases of LCAD deficiency have been described.</p> <p>Conclusion</p> <p>In summary, this work provides a detailed kinetic model of mitochondrial metabolism with specific focus on fatty acid β-oxidation to simulate and predict the dynamic response of that metabolic network in the context of human disease. Our findings offer insight into the disease process (e.g. rapid progress to coma) and might confirm new explanations (no human cases of LCAD deficiency), which can hardly be obtained from experimental data alone.</p

Genome-Wide Modeling of Transcription Preinitiation Complex Disassembly Mechanisms using ChIP-chip Data

Apparent occupancy levels of proteins bound to DNA in vivo can now be routinely measured on a genomic scale. A challenge in relating these occupancy levels to assembly mechanisms that are defined with biochemically isolated components lies in the veracity of assumptions made regarding the in vivo system. Assumptions regarding behavior of molecules in vivo can neither be proven true nor false, and thus is necessarily subjective. Nevertheless, within those confines, connecting in vivo protein-DNA interaction observations with defined biochemical mechanisms is an important step towards fully defining and understanding assembly/disassembly mechanisms in vivo. To this end, we have developed a computational program PathCom that models in vivo protein-DNA occupancy data as biochemical mechanisms under the assumption that occupancy levels can be related to binding duration and explicitly defined assembly/disassembly reactions. We exemplify the process with the assembly of the general transcription factors (TBP, TFIIB, TFIIE, TFIIF, TFIIH, and RNA polymerase II) at the genes of the budding yeast Saccharomyces. Within the assumption inherent in the system our modeling suggests that TBP occupancy at promoters is rather transient compared to other general factors, despite the importance of TBP in nucleating assembly of the preinitiation complex. PathCom is suitable for modeling any assembly/disassembly pathway, given that all the proteins (or species) come together to form a complex

Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

<p>Abstract</p> <p>Background</p> <p>With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.</p> <p>Results</p> <p>Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.</p> <p>Conclusion</p> <p>SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.</p

Modelling the Role of the Hsp70/Hsp90 System in the Maintenance of Protein Homeostasis

Neurodegeneration is an age-related disorder which is characterised by the accumulation of aggregated protein and neuronal cell death. There are many different neurodegenerative diseases which are classified according to the specific proteins involved and the regions of the brain which are affected. Despite individual differences, there are common mechanisms at the sub-cellular level leading to loss of protein homeostasis. The two central systems in protein homeostasis are the chaperone system, which promotes correct protein folding, and the cellular proteolytic system, which degrades misfolded or damaged proteins. Since these systems and their interactions are very complex, we use mathematical modelling to aid understanding of the processes involved. The model developed in this study focuses on the role of Hsp70 (IPR00103) and Hsp90 (IPR001404) chaperones in preventing both protein aggregation and cell death. Simulations were performed under three different conditions: no stress; transient stress due to an increase in reactive oxygen species; and high stress due to sustained increases in reactive oxygen species. The model predicts that protein homeostasis can be maintained during short periods of stress. However, under long periods of stress, the chaperone system becomes overwhelmed and the probability of cell death pathways being activated increases. Simulations were also run in which cell death mediated by the JNK (P45983) and p38 (Q16539) pathways was inhibited. The model predicts that inhibiting either or both of these pathways may delay cell death but does not stop the aggregation process and that eventually cells die due to aggregated protein inhibiting proteasomal function. This problem can be overcome if the sequestration of aggregated protein into inclusion bodies is enhanced. This model predicts responses to reactive oxygen species-mediated stress that are consistent with currently available experimental data. The model can be used to assess specific interventions to reduce cell death due to impaired protein homeostasis

A model of the PI cycle reveals the regulating roles of lipid-binding proteins and pitfalls of using mosaic biological data

The phosphatidylinositol (PI) cycle is central to eukaryotic cell signaling. Its complexity, due to the number of reactions and lipid and inositol phosphate intermediates involved makes it difficult to analyze experimentally. Computational modelling approaches are seen as a way forward to elucidate complex biological regulatory mechanisms when this cannot be achieved solely through experimental approaches. Whilst mathematical modelling is well established in informing biological systems, many models are often informed by data sourced from multiple unrelated cell types (mosaic data) or from purified enzyme data. In this work, we develop a model of the PI cycle informed by experimental and omics data taken from a single cell type, namely platelets. We were able to make a number of predictions regarding the regulation of PI cycle enzymes, the importance of the number of receptors required for successful GPCR signaling and the importance of lipid- and protein-binding proteins in regulating second messenger outputs. We then consider how pathway behavior differs, when fully informed by data for HeLa cells and show that model predictions remain consistent. However, when informed by mosaic experimental data model predictions greatly vary illustrating the risks of using mosaic datasets from unrelated cell types

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.National Institutes of Health (U.S.) (Grant P50-GM068762)National Institutes of Health (U.S.) (Grant R24-DK090963)United States. Army Research Office (Grant W911NF-09-0001)German Research Foundation (Grant GSC 111

There is more than one way to turn a spherical cellular monolayer inside out: type B embryo inversion in Volvox globator

Höhn S, Hallmann A. There is more than one way to turn a spherical cellular monolayer inside out: type B embryo inversion in Volvox globator. BMC Biology. 2011;9(1): 89.Background: Epithelial folding is a common morphogenetic process during the development of multicellular organisms. In metazoans, the biological and biomechanical processes that underlie such three-dimensional (3D) developmental events are usually complex and difficult to investigate. Spheroidal green algae of the genus Volvox are uniquely suited as model systems for studying the basic principles of epithelial folding. Volvox embryos begin life inside out and then must turn their spherical cell monolayer outside in to achieve their adult configuration; this process is called 'inversion.' There are two fundamentally different sequences of inversion processes in Volvocaceae: type A and type B. Type A inversion is well studied, but not much is known about type B inversion. How does the embryo of a typical type B inverter, V. globator, turn itself inside out? Results: In this study, we investigated the type B inversion of V. globator embryos and focused on the major movement patterns of the cellular monolayer, cell shape changes and changes in the localization of cytoplasmic bridges (CBs) connecting the cells. Isolated intact, sectioned and fragmented embryos were analyzed throughout the inversion process using light microscopy, confocal laser scanning microscopy, scanning electron microscopy and transmission electron microscopy techniques. We generated 3D models of the identified cell shapes, including the localizations of CBs. We show how concerted cell-shape changes and concerted changes in the position of cells relative to the CB system cause cell layer movements and turn the spherical cell monolayer inside out. The type B inversion of V. globator is compared to the type A inversion in V. carteri. Conclusions: Concerted, spatially and temporally coordinated changes in cellular shapes in conjunction with concerted migration of cells relative to the CB system are the causes of type B inversion in V. globator. Despite significant similarities between type A and type B inverters, differences exist in almost all details of the inversion process, suggesting analogous inversion processes that arose through parallel evolution. Based on our results and due to the cellular biomechanical implications of the involved tensile and compressive forces, we developed a global mechanistic scenario that predicts epithelial folding during embryonic inversion in V. globator

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering

<p>Abstract</p> <p>Background</p> <p>Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments.</p> <p>Results</p> <p>We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification.</p> <p>Conclusion</p> <p>We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich-data poor' paradox in Systems Biology.</p