Table of Contents Section of Computer Science

M.D. Anderson Cancer Center
Department of Biomathematics

Section of Mathematical Biology

Staff and Activities

Staff

Howard D. Thames, Ph.D. (1970) Rice University
Chief, Section of Mathematical Biology, Helen Buchanan and Stanley Joseph Seeger Professor of Biomathematics
Areas: Experimental and theoretical radiobiology

Birger Jansson, Ph.D. (1965) University of Stockholm
Biomathematician, Professor Emeritus of Biomathematics
Areas: Cell kinetics of ascites and solid tumors; dietary aspects of carcinogenesis; cancer prevention, geographical and quantitative epidemiology; information systems; operations research; queuing models and simulations

R. Allen White, Ph.D. (1970) University of Chicago
Professor of Biomathematics
Areas: Cell kinetics; computer sequence analysis of molecular biology sequence data; mathematical modeling

Stuart Zimmerman, Ph.D. (1964) University of Chicago
Chairman, Department of Biomathematics, Mattie Allen Fair Research Chair Professor of Biomathematics
Areas: Biomathematics; biomedical computing; image processing; computer karyotyping; biostatistics; cell kinetics

Susan L. Tucker, Ph.D. (1980) University of Michigan
Associate Professor of Biomathematics
Areas: Mathematical modeling; modeling in radiobiology; cell kinetics; data analysis

Arnout C.C. Ruifrok, Ph.D. (1987) University of Groningen
Assistant Professor of Biomathematics
Areas: Mathematical modeling of radiation response; planning and analysis of experimental studies

Mandri Obeyesekere, Ph.D. (1989) Texas Tech University
Instructor and Research Associate in Biomathematics
Areas: Numerical analysis; mathematical modeling; differential equations

Cyndi Smith, B.A. (1985) Rice University
Programmer Analyst II
Areas: Mathematical modeling; graphical applications; maintenance, distribution, and application of code for radiobiological analyses

Activities

Staff in this section are experienced in both applied mathematics and physical sciences. These skills are used for solution of problems in involving data acquisition, simulation, deterministic and stochastic modeling, and analysis. Active investigations include radiobiological studies, analysis and modeling in vivo cellular kinetics, DNA sequencing research, analytical epidemiology, and the analysis of structured models of interacting populations. In addition, a wide range of collaborative projects with both bench researchers and clinical investigators is supported.

