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Past MBI Seminars: 2005-2006 Wednesday, August 24, 3:30-4:30am We will discuss the advantages of using irreducible representations of su(2) and so(3) in describing various interactions in NMR. We will show how these representations to analyze the NMR of half-integer quadrupolar nuclei. We will also make use of theserepresentations to develop the theory of pulse sequences, and analyze a few pulse sequences used in the NMR of biological samples. The talk will introduce all the concepts required, and will be accessible to a broad audience. Thursday, September 22, 10:30-11:30am Presentation materials: PDF The talk will introduce the basics of NMR. The various spin interactions present in biological samples will be discussed, along with the commonly used methodology to obtain high resolution solid-state NMR spectra for these samples. Thursday, October 6, 10:30-11:30am SELEX experiments allow extracting, from an initially random pool of DNA, those oligomers with high affinity for a given DNA-binding protein. We address what is a suitable experimental and computational procedure to infer parameters of protein-DNA interaction from SELEX experiments. To answer this, we use a biophysical model of protein-DNA interactions to quantitatively model SELEX and show that the standard procedure is unsuitable for obtaining the interaction parameters. However, we show that a suitably modified experiment allows robust generation of an appropriate data set. Based on our quantitative model, we propose a novel bioinformatic method of data analysis. Our method results in a significantly improved false positive/false negative trade-off, as compared to the standard information-theory based method. In the second part of the talk, I will discuss analysis of virulent bacteriophage gene expression strategies. Most of genes of virulent Xp10 bacteriophage are organized similarly to lambdoid phages that rely only on host RNA polymerase for their development. However, unlike the lambdoid phages, Xp10 encodes its own RNA polymerase. We perform global transcription profiling, kinetic modeling and bioinformatics analyses, in order to understand the role of both host and phage RNA polymerases in the Xp10 gene expression. Our analysis results in the quantitative estimates of contributions of both RNA polymerases to the rates of transcription of all Xp10 genes, and in the identification of the previously unknown promoter sequence for Xp10 RNA polymerase. Developed methods of data analysis can be used to efficiently infer transcription strategies of other novel bacterial viruses. Monday, October 10, 3:30-4:30amMBI Lecture Hall - Mathematics Building, Room 240 Speaker: David Terman, Mathematical Biosciences Institute, The Ohio State University Title: Sleep Rhythms Paper: PDF Tuesday, October 11, 4:30-5:30pm Game-theoretic models are extensively used in the study of animal behavior. These models are used to predict optimal strategies for a variety of animal interactions, such as fighting, foraging, or signaling. In order to be of predictive value, a strategy must be evolutionarily stable (abbreviated ESS), which means that a population of animals who follow an ESS must be resistant to invasion of mutants who follow a different strategy. The first part of this talk will give a very brief introduction to evolutionary game theory and the ESS concept. Then a new game-theoretic model will be introduced that makes predictions about which contestant (the likely winner or the likely loser) can be expected to initiate escalation in a contest. Next, computer simulation studies on whether the ESS's predicted for this game can actually evolve in a finite population that initially behaves randomly will be presented. Finally, some experiments about representing strategies in computer simulations will be reported, and the relevance of the findings for the study of the genotype-phenotype map will be discussed. Thursday, October 13, 10:30-11:30am The emerging field of systems biology is focused on the integration of biological information into predictive mathematical models. One primary approach in the systems-biology paradigm is to build models from time series of experimental data, obtained by measuring the response of a biological system to perturbations. Referred to as reverse engineering, this approach is used to elucidate features of such systems, including their structure and dynamics. Of relevance for reverse engineering is to design biological experiments that are suitable for modeling and to identify perturbations that will reveal salient features of the system. In this talk I will introduce a collaborative project, in which one objective is to generate appropriate time series data for reverse engineering a stress-response network in yeast. I will present a modeling approach that uses algorithmic tools from computational algebra to build the set of all possible discrete models that fit time series data and to select minimal models from this set. In this setting, discrete models are given by systems of polynomial functions over a finite field. As it is important to identify which perturbations are best suited to build accurate models, properties of the data that make them appropriate for the discrete modeling method will be discussed. Thursday, October 13, 3:30-4:30amMBI Lecture Hall - Mathematics Building, Room 240 Tuesday, October 18, 4:30-5:30pm Thursday, October 20, 10:30-11:30am Articular cartilage is the primary load-bearing soft tissue in joints such as the knee, shoulder, and