A Systems Biology Approach to Develop Models of Signal Transduction Pathways

Mathematical models of signal transduction pathways are characterized by a large number of proteins and uncertain parameters, yet only a limited amount of quantitative data is available. The dissertation addresses this problem using two different approaches: the first approach deals with a model simplification procedure for signaling pathways that reduces the model size but retains the physical interpretation of the remaining states, while the second approach deals with creating rich data sets by computing transcription factor profiles from fluorescent images of green-fluorescent-protein (GFP) reporter cells. For the first approach a model simplification procedure for signaling pathway models is presented. The technique makes use of sensitivity and observability analysis to select the retained proteins for the simplified model. The presented technique is applied to an IL-6 signaling pathway model. It is found that the model size can be significantly reduced and the simplified model is able to adequately predict the dynamics of key proteins of the signaling pathway. An approach for quantitatively determining transcription factor profiles from GFP reporter data is developed as the second major contribution of this work. The procedure analyzes fluorescent images to determine fluorescence intensity profiles using principal component analysis and K-means clustering, and then computes the transcription factor concentration from the fluorescence intensity profiles by solving an inverse problem involving a model describing transcription, translation, and activation of green fluorescent proteins. Activation profiles of the transcription factors NF-κB, nuclear STAT3, and C/EBPβ are obtained using the presented approach. The data for NF-κB is used to develop a model for TNF-α signal transduction while the data for nuclear STAT3 and C/EBPβ is used to verify the simplified IL-6 model. Finally, an approach is developed to compute the distribution of transcription factor profiles among a population of cells. This approach consists of an algorithm for identifying individual fluorescent cells from fluorescent images, and an algorithm to compute the distribution of transcription factor profiles from the fluorescence intensity distribution by solving an inverse problem. The technique is applied to experimental data to derive the distribution of NF-κB concentrations from fluorescent images of a NF-κB GFP reporter system

Integrated modeling and experimental approach for determining transcription factor profiles from fluorescent reporter data

<p>Abstract</p> <p>Background</p> <p>The development of quantitative models of signal transduction, as well as parameter estimation to improve existing models, depends on the ability to obtain quantitative information about various proteins that are part of the signaling pathway. However, commonly-used measurement techniques such as Western blots and mobility shift assays provide only qualitative or semi-quantitative data which cannot be used for estimating parameters. Thus there is a clear need for techniques that enable quantitative determination of signal transduction intermediates.</p> <p>Results</p> <p>This paper presents an integrated modeling and experimental approach for quantitatively determining transcription factor profiles from green fluorescent protein (GFP) reporter data. The technique consists of three steps: (1) creating data sets for green fluorescent reporter systems upon stimulation, (2) analyzing the fluorescence images to determine fluorescence intensity profiles using principal component analysis (PCA) and K-means clustering, and (3) computing the transcription factor concentration from the fluorescence intensity profiles by inverting a model describing transcription, translation, and activation of green fluorescent proteins.</p> <p>We have used this technique to quantitatively characterize activation of the transcription factor NF-κB by the cytokine TNF-α. In addition, we have applied the quantitative NF-κB profiles obtained from our technique to develop a model for TNF-α signal transduction where the parameters were estimated from the obtained data.</p> <p>Conclusion</p> <p>The technique presented here for computing transcription factor profiles from fluorescence microscopy images of reporter cells generated quantitative data on the magnitude and dynamics of NF-κB activation by TNF-α. The obtained results are in good agreement with qualitative descriptions of NF-κB activation as well as semi-quantitative experimental data from the literature. The profiles computed from the experimental data have been used to re-estimate parameters for a NF-κB model and the results of additional experiments are predicted very well by the model with the new parameter values. While the presented approach has been applied to NF-κB and TNF-α signaling, it can be used to determine the profile of any transcription factor as long as GFP reporter fluorescent profiles are available.</p

Chemotherapeutic loading via tailoring of drug-carrier interactions in poly (sialic acid) micelles