Investigator-Initiated Research

  1. Mathematical Modeling of the Cell Cycle (Submitted Publications, #5). Dr. M.N. Obeyesekere and Dr. S.O. Zimmerman.
    It is well known that the cell cycle of eukaryotes is regulated by a cascade reaction of cellular proteins. Phosphorylation and dephosphorylation of complexes formed by members of the Cyclin and Cell division kinase (cdk) families comprise the backbone of this cascade reaction. In recent years, experimentalists have isolated some cellular proteins, which in their active or inactive form, will inhibit this cascade reaction. Furthermore, the assumption of a check point between the transitions from G2 phase to M phase and anaphase to metaphase has prompted scientists to search for transition regulatory proteins.
    Mathematical models have been developed to shed quantitative insight on the complex biochemical interactions encountered in the regulation of the cell cycle. Our initial work (M.N. Obeyesekere, S.L. Tucker, and S.O. Zimmerman, Biochemical and Biophysical Research Communications,184:782-789, 1992), focused on the regulation of the mitotic phase of the cell cycle and emphasized the reactions of cyclin B, cdc2 and their complex, MPF. This work established criteria for the existence of stable limit cycles and positivity of the reactants in the model. Because of the non-linear nature of the differential equations describing the behavior of these models, exact analytical solutions cannot be obtained. Quantitative analysis of the critical points of the solution is supplemented by numerical simulation of the dynamic system to give insights into its behavior. In subsequent work (Recent Publications, #18), regulation of the S-phase was incorporated along with M-phase. This involved the addition of a second cyclin, cyclin A, and a kinase, cdk2, and the regulatory proteins cdc25 and wee1. This resulted in a system of six non-linear ordinary differential equations. Besides describing the normal cell cycle, the model can account for cell cycle arrest when specific reactions are inhibited or over-expressed. The model also suggests that a depletion in activated cdc25 in embryonic cell would produce a transition from the rapid cell cycle of the embryonic cell to the slower cell cycle of the mature somatic cell.
    In a recent work, a mathematical model of cyclin E, cdk2 and Rb protein control of G1 phase of the human cell cycle is proposed. This model with three variables describes a normal as well as a neoplastic (retinoblastoma) cell cycle. Critical to this model is the assumption that unphosphorylated Rb inhibits the formulation of cyclin E/cdk2 complex. With this inhibition incorporated along with other known reactions in G1 phase, the model can exhibit periodic concentration profiles consistent with those seen in the cell cycle, and mimic known experiments such as cell-cycle arrest upon injection of transforming growth factor-ß, alpha-interferon, or D-erythro-sphingosine during G1 phase.
    These models can be used to identify the most sensitive reactions that may disrupt the cell cycle and influence the phase-specific activities, such as the rate of DNA synthesis. In this way, these models assist experimentalists in the design and conduct of more productive experiments.
  2. Changes in Expression of Cell-Cycle Regulatory Proteins at Altered Proliferation Stages in Mouse Tumors. Dr. A. Ruifrok, Dr. H.D. Thames, and Mrs. N. Hunter.
    In clinical as well as experimental studies, it has been shown that during fractionated irradiation treatment, tumors may respond by accelerated proliferation of clonogenic cells. An extensive series of fractionation treatments has been performed using the mouse OCA tumor to determine the time of onset and rate of accelerated proliferation. These experiments are extended to the mouse MCA4 and SANH tumors, to study accelerated proliferation in tumors of different histological origin. The expression of a number of growth-factors at different proliferation stages is studied using immunohistochemistry, SDS-PAGE and Western blotting. The study is focused on two groups of growth-factors: those directly involved in proliferation (EGF, TGF-alpha, FGF, TFG beta, P27), and those involved in cell-loss (P53, P21, bcl-2, bax). These studies may provide information about the mechanisms of growth regulation during radiation treatment. This information will be used to develop a mathematical model of the accelerated proliferation during fractionated irradiation treatments.
  3. Mechanisms of Compensatory Increases in Clonogen Production Rate in Tumors and Hierarchical Normal Tissues. Dr. H.D. Thames, Dr. A.C.C. Ruifrok, and Dr. L. Milas.
    Self-renewing tissues such as the gut, skin, and bone marrow respond to drug- or radiation-induced cell depletion by a compensatory increase in the net rate of clonogen production, and the same is true of some types of tumors. Changes in the clonogen production rate also occur in response to changes in numbers of end-stage differentiated cells. This rate is, broadly speaking, dependent on two factors i.e. the proliferation rate (reflecting the growth fraction and cell cycle time), and the differentiation probability following each division. A further consideration is the rate of loss of end-stage differentiated cells. Our research is aimed at ascertaining which of these factors is involved in compensatory responses, and whether the change is locally or systemically mediated. Answers to these questions would provide sufficient understanding of the compensatory response that it could be mathematically modelled. The experimental approach is to establish in vivo models where the timing and extent of the compensatory response can be quantified, and to correlate changes in labelling index and expression of growth factors with the response.
