Stacey D. Finley

Computational Framework for Predictive Biodegradation

As increasing amounts of anthropogenic chemicals are released into the environment, it is vital to human health and the preservation of ecosystems to evaluate the fate of these chemicals in the environment. It is useful to predict whether a particular compound is biodegradable and if alternate routes can be engineered for compounds already known to be biodegradable. In this work, we describe a computational framework (called BNICE) that can be used for the prediction of novel biodegradation pathways of xenobiotics. The framework was applied to 4‐chlorobiphenyl, phenanthrene, γ‐hexachlorocyclohexane, and 1,2,4‐trichlorobenzene, compounds representing various classes of xenobiotics with known biodegradation routes. BNICE reproduced the proposed biodegradation routes found experimentally, and in addition, it expanded the biodegradation reaction networks through the generation of novel compounds and reactions. The novel reactions involved in the biodegradation of 1,2,4‐trichlorobenzene were studied in depth, where pathway and thermodynamic analyses were performed. This work demonstrates that BNICE can be applied to generate novel pathways to degrade xenobiotic compounds that are thermodynamically feasible alternatives to known biodegradation routes and attractive targets for metabolic engineering. Biotechnol. Bioeng. 2009; 104: 1086–1097.

In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene

BackgroundBioremediation offers a promising pollution treatment method in the reduction and elimination of man-made compounds in the environment. Computational tools to predict novel biodegradation pathways for pollutants allow one to explore the capabilities of microorganisms in cleaning up the environment. However, given the wealth of novel pathways obtained using these prediction methods, it is necessary to evaluate their relative feasibility, particularly within the context of the cellular environment.ResultsWe have utilized a computational framework called BNICE to generate novel biodegradation routes for 1,2,4-trichlorobenzene (1,2,4-TCB) and incorporated the pathways into a metabolic model for Pseudomonas putida. We studied the cellular feasibility of the pathways by applying metabolic flux analysis (MFA) and thermodynamic constraints. We found that the novel pathways generated by BNICE enabled the cell to produce more biomass than the known pathway. Evaluation of the flux distribution profiles revealed that several properties influenced biomass production: 1) reducing power required, 2) reactions required to generate biomass precursors, 3) oxygen utilization, and 4) thermodynamic topology of the pathway. Based on pathway analysis, MFA, and thermodynamic properties, we identified several promising pathways that can be engineered into a host organism to accomplish bioremediation.ConclusionsThis work was aimed at understanding how novel biodegradation pathways influence the existing metabolism of a host organism. We have identified attractive targets for metabolic engineers interested in constructing a microorganism that can be used for bioremediation. Through this work, computational tools are shown to be useful in the design and evaluation of novel xenobiotic biodegradation pathways, identifying cellularly feasible degradation routes.

Effect of Tumor Microenvironment on Tumor VEGF During Anti-VEGF Treatment: Systems Biology Predictions

BACKGROUND Vascular endothelial growth factor (VEGF) is known to be a potent promoter of angiogenesis under both physiological and pathological conditions. Given its role in regulating tumor vascularization, VEGF has been targeted in various cancer treatments, and anti-VEGF therapy has been used clinically for treatment of several types of cancer. Systems biology approaches, particularly computational models, provide insight into the complexity of tumor angiogenesis. These models complement experimental studies and aid in the development of effective therapies targeting angiogenesis. METHODS We developed an experiment-based, molecular-detailed compartment model of VEGF kinetics and transport to investigate the distribution of two major VEGF isoforms (VEGF121 and VEGF165) in the body. The model is applied to predict the dynamics of tumor VEGF and, importantly, to gain insight into how tumor VEGF responds to an intravenous injection of an anti-VEGF agent. RESULTS The model predicts that free VEGF in the tumor interstitium is seven to 13 times higher than plasma VEGF and is predominantly in the form of VEGF121 (>70%), predictions that are validated by experimental data. The model also predicts that tumor VEGF can increase or decrease with anti-VEGF treatment depending on tumor microenvironment, pointing to the importance of personalized medicine. CONCLUSIONS This computational study suggests that the rate of VEGF secretion by tumor cells may serve as a biomarker to predict the patient population that is likely to respond to anti-VEGF treatment. Thus, the model predictions have important clinical relevance and may aid clinicians and clinical researchers seeking interpretation of pharmacokinetic and pharmacodynamic observations and optimization of anti-VEGF therapies.