New methods in nanoparticle development have aimed to develop customized carriers suited for specific purposes. Micelles, due to their highly tailorable nature, are prime candidates for this customizable methodology. In order to maximize drug loading and tailor release, groups of the micelle core should be carefully selected in order to exploit inherent interactions between the selected drug and the carrier core. Small variations within the composition of these groups can greatly affect micelle characteristics (e.g., size, stability, loading and release). While covalent bonding of drug-to-carrier has enhanced drug loading, drawbacks include inhibited release and altered drug properties. As a result, drug/carrier non-covalent interactions such as hydrophobic attraction, hydrogen bonding and π-π stacking have all garnered great interest, allowing for both enhanced loading as well as bond dissociation to aid in drug release. Just as important, external composition of these micelles should be suited for specific therapeutic applications. Examples include providing stabilization, enhanced circulation times and site-specific targeting. Poly (sialic acid) (PSA), a naturally occurring polysaccharide, has been shown to exhibit all three of these properties yet remains relatively unexplored in the field of micelle-based cancer drug delivery applications. Here, we have grafted various phenyl-terminated alkyl groups (PTAGs) onto the backbone of PSA (PTAG-g-PSA), each selected in order to exploit a specific non-covalent interaction (hydrophobic attraction, hydrogen bonding and π-π stacking) between the PTAG group and the anthracycline chemotherapeutic doxorubicin (DOX) (Figure 1). Upon aqueous self-assembly, these amphiphiles formed micelles which exhibited variation in size, stability, cytotoxicity and DOX loading/release based upon the PTAG selected. For example, PTAGs selected to exploit either hydrogen bonding or π-π stacking loaded in a similar fashion yet varied greatly in release properties. Therefore, the synergistic effect of these small-scale modifications in core groups selected can greatly effect micelle characteristics and result in highly tailorable carriers

Long-term antiplatelet therapy in medically managed non-ST-segment elevation acute coronary syndromes: The EPICOR Asia study

OBJECTIVES: To describe long-term antithrombotic management patterns (AMPs) in medically managed Asian patients with non-ST-segment myocardial infarction (NSTEMI) or unstable angina (UA). BACKGROUND: Current guidelines support an early invasive strategy in NSTEMI and UA patients, but many are medically managed, and data are limited on long-term AMPs in Asia. METHODS: Data were analyzed from medically managed NSTEMI and UA patients included in the prospective, observational EPICOR Asia study (NCT01361386). Survivors to hospital discharge were enrolled (June 2011 to May 2012) from 8 countries/regions across Asia. Baseline characteristics and AMP use up to 2 years post-discharge were collected. Outcomes were major adverse cardiovascular events (MACE: myocardial infarction, ischemic stroke, and death) and bleeding. RESULTS: Among 2289 medically managed patients, dual antiplatelet therapy (DAPT) use at discharge was greater in NSTEMI than in UA patients (81.8% vs 65.3%), and was significantly associated with male sex, positive cardiac markers, and prior cardiovascular medications (p < 0.0001). By 2 years, 57.9% and 42.6% of NSTEMI and UA patients, respectively, were on DAPT. On multivariable Cox regression analysis, risk of MACE at 2 years was most significantly associated with older age (HR [95% CI] 1.85 [1.36, 2.50]), diagnosis of NSTEMI vs UA (1.96 [1.47, 2.61]), and chronic renal failure (2.14 [1.34, 3.41]), all p ≤ 0.001. Risk of bleeding was most significantly associated with region (East Asia vs Southeast/South Asia) and diabetes. CONCLUSIONS: Approximately half of all patients were on DAPT at 2 years. MACE were more frequent in NSTEMI than UA patients during follow-up

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure &lt; 100 mmHg (n = 1127), estimated glomerular filtration rate &lt; 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Investigation of Antimicrobial Resistance Genes in Listeria&nbsp;monocytogenes from 2010 through to 2021