    Our results to date can be summarized as follows. (1) In normal self-renewing tissues (murine skin and gut) the clonogen production rate is increased when the exfoliation rate of end-stage differentiated cells is artificially increased. This is accompanied by an increased proliferation rate (increased labelling index) but not by an increase in absolute stem-cell number, with the implication that the differentiation probability is unchanged under this type of perturbation. Conversely, radiation depletes the number of cycling cells but has no direct effect on differentiated noncycling cells. Here it was observed that the proliferation rate increased and that the differentiation probability underwent a transitory decrease during the recovery period. The expression of a number of growth factors at different times after these perturbations is being studied using immunohistochemistry, SDS-PAGE gels and Western blotting. (2) In a slow-growing ovarian tumor model there is a delayed response to daily irradiation, such that nine days after inception of treatments; the rate of tumor clonogen production increases to three times faster than in the unperturbed, growing 8 mm tumor. This occurs concomitantly with a reduction in the radiation-induced apoptotic index to control levels, suggesting that the increased rate of clonogen production is mediated by a reduced rate of cell loss. The expression of growth factors before and after accelerated clonogen production is under study.
  4. The Clinical Significance of Ratios of Biological Parameters. Dr. H.D. Thames, Dr. B.M. Dubray, and Dr. S.M. Bentzen.
    There is considerable heterogeneity among individual patients in terms of both malignant and normal-tissue radiobiological characteristics, such as radiosensitivity and proliferative activity. A basic question is: assuming that the mathematical model is correct, can the important radiobiological parameters that describe the response of tissues and tumors to radiation be deduced from clinical data? In general the answer is no, on account of a variety of confounding effects including between-patient heterogeneity, patient selection, and underlying patterns of dose prescription. The hypothesis underlying this study is that although estimates of parameters describing the effects of treatment variables like total dose or overall time are too small to be interpreted radiobiologically (because of confounding effects), estimates of their ratios may very well be biologically reasonable. Examples of these ratios include (1) the ratio of the overall time coefficient to the total dose coefficient (estimate of the dose per day required to offset proliferation), (2) the ratio of the coefficients of total dose and total dose * dose per fraction (estimate of fractionation sensitivity), and (3) the ratio alpha/(2*ß/mu), which may be taken as a quantifier of the strength of the dose-rate effect in brachytherapy treatments where low to intermediate dose rates are used. It is known that ratio estimates are insensitive to model misspecification; we have studied the effects of interpatient heterogeneity. The general result is that estimates of ratios are likely to be much closer to biologically realistic values, and to be less influenced by patient heterogeneity, than estimates of individual coefficients. This is a useful outcome for at least two reasons. First, it is nice to have biologically reasonable estimates whenever tolerance calculations are attempted, as opposed to those conditioned on particular data sets. The second reason relates to the problematic mouse-to-man extrapolation. Although very little is known about human radiosensitivity, we have shown that the trend toward larger alpha/ß ratios for early than for late-effects tissues occurs in humans and in rodents, and in fact their absolute values are similar. This lends some hope for the validity of clinically derived estimates of ratios that are likely different in rodents and humans e.g. the dose recovered each day by proliferation of tumor clonogens.
  5. A Model for the Probability of Tumor Cure After Fractionated Radio-therapy (Submitted Publications, #10). Dr. S.Tucker.
    Mathematical models for the probability of tumor control are used to analyze clinical radiotherapy data and to determine the expected efficacy of new treatment schedules. In existing models, the expected number of surviving cells capable of tumor regeneration (clonogens) is modeled as a function of the initial clonogen number, the amount of cell killing during treatment, and the rate of cellular proliferation during treatment. The tumor cure probability, i.e. the probability that a tumor contains no clonogens at the end of treatment, is then calculated by assuming that the distribution of clonogens from tumor to tumor is governed by Poisson statistics.
    It is now recognized, however, that surviving clonogens do not in fact follow a Poisson distribution when proliferation occurs during treatment. Instead, surviving tumors contain more clonogens per tumor than would be expected in the absence of proliferation. As a consequence, the Poisson model, which is based on the correct average number of surviving clonogens per tumor, underestimates the size of the cured fraction.
    A modeling project is currently underway to estimate the magnitude of the error in the Poisson model of tumor cure for clinically relevant treatment schedules and under plausible assumptions regarding the biology of the tumor. Preliminary results suggest that the absolute error in the Poisson estimate of the cure probability can approach 100% in some settings. In addition, efforts are being directed at developing an accurate alternative to the Poisson model of tumor cure. A candidate model has been identified, and studies are presently being performed to verify the accuracy of this model.
  6. Estimation of the Spatial Distribution of Target Cells in Mouse Lung (Submitted Publications, #9). Dr. S. Tucker.
