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Biomedical subjects

Stephanie Forrest

Publications and source records attributed to Stephanie Forrest.

13 recordsLinked to original sources
PubMed2006

Modeling somatic evolution in tumorigenesis.

Tumorigenesis in humans is thought to be a multistep process where certain mutations confer a selective advantage, allowing lineages derived from the mutated cell to outcompete other cells. Although molecular cell biology has substantially advanced cancer research, our understanding of the evolutionary dynamics that govern tumorigenesis is limited. This paper analyzes the computational implications of cancer progression presented by Hanahan and Weinberg in The Hallmarks of Cancer. We model the complexities of tumor progression as a small set of underlying rules that govern the transformation of normal cells to tumor cells. The rules are implemented in a stochastic multistep model. The model predicts that (i) early-onset cancers proceed through a different sequence of mutation acquisition than late-onset cancers; (ii) tumor heterogeneity varies with acquisition of genetic instability, mutation pathway, and selective pressures during tumorigenesis; (iii) there exists an optimal initial telomere length which lowers cancer incidence and raises time of cancer onset; and (iv) the ability to initiate angiogenesis is an important stage-setting mutation, which is often exploited by other cells. The model offers insight into how the sequence of acquired mutations affects the timing and cellular makeup of the resulting tumor and how the cellular-level population dynamics drive neoplastic evolution.

Cell Transformation, Neoplastic↗
PubMed2006

Modeling intercellular interactions in early Mycobacterium infection.

Infection with Mycobacterium tuberculosis (Mtb) is characterized by localized, roughly spherical lesions within which the pathogen interacts with host cells. Containment of the infection or progression of disease depends on the behavior of individual cells, which, in turn, depends on the local molecular environment and on contact with neighboring cells. Modeling can help us understand the nonlinear interactions that drive the overall dynamics in this system. Early events in infection are particularly important, as are spatial effects and inherently stochastic processes. We describe a model of early Mycobacterium infection using the CyCells simulator, which was designed to capture these effects. We relate CyCells simulations of the model to several experimental observations of individual components of the response to Mtb.

Computer Simulation↗
PubMed2006

Simulating the hallmarks of cancer.

Cancer can be viewed as the loss of cooperative cell behaviors that normally facilitate multicellularity, including the formation of tissues and organs. Hanahan and Weinberg describe the phenotypic differences between healthy and cancerous cells in an article titled "The Hallmarks of Cancer" (Cell, 100, 57-70, 2000). Here the authors propose six phenotypic changes at the cellular level as the essential hallmarks of cancer. They investigate the dynamics and interactions of these hallmarks in a model known as CancerSim. They describe how CancerSim implements the hallmarks in an agent-based simulation which can help test the hypotheses put forth by Hanahan and Weinberg. Experiments with CancerSim are described that study the interactions of cell phenotype alterations, and in particular, the likely sequences of precancerous mutations, known as pathways. The experiments show that sequencing is an important factor in tumorigenesis, as some mutations have preconditions--they are selectively advantageous only in combination with other mutations. CancerSim enables a modeler to study the dynamics of a developing tumor and simulate how progression can be altered by tuning model parameters.

Apoptosis↗
PubMed2005

The effects of thymic selection on the range of T cell cross-reactivity.

Based on the results of a computational model of thymic selection, we propose a mechanism that produces the observed wide range of T cell cross-reactivity. The model suggests that the cross-reactivity of a T cell that survives thymic selection is correlated with its affinity for self peptides. In order to survive thymic selection, a T cell with low affinity for all self peptides expressed in the thymus must have high affinity for major histocompatibility complex (MHC), which makes it highly cross-reactive. A T cell with high affinity for any self peptide must have low MHC affinity to survive selection, which makes it highly specific for its cognate peptide. Our model predicts that (1) positive selection reduces by only 17% the number of T cells that can detect any given foreign peptide, even though it eliminates over 95% of pre-selection cells; (2) negative selection decreases the average cross-reactivity of the pre-selection repertoire by fivefold; and (3) T cells responding to foreign peptides similar to self peptides will have a lower average cross-reactivity than cells responding to epitopes dissimilar to self.

Animals↗
PubMed2005

A machine learning evaluation of an artificial immune system.

ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.

Algorithms↗
PubMed2004

A stochastic model of cytotoxic T cell responses.

We have constructed a stochastic stage-structured model of the cytotoxic T lymphocyte (CTL) response to antigen and the maintenance of immunological memory. The model follows the dynamics of a viral infection and the stimulation, proliferation, and differentiation of naïve CD8(+) T cells into effector CTL, which can eliminate virally infected cells. The model is capable of following the dynamics of multiple T cell clones, each with a T cell receptor represented by a digit string. MHC-viral peptide complexes are also represented by strings and a string match rule is used to compute the affinity of a T cell receptor for a viral epitope. The avidities of interactions are also computed by taking into consideration the density of MHC-viral peptides on the surface of an infected cell. Lastly, the model allows the probability of T cell stimulation to depend on avidity but also incorporates the notion of an antigen-independent programmed proliferative response. We compare the model to experimental data on the cytotoxic T cell response to lymphocytic choriomeningitis virus infections.