Antimicrobial resistance (AMR) is a serious public health issue. Due to resistance to current antibiotics and a low rate of development of new classes of antimicrobials, AMR is a leading cause of death worldwide. Listeria monocytogenes is a deadly foodborne pathogen that causes listeriosis for the immunocompromised, the elderly, and pregnant women. Unfortunately, antimicrobial resistance has been reported in L. monocytogenes. This study conducted the first comprehensive statistical analysis of L. monocytogenes isolate data from the National Pathogen Detection Isolate Browser (NPDIB) to identify the trends for AMR genes in L. monocytogenes. Principal component analysis was firstly used to project the multi-dimensional data into two dimensions. Hierarchical clustering was then used to identify the significant AMR genes found in L. monocytogenes samples and to assess changes during the period from 2010 through to 2021. Statistical analysis of the data identified fosX, lin, abc-f, and tet(M) as the four most common AMR genes found in L. monocytogenes. It was determined that there was no increase in AMR genes during the studied time period. It was also observed that the number of isolates decreased from 2016 to 2020. This study establishes a baseline for the ongoing monitoring of&nbsp;L. monocytogenes for AMR genes

A Continuous Markov-Chain Model for the Simulation of COVID-19 Epidemic Dynamics

To address the urgent need to accurately predict the spreading trend of the COVID-19 epidemic, a continuous Markov-chain model was, for the first time, developed in this work to predict the spread of COVID-19 infection. A probability matrix of infection was first developed in this model based upon the contact frequency of individuals within the population, the individual&rsquo;s characteristics, and other factors that can effectively reflect the epidemic&rsquo;s temporal and spatial variation characteristics. The Markov-chain model was then extended to incorporate both the mutation effect of COVID-19 and the decaying effect of antibodies. The developed comprehensive Markov-chain model that integrates the aforementioned factors was finally tested by real data to predict the trend of the COVID-19 epidemic. The result shows that our model can effectively avoid the prediction dilemma that may exist with traditional ordinary differential equations model, such as the susceptible&ndash;infectious&ndash;recovered (SIR) model. Meanwhile, it can forecast the epidemic distribution and predict the epidemic hotspots geographically at different times. It is also demonstrated in our result that the influence of the population&rsquo;s spatial and geographic distribution in a herd infection event is needed in the model for a better prediction of the epidemic trend. At the same time, our result indicates that no simple derivative relationship exists between the threshold of herd immunity and the virus basic reproduction number R0. The threshold of herd immunity achieved through natural immunity is significantly higher than 1 &minus; 1/R0. These not only explain the theoretical misconceptions of herd immunity thresholds in herd immunity theory but also provide a guidance for predicting the optimal vaccination coverage. In addition, our model can predict the temporal and spatial distribution of infections in different epidemic waves. It is implied from our model that it is challenging to eradicate COVID-19 in the short term for a large population size and a wide spatial distribution. It is predicted that COVID-19 is likely to coexist with humans for a long time and that it will exhibit multipoint epidemic effects at a later stage. The statistical evidence is consistent with our prediction and strongly supports our modeling results

A Kinetic-Model-Based Approach to Identify Malfunctioning Components in Signal Transduction Pathways from Artificial Clinical Data

Detection of malfunctioning reactions or molecules from clinical data is essential for disease treatments. In order to find an alternative to the existing oversimplistic mathematical models, a kinetic model is developed in this work to infer the malfunctioning reactions/molecules by quantifying the similarity between the clinical profile and the output profiles predicted from the model in which certain reactions/molecules malfunction. The new approach was tested in IL-6 and TNF-α/NF-κB signaling pathway, for four abnormal conditions including up/downregulation of single reaction rate constants and up/downregulation of single molecules. Since limited quantitative clinical data were available, the IL-6 ODE model was used to generate artificial clinical data for the abnormal steady-state value shown in two key molecules: nuclear STAT3 and SOCS3. Similarly, the TNF-α/NF-κB model was used to obtain the data in which abnormal oscillation dynamic was shown in the profile of NF-κB. The results show that the approach developed in this study was able to successfully identify the malfunctioning reactions and molecules from the clinical data. It was also found that this new approach was noise-robust and that it managed to reveal unique solution for the faulty components in a network