A systems biology view of blood vessel growth and remodelling

Blood travels throughout the body in an extensive network of vessels – arteries, veins and capillaries. This vascular network is not static, but instead dynamically remodels in response to stimuli from cells in the nearby tissue. In particular, the smallest vessels – arterioles, venules and capillaries – can be extended, expanded or pruned, in response to exercise, ischaemic events, pharmacological interventions, or other physiological and pathophysiological events. In this review, we describe the multi‐step morphogenic process of angiogenesis – the sprouting of new blood vessels – and the stability of vascular networks in vivo. In particular, we review the known interactions between endothelial cells and the various blood cells and plasma components they convey. We describe progress that has been made in applying computational modelling, quantitative biology and high‐throughput experimentation to the angiogenesis process.

Predicting the effects of anti-angiogenic agents targeting specific VEGF isoforms.

Vascular endothelial growth factor (VEGF) is a key mediator of angiogenesis, whose effect on cancer growth and development is well characterized. Alternative splicing of VEGF leads to several different isoforms, which are differentially expressed in various tumor types and have distinct functions in tumor blood vessel formation. Many cancer therapies aim to inhibit angiogenesis by targeting VEGF and preventing intracellular signaling leading to tumor vascularization; however, the effects of targeting specific VEGF isoforms have received little attention in the clinical setting. In this work, we investigate the effects of selectively targeting a single VEGF isoform, as compared with inhibiting all isoforms. We utilize a molecular-detailed whole-body compartment model of VEGF transport and kinetics in the presence of breast tumor. The model includes two major VEGF isoforms, VEGF121 and VEGF165, receptors VEGFR1 and VEGFR2, and co-receptors Neuropilin-1 and Neuropilin-2. We utilize the model to predict the concentrations of free VEGF, the number of VEGF/VEGFR2 complexes (considered to be pro-angiogenic), and the receptor occupancy profiles following inhibition of VEGF using isoform-specific anti-VEGF agents. We predict that targeting VEGF121 leads to a 54% and 84% reduction in free VEGF in tumors that secrete both VEGF isoforms or tumors that overexpress VEGF121, respectively. Additionally, 21% of the VEGFR2 molecules in the blood are ligated following inhibition of VEGF121, compared with 88% when both isoforms are targeted. Targeting VEGF121 reduces tumor free VEGF and is an effective treatment strategy. Our results provide a basis for clinical investigation of isoform-specific anti-VEGF agents.

Thermodynamic Analysis of Biodegradation Pathways

Microorganisms provide a wealth of biodegradative potential in the reduction and elimination of xenobiotic compounds in the environment. One useful metric to evaluate potential biodegradation pathways is thermodynamic feasibility. However, experimental data for the thermodynamic properties of xenobiotics is scarce. The present work uses a group contribution method to study the thermodynamic properties of the University of Minnesota Biocatalysis/Biodegradation Database. The Gibbs free energies of formation and reaction are estimated for 914 compounds (81%) and 902 reactions (75%), respectively, in the database. The reactions are classified based on the minimum and maximum Gibbs free energy values, which accounts for uncertainty in the free energy estimates and a feasible concentration range relevant to biodegradation. Using the free energy estimates, the cumulative free energy change of 89 biodegradation pathways (51%) in the database could be estimated. A comparison of the likelihood of the biotransformation rules in the Pathway Prediction System and their thermodynamic feasibility was then carried out. This analysis revealed that when evaluating the feasibility of biodegradation pathways, it is important to consider the thermodynamic topology of the reactions in the context of the complete pathway. Group contribution is shown to be a viable tool for estimating, a priori, the thermodynamic feasibility and the relative likelihood of alternative biodegradation reactions. This work offers a useful tool to a broad range of researchers interested in estimating the feasibility of the reactions in existing or novel biodegradation pathways. Biotechnol. Bioeng. 2009;103: 532–541.

Compartment Model Predicts VEGF Secretion and Investigates the Effects of VEGF Trap in Tumor-Bearing Mice

