Biomedical subjects

Denise Kirschner

Publications and source records attributed to Denise Kirschner.

9 recordsLinked to original sources

PubMed2006

Identifying DNA splice sites using hypernetworks with artificial molecular evolution.

Identifying DNA splice sites is a main task of gene hunting. We introduce the hyper-network architecture as a novel method for finding DNA splice sites. The hypernetwork architecture is a biologically inspired information processing system composed of networks of molecules forming cells, and a number of cells forming a tissue or organism. Its learning is based on molecular evolution. DNA examples taken from GenBank were translated into binary strings and fed into a hypernetwork for training. We performed experiments to explore the generalization performance of hypernetwork learning in this data set by two-fold cross validation. The hypernetwork generalization performance was comparable to well known classification algorithms. With the best hypernetwork obtained, including local information and heuristic rules, we built a system (HyperExon) to obtain splice site candidates. The HyperExon system outperformed leading splice recognition systems in the list of sequences tested.

Algorithms↗

PubMed2005

Mycobacterium tuberculosis as viewed through a computer.

Mathematical models are emerging as important tools in the study of microbiology. As an illustrative example, we present results from several models each generated to study the interaction of Mycobacterium tuberculosis and the immune system. Different mathematical models were formulated on the basis of assumptions regarding system-component interactions, enabling us to explore specific aspects at diverse biological scales (e.g. intracellular, cell-cell interactions, and cell population dynamics). In addition, we were able to examine both temporal and spatial aspects. At each scale, there were consistent themes that emerged as determinative in infection outcome. Factors we identified include both host and microbial characteristics. The use of the models lies in generating hypotheses that can then be tested experimentally. Here, we outline the primary host and bacterial factors that we have identified as key mechanisms that contribute to the success of M. tuberculosis as a human pathogen. Our goal is to stimulate experimentation and foster collaborations between theoretical and experimental scientists.

Animals↗

PubMed2004

Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model.

Infection with Mycobacterium tuberculosis is a major world health problem. An estimated 2 billion people are presently infected and the disease causes approximately 3 million deaths per year. After bacteria are inhaled into the lung, a complex immune response is triggered leading to the formation of multicellular structures termed granulomas. It is believed that the collection of host granulomas either contain bacteria resulting in a latent infection or are unable to do so, leading to active disease. Thus, understanding granuloma formation and function is essential for improving both diagnosis and treatment of tuberculosis. Granuloma formation is a complex spatio-temporal system involving interactions of bacteria, specific immune cells, including macrophages, CD4+ and CD8+ T cells, as well as immune effectors such as chemokine and cytokines. To study this complex dynamical system we have developed an agent-based model of granuloma formation in the lung. This model combines continuous representations of chemokines with discrete agent representations of macrophages and T cells in a cellular automata-like environment. Our results indicate that key host elements involved in granuloma formation are chemokine diffusion, prevention of macrophage overcrowding within the granuloma, arrival time, location and number of T cells within the granuloma, and an overall host ability to activate macrophages. Interestingly, a key bacterial factor is its intracellular growth rate, whereby slow growth actually facilitates survival.

Chemokines↗

PubMed2004

A model for the study of Helicobacter pylori interaction with human gastric acid secretion.

We present a comprehensive mathematical model describing Helicobacter pylori interaction with the human gastric acid secretion system. We use the model to explore host and bacterial conditions that allow persistent infection to develop and be maintained. Our results show that upon colonization, there is a transient period (day 1-20 post-infection) prior to the establishment of persistence. During this period, changes to host gastric physiology occur including elevations in positive effectors of acid secretion (such as gastrin and histamine). This is promoted by reduced somatostatin levels, an inhibitor of acid release. We suggest that these changes comprise compensatory mechanisms aimed at restoring acid to pre-infection levels. We also show that ammonia produced by bacteria sufficiently buffers acid promoting bacteria survival and growth.

Ammonia↗

PubMed2003

On treatment of tuberculosis in heterogeneous populations.

Global eradication of tuberculosis (TB) is an international agenda. Thus understanding effects of treatment of TB in different settings is crucial. In previous work, we introduced the framework for a mathematical model of epidemic TB in demographically distinct, heterogeneous populations. Simulations showed the importance of genetic susceptibility in determining endemic prevalence levels. In the work presented here, we include treatment and investigate different strategies for treatment of latent and active TB disease in heterogeneous populations. We illustrate how the presence of a genetically susceptible subpopulation dramatically alters effects of treatment in the same way a core population does in the setting of sexually transmitted diseases. In addition, we evaluate treatment strategies that focus specifically on this subpopulation, and our results indicate that genetically susceptible subpopulations should be accounted for when designing treatment strategies to achieve the greatest reduction in disease prevalence.