hip. Multiphasic continuum mixture models have been used to describe the relative contribution of effects due to solid, fluid, and ionic phases in cartilage. This research is motivated by the need to quantify differences between the normal and osteoarthritic mechanical and physico-chemical states in the tissue. In this talk, I will present numerical methods and mathematical models pertaining to the cells and extracellular matrix of articular cartilage. Two problems will be described. The first problem is the development of an accelerated numerical method for the continuous spectrum biphasic poroviscoelastic (BPVE) model of articular cartilage deformation. The second problem is the formulation and application of a triphasic mechano-chemical model to analyze osmotic loading experiments for the chondron, which is the functional cell-matrix unit in cartilage. Monday, October 24, 3:30-4:30amMBI Lecture Hall - Mathematics Building, Room 240 Speaker: Sharmila Venugopal Title: A Model for Central Pattern Generator Driving Taste-Induced Ingestion and Rejection Oromotor Behaviors Tuesday, October 25, 4:30-5:30pm Abstract: Locally stationary time series have formally been introduced by Dahlhaus (1997). A simple example is provided by an autoregressive process with time varying parameters. We show how such models can successfully be applied to the problem of discriminating seismographic readings of earthquakes and explosions. Discrimination is here based on functionals of estimated time varying variance functions. Our method has the advantage that no alignment of the underlying time series is required. Estimating the variance functions is accomplished via a minimum distance approach. We utilize prior knowledge about our target problem by introducing shape constraints into the estimation process. Some justification for our method in form of large sample results will be presented, and the method is illustrated using simulations and a real data application. This presentation is based on joint work with G. Chandler and R. Dahlhaus. Thursday, October 27, 10:30-11:30am Despite the fact that all terrestrial plants require the same essential resources (such as mineral nutrients, water, and sunlight), we often observe multiple plant species successfully living closely together. In this talk, we investigate the role of canopy partitioning (or vertical leaf placement) as a possible mechanism by which clonal plant species with different competitive abilities may coexist. I will begin by showing how plant competition for sunlight fits within the mathematical framework of resource competition. Next, I will present an analytic model of clonal plant population growth that emphasizes the role of light capture by leaves at different heights. This model's extension to two species competition is realized by a system of Kolmogorov integro-differential equations that are coupled through the species' vertical leaf density functions. I will then describe some mathematical methods that we use to determine the outcome of competition between two model species in the interesting but difficult case that they possess overlapping vertical leaf profiles. If time permits, I will also indicate some ways in which the biological realism of this model can be increased without altering its qualitative conclusions. This work is in collaboration with Richard R. Vance of the University of California, Los Angeles. Tuesday, November 1, 4:30-5:30pm Presentation materials: PPT Prions are infectious proteins that are hypothesized to be the causative agent of diseases such as Creutzfeld-Jacob disease in humams, scrapie in sheep, and bovine spongiform encephalopathies in cows (mad cow disease). This hypothesis is controversial, because prion populations are capable of proliferation even though prions do not contain DNA or RNA. A mathematical model is analyzed to explain prion proliferation. Them model consists of a system of nonlinear ordinary and partial differential equations. An analysis is given of the model, and model simulations are compared to experimental data. Thursday, November 3, 10:30-11:30am In the study of various aspects of cell metabolism, in particular, folate and methionine metabolism, new mathematical models are developed. The new models are used to to better understand the temporal variations in methionine and folate input on the other metabolite concentrations. Sensitivity analysis of the model is performed to better understand the molecular mechanisms underlying the complexity of the cycles. More specifically, we were interested in how the model qualitative behavior depends on precise choices of parameter values. This is an ongoing work, we finally aim to develop a visualization project, which it will be designed to be used by scientists as testbeds for exploring and evaluating the folate and methionine metabolism. Using advanced computer imaging techniques, the folate cycle and the methionine cycle may be reconstructed from model parameters. The computer reconstructions created through the visualization project will permit cycles to be explored interactively for presentation purposes, while providing an additional modality for data exploration and analysis. PS 1: The folic acid cycle plays a central role in cell metabolism. Among the important functions of the folate cycle are the synthesis of pyrimidines and purines and the delivery of one carbon units to the methionine cycle for use in methylation reactions. Dietary folate deficiencies as well as mutations in enzymes of the folate cycle are associated with megaloblastic anemia, cancers of the colon, breast and cervix, affective disorders, cleft palate, neural tube defects, Alzheimers disease, Down's syndrome, preeclampsia and early pregnancy loss and several enzymes in the cycle are the targets of anti-cancer drugs. PS 2: The methionine cycle is important for the regulation of homocysteine, an important risk factor for heart disease, and for the control of DNA methylation. Both hyper- and hypomethylation have been proposed as crucial steps in chains of events that turn normal cells into cancerous cells. Monday, November 7, 3:30-4:30am Tuesday, November 8, 4:30-5:30pm