Antigens, Viral↗
PubMed2004

Homeostasis of peripheral immune effectors.

In this paper, we use both mathematical modeling and simulation to explore homeostasis of peripheral immune system effector cells, particularly alveolar macrophages. Our interest is in the distributed control mechanisms that allow such a population to maintain itself. We introduce a multi-purpose simulator designed to study individual cell responses to local molecular signals and their effects on population dynamics. We use the simulator to develop a model of growth factor regulation of macrophage proliferation and survival. We examine the effects of this form of regulation in the context of two competing hypotheses regarding the source of new alveolar macrophages. In one model, local cells divide to replenish the population; in the other, only cells migrating from circulation divide. We find that either scenario is plausible, although the influx-driven system is inherently more stable. The proliferation-driven system requires lower cell death and efflux rates than the influx-driven system.

Animals↗
PubMed2004

A formal framework for positive and negative detection schemes.

In anomaly detection, the normal behavior of a process is characterized by a model, and deviations from the model are called anomalies. In behavior-based approaches to anomaly detection, the model of normal behavior is constructed from an observed sample of normally occurring patterns. Models of normal behavior can represent either the set of allowed patterns (positive detection) or the set of anomalous patterns (negative detection). A formal framework is given for analyzing the tradeoffs between positive and negative detection schemes in terms of the number of detectors needed to maximize coverage. For realistically sized problems, the universe of possible patterns is too large to represent exactly (in either the positive or negative scheme). Partial matching rules generalize the set of allowable (or unallowable) patterns, and the choice of matching rule affects the tradeoff between positive and negative detection. A new match rule is introduced, called r-chunks, and the generalizations induced by different partial matching rules are characterized in terms of the crossover closure. Permutations of the representation can be used to achieve more precise discrimination between normal and anomalous patterns. Quantitative results are given for the recognition ability of contiguous-bits matching together with permutations.

Journal Article↗
PubMed2004

Modelling the impact of antigen kinetics on T-cell activation and response.

Cytotoxic T lymphocyte (CTL) responses are thought to be important for the control of many viral and other infections. Qualitative aspects of the CTL response, including the epitope specificity, affinity, and clonal composition, may affect the ability of T cells to mediate infection control. Although it is clear that the mode of introduction and the dose of antigen can affect these qualitative aspects of the response, little is understood of the mechanisms. We have developed an in silico model of the CTL response, which we use to study the impact of antigen dose, antigen kinetics and repeated antigen delivery on the response. The results suggest that recent observations on differences in response to killed antigen can be explained simply by differences in timing of T-cell activation. These findings may provide insight into how different vaccination strategies can quantitatively and qualitatively affect the outcome of the immune response.

Antigens, Viral↗
PubMed2004

Cancer prevention strategies that address the evolutionary dynamics of neoplastic cells: simulating benign cell boosters and selection for chemosensitivity.

Cells in neoplasms evolve by natural selection. Traditional cytotoxic chemotherapies add further selection pressure to the evolution of neoplastic cells, thereby selecting for cells resistant to the therapies. An alternative proposal is a benign cell booster. Rather than trying to kill the highly dysplastic or malignant cells directly, a benign cell booster increases the fitness of the more benign cells, which may be either normal or benign clones, so that they may outcompete more advanced or malignant cells in a neoplasm. In silico simulations of benign cell boosters in neoplasms with evolving clones show benign cell boosters to be effective at destroying advanced or malignant cells and preventing relapse even when applied late in progression. These results are conditional on the benign cell boosters giving a competitive advantage to the benign cells in the neoplasm. Furthermore, the benign cell boosters must be applied over a long period of time in order for the benign cells to drive the dysplastic cells to extinction or near extinction. Most importantly, benign cell boosters based on this strategy must target a characteristic of the benign cells that is causally related to the benign state to avoid relapse. Another promising strategy is to boost cells that are sensitive to a cytotoxin, thereby selecting for chemosensitive cells, and then apply the toxin. Effective therapeutic and prevention strategies will have to alter the competitive dynamics of a neoplasm to counter progression toward invasion, metastasis, and death.

Antineoplastic Agents↗
PubMed2003

Stochastic stage-structured modeling of the adaptive immune system.

We have constructed a computer model of the cytotoxic T lymphocyte (CTL) response to antigen and the maintenance of immunological memory. Because immune responses often begin with small numbers of cells and there is great variation among individual immune systems, we have chosen to implement a stochastic model that captures the life cycle of T cells more faithfully than deterministic models. Past models of the immune response have been differential equation based, which do not capture stochastic effects, or agent-based, which are computationally expensive. We use a stochastic stage-structured approach that has many of the advantages of agent-based modeling but is much more efficient. Our model can provide insights into the effect infections have on the CTL repertoire and the response to subsequent infections.

Adaptation, Physiological↗
PubMed2002

Email networks and the spread of computer viruses.

Many computer viruses spread via electronic mail, making use of computer users' email address books as a source for email addresses of new victims. These address books form a directed social network of connections between individuals over which the virus spreads. Here we investigate empirically the structure of this network using data drawn from a large computer installation, and discuss the implications of this structure for the understanding and prevention of computer virus epidemics.

Journal Article↗