Demography↗

PubMed2002

A model for integrative study of human gastric acid secretion.

We have developed a unique virtual human model of gastric acid secretion and its regulation in which food provides a driving force. Food stimulus triggers neural activity in central and enteric nervous systems and G cells to release gastrin, a critical stimulatory hormone. Gastrin stimulates enterochromaffin-like cells to release histamine, which, together with acetylcholine, stimulates acid secretion from parietal cells. Secretion of somatostatin from antral and corpus D cells comprises a negative-feedback loop. We demonstrate that although acid levels are most sensitive to food and nervous system inputs, somatostatin-associated interactions are also important in governing acidity. The importance of gastrin in acid secretion is greatest at the level of transport between the antral and corpus regions. Our model can be applied to study conditions that are not yet experimentally reproducible. For example, we are able to preferentially deplete antral or corpus somatostatin. Depletion of antral somatostatin exhibits a more significant elevation of acid release than depletion of corpus somatostatin. This increase in acid release is likely due to elevated gastrin levels. Prolonged hypergastrinemia has significant effects in the long term (5 days) by promoting enterochromaffin-like cell overgrowth. Our results may be useful in the design of therapeutic strategies for acid secretory dysfunctions such as hyper- and hypochlorhydria.

Cell Division↗

PubMed2002

Dynamics of naive and memory CD4+ T lymphocytes in HIV-1 disease progression.

Understanding the dynamics of naive and memory CD4+ T cells in the immune response to HIV-1 infection can help elucidate typical disease progression patterns observed in HIV-1 patients. Although infection markers such as CD4+ T-cell count and viral load are monitored in patient blood, the lymphatic tissues (LT) have been shown to be an important viral reservoir. Here, we introduce the first comprehensive theoretical model of disease progression based on T-cell subsets and virus circulating between the two compartments of LT and blood. We use this model to predict several trademarks observed in adult HIV-1 disease progression such as the establishment of a setpoint in the asymptomatic stage. Our model predicts that both host and viral elements play a role in determining different disease progression patterns. Viral factors include viral infectivity and production rates, whereas host factors include elements of specific immunity. We also predict the effect of highly active antiretroviral therapy and treatment cessation on cellular and viral dynamics in both blood and LT.

Adult↗

PubMedDate not supplied

Mathematical analysis of the global dynamics of a model for HTLV-I infection and ATL progression.

Mathematical analysis is carried out that completely determines the global dynamics of a mathematical model for the transmission of human T-cell lymphotropic virus I (HTLV-I) infection and the development of adult T-cell leukemia (ATL). HTLV-I infection of healthy CD4(+) T cells takes place through cell-to-cell contact with infected T cells. The infected T cells can remain latent and harbor virus for several years before virus production occurs. Actively infected T cells can infect other T cells and can convert to ATL cells, whose growth is assumed to follow a classical logistic growth function. Our analysis establishes that the global dynamics of T cells are completely determined by a basic reproduction number R(0). If R(0)< or =1, infected T cells always die out. If R(0)>1, HTLV-I infection becomes chronic, and a unique endemic equilibrium is globally stable in the interior of the feasible region. We also show that the equilibrium level of ATL-cell proliferation is higher when the HTLV-I infection of T cells is chronic than when it is acute.

Adult↗

PubMedDate not supplied

Comparing epidemic tuberculosis in demographically distinct heterogeneous populations.

There is wide variation in endemic tuberculosis (TB) levels between countries and we seek to identify possible causes of these differences. In this study we present an epidemiological model of Mycobacterium tuberculosis infection to investigate the effects of host genetics and demographic factors on epidemic TB. We discuss the general framework for this approach and present analytical results to identify important parameters affecting steady-state prevalence and incidence rates of TB disease. We then use numerical simulations of our model to observe the effects of a genetically susceptible subpopulation on TB disease dynamics at the population level. Finally, we simulate infection within a genetically heterogeneous population in two demographic settings: India (a typical population with high TB prevalence) and the USA (a typical population with low TB prevalence). Results show that changes in transmission parameters, the fraction of the population genetically susceptible to infection, and demographic factors strongly affect TB prevalence and incidence rates.

Computer Simulation↗