    Normal tissue complications in radiotherapy tend to increase with the volume of tissue irradiated. This volume effect has important implications for the treatment of malignant disease, since the tumor dose could potentially be boosted well above conventional treatment levels, with the possibility of an improved cure rate, if the volume of irradiated tissue were small enough.
    The present study was motivated by the detailed studies of Dr. Travis and coworkers in the Department of Experimental Radiotherapy concerning the volume effect in mouse lung. Those studies demonstrated that the lung is spatially heterogeneous in its sensitivity to radiation, with treatment of equal subvolumes producing markedly different responses depending on the location in the lung of the irradiated subvolume. The existing volume-effect models, which do not distinguish between equally-sized subvolumes located in different anatomical regions of the lung, were not adequate for the analysis of these data.
    Therefore, a new mathematical model has been derived, describing the probability of lung response as a function not only of dose and volume of lung irradiated, but also of the location in the lung of the irradiated subvolume. The model is based on an uneven distribution of the target cells responsible for lung function, and a fit of the model to the data has provided an estimate of the spatial distribution of those cells. Studies are currently underway to explore the implications of the model, including a comparison of the effects of qualitatively different exposure patterns (e.g. "a lot to a little" vs. "a little to a lot") and an investigation of the threshold volumes predicted to exist in various subregions of the lung.
  7. Studies Concerning Predictive Assays of Fibroblast Radiosensitivity (Submitted Publications, #8). Dr. S. Tucker.
    Individual differences in the normal-tissue reactions of patients treated with radiotherapy are now known to be due, at least in part, to individual differences in intrinsic radiosensitivity. Since conventional treatment schedules are designed to keep injury to normal tissues within acceptable limits, the existence of interpatient heterogeneity implies that the doses used in conventional treatment are determined largely by the most sensitive patients. This raises the possibility that predictive assays could be used prior to treatment to identify the most radiosensitive patients, so that the doses to the remaining patients could be escalated, with the potential for an improved tumor control rate.
    Colleagues in the Departments of Clinical and Experimental Radiotherapy have conducted studies confirming the correlation between fibroblast radiosensitivity and the severity of late tissue reactions in radiotherapy patients. However, the strength of these correlations is limited in part by the precision of the radiosensitivity assay. Based on this observation, mathematical modeling studies have been initiated to investigate the extent to which the correlation between fibroblast radiosensitivity and clinical complications would be expected to improve as the precision of the assay is sharpened. These studies will guide the future design of the in vitro sensitivity assay, by indicating the number of replicate cell cultures required to reach the necessary precision. Modeling studies are also underway to estimate the expected degree of improvement in the therapeutic ratio that would be achievable using a predictive assay of normal tissue response.
  8. Research in Computational Analysis of Sequence Data. Dr. R. Allen White.
    While most of the activities in the Core Facility for Analysis of Macromolecular Sequence Data involve education and support functions, several projects are ongoing. Most researchers in the field of molecular biology are extremely capable in utilizing the computer methodology required in the field. However, special problems often arise that cannot be solved using standard procedures.
    One example of this work is a collaboration with Dr. R. Arlinghaus of Molecular Pathology and his former Ph.D. student, Dr. M. Meyer. The problem that arose is to identify the portion of a protein sequence generated by the moloney murine leukemia virus necessary for targeting the virus into the nucleus. In fact there is a family of these type C retroviruses, all having relatively conserved portions of proteins. The problem then becomes two fold. First, it is necessary to identify the region responsible for nuclear targeting and second to characterize a common motif for the region. A search of the literature shows that in some phage that the nuclear targeting sequence consists of four residues; however, in other sequences, it appears that the actual targeting sequence is at least bi- and possible tripartite.
    Computationally, a crude method was chosen to search all peptide sequences using possible motifs to determine whether potential targeting sequences are found randomly. In fact, a study of a ten amino acid sequence identified all but one known type C retroviruses without identifying any other sequence. Using a less restrictive search algorithm still found only one other family of similar sequences. Thus, we tentatively conclude that the particular sequence is both conserved within the family of viruses and that the sequence is quite rare. Currently, we are using statistical software to analyze the sequence to determine the characteristics of the possible targeting region.