Wednesday, November 9, 4:30-5:30pm Thursday, November 10, 10:30-11:30am Determining the long-time behavior of large dynamical systems has proved to be a remarkably difficult problem. And yet the robustness and stability of molecular networks in biology seems to indicate a certain underlying structure that doesn't change under (some) small changes in the topology or the parameter values. Using the theory of monotone systems, we have tried to underline some of the relevant stability features of certain potentially high-dimensional systems. In this talk, I give sufficient qualitative and quantitative conditions for global attractivity and multistability, even for systems that are not monotone themselves, with applications to delay differential equations arising in molecular biology. Monday, November 21, 2:30-3:30pm Tuesday, November 22, 4:30-5:30pm Tuesday, November 29, 4:30-5:30pm This talk will describe several combinatorial descriptions of RNA secondary structure topologies. I will review some of the older models and describe in detail a new model using permutations. This most recent (permutation) model gives an exact description of the permutations involved, and relates classical statistics on permutations to information on the secondary structures. Tuesday, December 6, 4:30-5:30pm The neural coding of the direction of stimulus motion, which is a classic example of local neural computation, is a common feature of the nervous system. In the vertebrate retina, the mechanisms that underlie the computation of the direction of image motion remain unresolved. Recent evidence indicates that directionally-selective light responses occur first in the dendrites of a retinal interneuron, the starburst amacrine cell, and that these responses are highly sensitive to the activity of Na-K-2Cl (NKCC) and K-Cl (KCC), two types of chloride cotransporter that determine whether the neurotransmitter GABA depolarizes or hyperpolarizes neurons, respectively. By measuring the GABA reversal potential in different starburst dendritic compartments a nd by mapping NKCC2 and KCC2 antibody staining on these dendrites, we have recently found that the localization of NKCC2 and KCC2 in different dendritic compartments results in a GABA-evoked depolarization and hyperpolarization at the NKCC2 and KCC2 compartments, respectively, and underlies the directionally-selective responses of starburst dendrites. Computational analysis of light-evoked voltage changes at the starburst cell body and dendritic tip suggest that directionally-selective light responses similar to those we have observed experimentally can be generated if there is a chloride gradient along starburst dendrites due to the differential compartmentalization of the chloride cotransporters and if the GABA-evoked increase in the chloride conductance is relatively long-lasting. Experimental measurements indicate that GABA produces long-lasting responses from starburst cells. The functional compartmentalization of interneuron dendrites may be an important means by which the nervous system computes complex information at the subcellular level. Thursday, December 8, 10:30-11:30am The central ideas of Formal Concept Analysis revolve around the notion of a formal context and a formal concept. Of interest is the duality called Galois connection that arises naturally in different contexts. This duality is often observed between sets whose elements are related, such as objects and their attributes. In a Galois connection between two sets, the increase in size of one set corresponds to the decrease in size of the other set and vice versa. For example, an increase in the number of search terms used in a Google query corresponds, in general, to a decrease in the number of hits. I will introduce the fundamentals of Formal Concept Analysis and demonstrate how we applied the ideas of the field to problems in microarray analysis. In this work we integrate biological attributes related to genes along with their expression values obtained from a microarray experiment. The integrated data is represented as a partially ordered set that respect Galois connections inherent in the data. Metrics are applied to the representations of multiple samples to discover biological similarities. Thursday, January 19, 10:30-11:30am Sequence alignment is the most prevalent computational method for functionally annotating newly found genes. It remains a crucial problem in the application of sequence alignment to distinguish between biologically significant and spurious similarities between the query sequence and a database sequence. Current numerical methods for assessing the statistical significance of local alignments with gaps are time consuming. Analytical solutions thus far have been limited to specific cases. Here, we present a new line of attack to the problem of statistical significance assessment. We combine this new approach with known properties of the dynamics of the global