Angiogenesis, the formation of new blood vessels from existing vasculature, is important in tumor growth and metastasis. A key regulator of angiogenesis is vascular endothelial growth factor (VEGF), which has been targeted in numerous anti-angiogenic therapies aimed at inhibiting tumor angiogenesis. Systems biology approaches, including computational modeling, are useful for understanding this complex biological process and can aid in the development of novel and effective therapeutics that target the VEGF family of proteins and receptors. We have developed a computational model of VEGF transport and kinetics in the tumor-bearing mouse, which includes three-compartments: normal tissue, blood, and tumor. The model simulates human tumor xenografts and includes human (VEGF121 and VEGF165) and mouse (VEGF120 and VEGF164) isoforms. The model incorporates molecular interactions between these VEGF isoforms and receptors (VEGFR1 and VEGFR2), as well as co-receptors (NRP1 and NRP2). We also include important soluble factors: soluble VEGFR1 (sFlt-1) and α-2-macroglobulin. The model accounts for transport via macromolecular transendothelial permeability, lymphatic flow, and plasma clearance. We have fit the model to available in vivo experimental data on the plasma concentration of free VEGF Trap and VEGF Trap bound to mouse and human VEGF in order to estimate the rates at which parenchymal cells (myocytes and tumor cells) and endothelial cells secrete VEGF. Interestingly, the predicted tumor VEGF secretion rates are significantly lower (0.007–0.023 molecules/cell/s, depending on the tumor microenvironment) than most reported in vitro measurements (0.03–2.65 molecules/cell/s). The optimized model is used to investigate the interstitial and plasma VEGF concentrations and the effect of the VEGF-neutralizing agent, VEGF Trap (aflibercept). This work complements experimental studies performed in mice and provides a framework with which to examine the effects of anti-VEGF agents, aiding in the optimization of such anti-angiogenic therapeutics as well as analysis of clinical data. The model predictions also have implications for biomarker discovery with anti-angiogenic therapies.

Computational systems biology approaches to anti-angiogenic cancer therapeutics.

Angiogenesis is an exquisitely regulated process that is required for physiological processes and is also important in numerous diseases. Tumors utilize angiogenesis to generate the vascular network needed to supply the cancer cells with nutrients and oxygen, and many cancer drugs aim to inhibit tumor angiogenesis. Anti-angiogenic therapy involves inhibiting multiple cell types, molecular targets, and intracellular signaling pathways. Computational tools are useful in guiding treatment strategies, predicting the response to treatment, and identifying new targets of interest. Here, we describe progress that has been made in applying mathematical modeling and bioinformatics approaches to study anti-angiogenic therapeutics in cancer.

A Two-Compartment Model of VEGF Distribution in the Mouse

Vascular endothelial growth factor (VEGF) is a key regulator of angiogenesis – the growth of new microvessels from existing microvasculature. Angiogenesis is a complex process involving numerous molecular species, and to better understand it, a systems biology approach is necessary. In vivo preclinical experiments in the area of angiogenesis are typically performed in mouse models; this includes drug development targeting VEGF. Thus, to quantitatively interpret such experimental results, a computational model of VEGF distribution in the mouse can be beneficial. In this paper, we present an in silico model of VEGF distribution in mice, determine model parameters from existing experimental data, conduct sensitivity analysis, and test the validity of the model. The multiscale model is comprised of two compartments: blood and tissue. The model accounts for interactions between two major VEGF isoforms (VEGF120 and VEGF164) and their endothelial cell receptors VEGFR-1, VEGFR-2, and co-receptor neuropilin-1. Neuropilin-1 is also expressed on the surface of parenchymal cells. The model includes transcapillary macromolecular permeability, lymphatic transport, and macromolecular plasma clearance. Simulations predict that the concentration of unbound VEGF in the tissue is approximately 50-fold greater than in the blood. These concentrations are highly dependent on the VEGF secretion rate. Parameter estimation was performed to fit the simulation results to available experimental data, and permitted the estimation of VEGF secretion rate in healthy tissue, which is difficult to measure experimentally. The model can provide quantitative interpretation of preclinical animal data and may be used in conjunction with experimental studies in the development of pro- and anti-angiogenic agents. The model approximates the normal tissue as skeletal muscle and includes endothelial cells to represent the vasculature. As the VEGF system becomes better characterized in other tissues and cell types, the model can be expanded to include additional compartments and vascular elements.

Inferring relevant control mechanisms for interleukin-12 signaling in naïve CD4+ T cells.

Interleukin‐12 (IL‐12) is a key cytokine involved in shaping the cell‐mediated immunity to intracellular pathogens. IL‐12 initiates a cellular response through the IL‐12 signaling pathway, a member of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) family of signaling networks. The JAK/STAT pathway includes several regulatory elements; however, the dynamics of these mechanisms are not fully understood. Therefore, the objective of this study was to infer the relative importance of regulatory mechanisms that modulate the activation of STAT4 in naïve CD4+ T cells. Dynamic changes in protein expression and activity were measured using flow cytometry and these data were used to calibrate a mathematical model of IL‐12 signaling. An empirical Bayesian approach was used to infer the relative strengths of the different regulatory mechanisms in the system. The model predicted that IL‐12 receptor expression is regulated by a dynamic, autonomous program that was independent of STAT4 activation. In summary, a mathematical model of the canonical IL‐12 signaling pathway used in conjunction with a Bayesian framework provided high‐confidence predictions of the system‐specific control mechanisms from the available experimental observations.