    A second area of interest is based on a longtime collaboration with Dr. Jean Numa Lapeyre, who is currently working with a commercial firm. In this study we are examining potential primers for differential display of mRNA. In differential display of mRNA, a few primers are prepared and polymerase chain reactions are used to determine all the expressed mRNAs in a cell line. The question arises as to what should be the optimal family of primers used to select all mRNAs without at the same time obtaining multiple copies of the same expressed gene. We have been able to show that the distribution of possible sites in expressed mRNAs is well fitted by a geometric distribution and have developed guidelines to select the minimal numbers of primers based on the frequency of nucleotide tetramers and pentamers in coding regions of DNA.
    These diverse studies exemplify the range of problems we have encountered in this area. We continue to discover new and important applications for the analysis of macromolecular sequence data.
  9. Dynamic Studies in Cell Proliferation. Dr. R. Allen White.
    This research studies the proliferation properties of populations of cells both in tumors and in normal tissues. There are multiple phases to this study, beginning with the identification of subpopulations of cells through the use of analytical cytometry to provide quantitative estimates of the fractions of cells in each subpopulation identified in the total population. Secondly, the fractions of cells in each subpopulation are interpreted in terms of the movement of these cells through each subpopulation. Finally, the movement and fractions of cells in each subpopulation are studied for their diagnostic and prognostic significance in the response of the total population to cancer therapy.
    The initial identification of cell subpopulations is based on studying data which is commonly obtained by flow cytometry, wherein a tissue sample is disaggregated into separate cells or nuclei, stained for various biochemical constituents such as DNA, protein or a cell surface marker and the distribution of these constituents in the population measured. For the past several years, we have been most interested in measuring the DNA content of a cell along with a thymidine analogue such as bromodeoxyuridine or iododeoxyuridine that is incorporated by cells synthesizing DNA. By measuring the distribution of the cells with incorporated thymidine analogue as a function of DNA content, it is possible to measure the progression of those cells through the division cycle and hence obtain estimates of the proliferation properties of the cells.
    Most recently we have been able to identify a group of cells that are undergoing the nuclear fragmentation characteristic of cell death by apoptosis. Even cells that have incorporated the thymidine analogue can be identified as undergoing fragmentation. Current research is based on developing objective, automatic procedures for estimating the fraction of cells in this subpopulation from the bivariate DNA-thymidine analogue histogram. Towards this end we have been developing a new procedure for analyzing the DNA distribution of the cell population. Traditional methods have always assumed that the DNA distribution consisted of two peaks, corresponding to the cells with G1 and G2 + M DNA content, with some smooth curve between the two to account for the cells synthesizing DNA. In addition, we are now including a lognormal distribution to characterize the fragmenting cells. The choice of a lognormal distribution is based on theoretical studies on fragmentation patterns. Historically, the lognormal distribution was discovered to provide a useful fit to the distribution of fragment sizes in gold bearing ores from mines and the distribution can be generated by assuming a breaking of the nuclear material into fragments depending on the size of the remaining nuclear material.
    The second step in the analysis is to study the effects of this measured cell loss pattern on the estimates of cell proliferation. Previously, the parameter of choice was the potential doubling time, or Tpot, which was calculated by relating the fraction of cells synthesizing DNA to the time required in duplicating DNA preparatory to cell division. Ideally Tpot would be the time required for a population to double in size if there were no cell loss, but with some fraction of the cells not dividing. In fact, as seen in the accompanying figure, the value of Tpot is relatively insensitive to the fraction of cells that will be lost to the population due to fragmentation. Figure 1 shows the effect of decreasing numbers of cells remaining in the population for proliferation. (The number A, the so-called daughter factor, ranges between 1 and 2 for a growing population). In contrast, the predicted doubling time that we call TDmin can increase dramatically with decreasing numbers of cells remaining in the population. Strictly TDmin will not be the true doubling time unless all cells lost to the population pass through the fragmentation pathway, but it does provide a better estimate of the true doubling time of the population than that provided by Tpot. Most importantly, this new method of analysis provides a direct, predictive method for estimating the doubling time of any given tumor.
    With both an objective algorithm for computing a fraction of fragmenting cells and a theoretical basis for including an analysis of a cell death pathway, we are beginning a series of studies on cell death following treatment. In a recent study in collaboration with colleagues in the Department of Experimental Radiotherapy, we examined the proliferation of cells following treatment with taxol. For times up to three days following administration of taxol, we find that we can characterize both the numbers of dying cells and the kinetics of cells through the division cycle for a tumor. Thus for the first time, we can begin to monitor the proliferation of tumors following therapeutic treatment.
    Drs. Alan Pollack, Marvin Meistrich, Tyvin Rich, and Nicholas Terry all continue to provide the exquisite technical expertise to make this research possible.
    Figure 1