alignment algorithm and high performance numerical techniques and present a novel method for assessing significance of gaps within practical time scales. The results and performance of these new methods test very well against tried methods with drastically less effort. Tuesday, January 24 Spatial moment equations are an alternative approach to understanding population and community dynamics in a heterogeneous spatial environment. In contrast to typical spatial PDEs, which track population densities at every point in the habitat, spatial moment equations focus on the spatial correlations in density between nearby points. I will give a brief and highly non-rigorous derivation of spatial moment equations, followed by an application to the evolution of dispersal distance and shape in a heterogeneous environment. Thursday, January 26, 10:30-11:30am Tuesday, January 31, 4:30-5:30pm The mathematical tools used in protein structure determination from Nuclear Magnetic resonance data are distance geometry and discrete differential geometry, dealing with distance and orientational constraints respectively. We give an introduction to these methods and their use in finding structures of proteins from NMR experiments in solid-state. Tuesday, February 14, 4:30-5:30pm Thursday, February 16, 10:30-11:30am When standing up, blood is pooled in the legs due to the effect of gravity resulting in a drop in systemic arterial pressure and widening of the blood flow velocity. This can be modeled by increasing the blood pressure in the compartments representing the lower body. To restore blood pressure and blood flow velocity a number of regulatory mechanisms are activated. The most important mechanisms are autonomic reflexes mediated by the sympathetic nervous system and cerebral autoregulation mediated by changes in concentrations of oxygen and carbon dioxide. The response to standing is an increase in nervous activity, which results in increased heart rate and cardiac contractility, vasoconstriction of the systemic arterioles, and changes in unstressed volume and venous compliance. The response by the cerebral autoregulation is to dilate arterioles in the cerebral vascular bed. It is not clear how the autonomic and autoregulation interacts; one theory suggests that vasoconstriction, resulting from increased sympathetic activity, has an effect throughout the body, but that cerebral vasoconstriction gets overridden (possibly with a significant delay) by autoregulation resulting in a net vasodilatation of the cerebral vascular bed. In this work we demonstrate how mathematical modeling can be used to predict the interaction between autonomic and autoregulation, and how methods from optimal control theory can be used to identify model parameters to make the model patient specific. We will show that our models can be used to predict the response, for healthy young people, for healthy elderly people, and for hypertensive people. Thursday, February 16, 1:00pm Tuesday, February 21, 10:30-11:30am The focus of this talk is a 3-dimensional, stochastic, rule-based model of immune response to viral pathogens. In its present form, the PathSim model focuses on Epstein-Barr virus (EBV) infection of the Waldeyer's tonsilar ring. EBV is a ubiquitous and sometimes pathogenic human herpesvirus that establishes a life-long infection in B cells despite an aggressive immune response. EBV is an ideal model system for studying persistent infection because: 1) sites of infection are accessible; 2) levels of infected cells, viral shedding, anti-viral antibody, and T cell responses can be measured in parallel; and 3) infection can be studied from an extreme state of perturbation (mononucleosis) into persistence. Mechanisms underlying the establishment and maintenance of persistence are complex and, given the lack of animal models, we seek to better understand them using modeling strategies.A multi-scale anatomical viewer helps to visualize infection model dynamics. Preliminary results qualitatively match clinical data. Furthermore, simulations reveal that persistence appears very dependent on access to the circulation of latently infected B cells; when access to the circulation is blocked,the infection is cleared. One factor that dramatically affects the course of infection is the percentage of latently infected cells triggered to begin viral replication upon returning from the blood. Rule-based models are well-suited for the simulation of dynamics resulting from a large number of spatially distributed, interacting entities, such as virions and immune cells. One of their shortcomings, however, is the relative lack of mathematical tools available to analyze model dynamics and, in particular, to formulate and solve optimal control problems. We will describe an approach to develop a mathematical foundation for PathSim, which allows the development of control theoretic methods. Thursday, February 23, 10:30-11:30am Tuesday, February 28 Monday, March 6, 10:30-11:30am Tuesday, March 7 The development of drug-resistant strains of bacteria is an increasing threat to society, especially in hospital settings. Many antibiotics that were formerly effective in combating bacterial infections in hospital patients are no longer effective due to the evolution of resistant strains. The evolution of these resistant strains compromises medical care worldwide. In this article, we formulate a two-level population model to quantify key elements in nosocomial infections. At the bacteria level patients infected with these strains generate both nonresistant and resistant bacteria. At the patient level susceptible patients are infected by infected patients at rates proportional to the total bacteria load of each strain present in the hospital. The objectives are to analyze the dynamic elements of nonresistant and resistant bacteria strains in epidemic populations in hospital environments and to provide understanding of measures to avoid the endemicity of resistant antibiotic strains. Wednesday, March 8, 4:30-5:00pm All organisms are composed of multiple chemical elements such as carbon, nitrogen, and phosphorus. Element cycling and energy flow are two fundamental and unifying principles in ecosystem theory; however, population models rarely take advantage of the former. Instead, they assume chemical homogeneity of all populations by concentrating on a single constituent, generally an equivalent of energy. In this talk, we examine ramifications of an explicit assumption that both predator and prey are chemically heterogeneous. Using stoichiometric principles, we construct a 2D Lotka-Volterra predator-prey type model where both populations are composed of two essential elements: carbon and phosphorous. The analysis shows that indirect competition between two populations for phosphorus can shift predator-prey interactions from a (+, -) type to an unusual (-, -) class. This leads to complex dynamics with multiple positive equilibria, where bistability and deterministic extinction of the predator are possible. Rosenzweig's paradox of enrichment holds only in the part of the phase plane where the predator is energy (food quantity) limited; a new phenomenon, the paradox of energy enrichment, arises in the other part, where the predator is phosphorus (food quality) limited. Subsequent laboratory experiments validated the outcomes of this model. Thursday, March 9, 11:45am Tuesday, March 21, 4:30-5:30pm Since 1999, West Nile virus has spread spatially from the East to the West coast of North America. A partial differential equation model for this spatial spread is developed and analyzed. The model has cross infection between mosquitoes and birds, with diffusion terms describing their movement. Using a simplified version of the model, the cooperative nature of the cross-infection dynamics is used to prove the existence of traveling waves and to give an expression for the spatial spread of infection. A comparison theorem is used to show that this spread rate may provide an upper bound for the spread rate of the more realistic model. March 23, Thursday, 4:30-5:30pm Sleep Seminar Monday April 3, 12:30pm Presentation materials: PDF Sleep Seminar Tuesday, April 11, 4:30-5:30pm Rabies, the most important viral zoonotic disease world-wide, has been undergoing epidemic expansion along the eastern seaboard of the United States since the mid-1970s following an accidental introduction of rabid raccoons from a source of endemic infection in the southeastern US. Using data submitted from US States to the Centers for Disease Control and Prevention, we have constructed stochastic simulations of the spatial dynamics of rabies as it has spread into new geographic region. The simulation was constructed as an interaction network with nodes of the network defined by township and county centroids. Interaction strengths along specific connections were sensitive to local geographic conditions and parameterized against reported data on the time and spatial location of detected rabid animals. The parameterized model has proven to be a valuable model for strategic planning for disease emergence and to direct the development of spatial control strategies. Thursday, April 13, 10:30-11:30am Friday, April 14, 2:30-3:30 pm (tentative) Tuesday, April 18, 3:30pm Gonadotropin releasing-hormone (GnRH) is a small neuropeptide that regulates pituitary release of luteinizing hormone (LH) and follicle-stimulating hormone (FSH). These gonadotropins are essential for the regulation of reproductive function. GnRH release is not continuous, but rather is released in episodic pulses which are essential for reproduction. The identity of the neuronal substrate that results in pulsatile GnRH release, and therefore comprises the GnRH "pulse" generator, is unknown. The intermittent stimuli for GnRH release may arise from input to the GnRH cells and reflect synaptic interactions between GnRH neurons and a secondary network. Alternatively, pulsatile release of GnRH may be a consequence of spontaneous activity of the GnRH neurons themselves. These two hypotheses are not mutually exclusive; the GnRH pulse generator is likely derived from a combination of intrinsic properties and synaptic interactions. I will present evidence from electrophysiological experiments in single GnRH neurons and compartmental models of GnRH neurons that supports a role for excitatory synaptic input as a key regulator of repetitive firing in GnRH neurons. Thursday, April 20, 10:30-11:30am Thursday, April 27, 10:30-11:30am Friday, April 28, 4:30-5:20pm Neonates are more subject to the Sudden Death Infant syndrom than other babies, mainly because their nervous organisation is not fully achieved when they come to life. In Amiens hospital, pedriatricians started a new protocol (30 babies treated up to now) : they gave them caffeine, so as to augment their hearth activity and they bet this treatment would keep the babies in better conditions not to forget to breathe. Of course, on the other hand, overdose of caffeine may threaten the babies'lives due to possible tachycardia events. Unfortunately, it is not technically feasable to monitor online caffeine concentration within a neonate's blood and a protocol to keep caffeine concentration within therapeutic bounds has to be designed and adapted to each baby. Sandrine Micallef and Billy Amzal, two PhD students of mine have been working on a Bayesian analysis of such a cafeine treatment for neonates. I will first describe a population model that has been set up to describe the cafeine elimination within the body of baby. We have designed a nonstationary compartment model that takes into account the rapid growth rate of babies during their first weeks. Second, I will focus onto the design of the caffeine protocol that has to be optimized under uncertainty and how it can be updated when a new case enters the study. Tuesday, May 2, 4:30pm In recent studies, we have applied a diffusion model to examine differences in processing between college students and older adults. The results show that in several paradigms, namely signal detection, brightness discrimination, recognition memory, and lexical decision, the rate of accumulation of evidence in the decision process is not significantly different for the two groups. Longer response times for the older adults come from more conservative decision criteria and from a small increase in the nondecision components of processing. In contrast, in letter discrimination, the older adults' longer response times come from a reduced rate of accumulation of evidence, as well as more conservative decision criteria and a small increase in the nondecision components of processing. In this talk, we review these results, present data from the same group of subjects tested on four of these tasks, and we apply other sequential sampling models to the data from the five paradigms to determine whether the results obtained using the diffusion model are specific to that model or general across the class of models. I will also work through the models and show how they account for correct and error RT distributions and accuracy and how parameters are associated with different experimental effects. Thursday, May 4, 10:30-11:30am The chemostat is a biological reactor used to study the dynamics of species competing for nutrients. If there are n>1 competitors and a single nutrient, then at most one species survives, provided the control variables of the reactor are constant. This result is known as the competitive exclusion principle. I will review what happens if one of the control variables-the dilution rate- is treated as a feedback variable. Several species can coexist for appropriate choices of the feedback. Also, the dynamical behavior can be more complicated, exhibiting oscillations or bistability. Thursday, May 4, 3:30pm There is growing interest in proteins that lack a stable and well-defined three-dimensional structure - often referred to as intrinsically disordered proteins - but have functionally important properties that depend on the lack of structure. It has been shown that these proteins possess a range of important properties and functions that derive from being disordered. In this talk, I explore the properties of intrinsically disordered proteins with both computational and experimental methods. First, I present a support vector machine (SVM) trained on naturally occurring disordered and ordered proteins, which is used to examine the contribution of various parameters to recognizing proteins that contain disordered regions. I show that a SVM that incorporates only amino acid composition has a recognition accuracy of 87+/-2%. This result suggests that composition alone is sufficient to accurately recognize disorder. Interestingly, SVMs using reduced sets of amino acids based on chemical similarity preserve high recognition accuracy. A set as small as four retains an accuracy of 84+/-2%; this result suggests that general physicochemical properties rather than specific amino acids are important factors contributing to protein disorder. Second, I build on the SVM analysis by examining the relationship of disorder propensity to sequence complexity. I graph the distributions of 40 amino acid peptides from both ordered and disordered proteins in disorder-complexity space. An analysis of the Swiss-Prot database shows that most peptides are of high complexity and relatively low disorder. However, there are also an appreciable number of low complexity-high disorder peptides in the database. In contrast, there are no low complexity-low disorder peptides. A similar analysis for peptides in the Protein Data Bank (PDB) reveals a much narrower distribution, with few peptides of low complexity and high disorder. In the case of the PDB, the bounds of the disorder-complexity distribution are well defined, and might be used to evaluate the likelihood that a peptide can be crystallized with current methods. I also examine disorder-complexity distributions of individual proteins and sets of proteins grouped by function. Among individual proteins, there are a variety of distributions that in some cases can be rationalized with regard to function. Groups of functionally related proteins are found to have distributions that are similar within each group, but show notable differences between groups. In addition, I use a pattern matching algorithm to search for proteins with particular disorder-complexity distributions. The results suggest that this approach might be used to identify relationships between otherwise dissimilar proteins. Thursday, May 11, 10:30-11:30am Abstract: An agent-based computer simulation has been created to study the complex network behavior of the immune system in a way that is not possible using a living system. It includes agent and signal representations of all of the basic elements of the immune system, and emulates both normal and pathological immune system behavior in a viral infection scenario. By designating the agents (cells) as nodes and meaningful interactions between the agents as links, the data generated during the simulated immune response demonstrates behavior like that of a scale-free network with dendritic cell agents as hubs. The average number of links for each agent type also correlates with their contribution to the success of the immune response. Modeling the immune system as a scale-free network creates a new window to information that can be used for development of new strategies for manipulating the immune response. Tuesday, May 23, 4:30-5:30pm Addictive drugs have been hypothesized to access the same
neurophysiological mechanisms as natural learning systems. These
natural learning systems can be modeled through temporal-difference
reinforcement learning (TDRL), which requires a reward-error signal
that has been hypothesized to be carried by dopamine. TDRL learns to
predict reward by driving that reward-error signal to zero. By adding
a noncompensable drug-induced dopamine increase to a TDRL model, a
computational model of addiction is constructed that overselects
actions leading to drug receipt. The model provides an explanation for
important aspects of the addiction literature and provides a theoretic
viewpoint with which to address other aspects. I will present how
this model explains important aspects of cocaine addiction. Models of Cellular Regulation Journal Club Wednesday, May 24, 3:30-4:30pm Thursday, May 25, 10:30-11:30am A Boolean dynamical system is a dynamical system whose state
space consists of vectors of fixed finite length of Boolean values 0 and
1. Such systems have important applications in mathematical biology as
models of gene regulatory networks. In studying these models, one would
like to have an efficient algorithm for deducing the dynamical properties
of the system from the formula for the updating function. In particular,
one would like to know if all attractors of the system are steady states.
Efficient algorithms for this problem are known if the updating function
is linear or each of its components is a monomial. In this talk we will
see that if the set of permissible updating functions is only slightly
broadened, the problem becomes computationally intractable, more
precisely, NP-hard. Tuesday, May 30, 4:30-5:30pm Models of Cell-Fate Journal Club Wednesday, May 31, 3:30-4:30pm Presentation materials: PPT Wednesday, June 7, 3:30-4:30pm Presentation materials: PPT Wednesday, June 14, 3:30-4:30pm Wednesday, June 21, 3:30-4:30pm Presentation materials: PPT Friday, June 30, 3:30-4:30pm Presentation materials: PPT Wednesday, July 5, 3:30-4:30pm Presentation materials: PPT Wednesday, July 12, 3:30-4:30pm Presentation materials: PPT Wednesday, July 19, 3:30-4:30pm Presentation materials: PPT Thursday, July 27, 3:30-4:30pm Presentation materials: PPT
Thursday, June 15, 1:00-2:00pm Abstract: It is now known that the majority of human genes have more than one promoter. In other words, the process of transcription can initiate at more than one place in the gene, and each of these locations has its own regulatory mechanisms including unique transcription factor binding sites. If we are to truly understand gene expression in different tissues and diseases, we must investigate the activity of individual promoters within the gene. To do this, we are creating a custom microarray in which the probes are specifically designed to measure the activity of alternative promoters in human genes. I will discuss the approach we're taking in the design of these probes.Monday, June 19 , 10:30-11:30am This work is based on the experimental observations of migration of neurons in the presence of a signalling molecule known as Slit, found in the migratory path. Experiments by Ward et al [J.Neurosci, 2003, 23(12):5170-5177] in vitro involved a circular tissue explant containing many neurons, placed in the proximity of a Slit source. In the absence of a Slit source, the neurons migrated away from the explant in a radially symmetric fashion. When Slit was present asymmetric distributions of neurons over time were observed, pointing to a possible inhibitory or repulsive role of Slit. We have used population models and individual nearest neighbour random walk models to match experimental observations of the cell distributions and individual cell tracks. This talk describes preliminary results of one and two-dimensional models. Title2: Multi-scale modelling and simulation of contact inhibited cell motility and proliferation Numerous cell migration processes exhibit travelling waves, from tumour cell invasion to wound healing. Using a wound healing assay, we model contact inhibited cell motility and cell proliferation with both continuum and discrete techniques. Imaging analysis shows that cells at the healing wavefront tend to be more motile compared to the cells behind the wavefront. This work has applications to the modelling of cell migration where diffusion and proliferation are the dominant mechanisms. We use both a modified Fisher equation, and an interacting population model to match simulation outputs with experimental data. Discrete simulations of reaction diffusion equations using continuous time random walkers will be also discussed.
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