Scientific Consultations and Collaborations

  1. Radiation-induced cell death and senescence in mouse fibroblasts depending on p53 status. Dr. A. Ruifrok, Dr. G. Lozano, Dr. W. Brock, and Dr. H.D. Thames.
  2. Changes in expression of growth factors in regenerating mouse gut. Dr. A. Ruifrok, Ms. K. Mason, and Dr. H.D. Thames.
  3. Modelling the seeding-density dependent plating efficiency in in vitro experiments. Dr. A. Ruifrok, Dr. J. Pomp, Ms. C. Smith, Dr. W. Brock, Dr. H.D. Thames.
  4. Engraftment of donor bone marrow in relation to total body irradiation dose in MHC compatible and MHC incompatible allogeneic and semi-allogeneic mice. Dr. J.D. Down and Dr. H.D. Thames.
  5. Tumor volume as predictor of radiocurability. Dr. C. Johnson, Dr. S.M. Bentzen, and Dr. H.D. Thames.
  6. The volume effect in canine lung. Dr. E.L. Gillette and Dr. H.D. Thames.
  7. A hierarchical tissue model for radiation sensitivity and latent-time of response in the rat cervical spinal cord. Dr. A. Ruifrok, Dr. A.J. van der Kogel, Dr. S.L. Tucker, and Mrs. K. Russell.
  8. Volume effects in the radiotherapy of normal tissues. Dr. E.L. Travis and Dr. S.L. Tucker.
  9. Accuracy of in vitro assays of fibroblast survival in predicting the severity of late normal tissue complications in radiotherapy patients. Dr. W.A. Brock, Dr. F.B. Geara, Dr. L.J. Peters, and Dr. S.L. Tucker.
  10. A model for the latent-time to spinal cord injury after retreatment with radiotherapy. Dr. A. Ruifrok and Dr. S.L. Tucker.
  11. Measurement of oxygen levels in murine tumors using the Eppendorf electrode technique. Dr. C. Milross and Dr. S.L. Tucker.
  12. Prognostic factors for nodal positivity in patients with tubular carcinoma of the breast. Dr. S.E. Singletary, Dr. S.L. Tucker, and Dr. D. Winchester.
  13. Studies of tumors using in situ hybridization techniques. Dr. A. El-Naggar and Dr. S.L. Tucker.
  14. Prognostic factors for survival among patients with salivary gland tumors. Dr. A. El-Naggar and Dr. S.L. Tucker.
  15. Comparison of apoptosis and survival among neutrophils in CML patients and normal donors. Dr. H. Gisslinger, Dr. R. Kurzrock, and Dr. S.L. Tucker.
  16. Dynamic studies in cell proliferation. Dr. R.A. White and Dr. N.H.A. Terry.
  17. Extension of radiotherapy research. Dr. L.J. Peters and Dr. R.A. White.
  18. Cellular kinetics of normal tissues. Dr. N.H.A. Terry and Dr. R.A. White.
  19. Bladder preservation for early muscle-invasive cancer. Dr. A.P. Pollack and Dr. R.A. White.
  20. Genetic basis of radiation induced fibrosis. Dr. E.L. Travis and Dr. R.A. White.
  21. Cooperative family registry for epidemiologic studies of breast cancer. Dr. C. Amos and Dr. R.A. White.
  22. Identification of nuclear targeting sequences in RNA viruses. Dr. R.A. White, Dr. R. Arlinghaus, and Dr. M. Meyers.
  23. Use of non-standard amino acids for differential display of mRNA. Dr. N. Lapeyre and Dr. R.A. White.
  24. Morphine-augmented heptobiliary scintigraphy (MA-HBS). Dr. G. Wong and Dr. S. Zimmerman.
  25. The treatment of post-operative nausea and vomiting after mastectomy for breast cancer using an acupressure wrist band. Dr. C. Dai and Dr. S. Zimmerman.
  26. Cefonicid wound infection study. Dr. P. Mansfield and Dr. S. Zimmerman.
  27. Hepatic lymph node metastases analysis. Dr. H. Libshitz and Dr. S. Zimmerman.
  28. Harvesting strategies in outpatient autologous bone marrow transplanation. Dr. K.A. Dicke and Dr. S. Zimmerman.

CCSG Shared Resource:
Computer Analysis of
Macromolecular Sequence Data

This service facility, funded by the Cancer Center Support Grant, provides support for researchers needing aid in computational biology. This Facility provides a central location in the Institution for maintaining macromolecular sequence data bases and the programs needed for accessing those data bases. Programs are maintained for manipulating this and similar data generated by users in this Institution. In addition a series of programs are provided for use in the analysis of genetic sequence data and structural studies in macromolecular sequence data.

The use of the facility is best seen in the accompanying graph of the number of hours of computer time used monthly between January 1992 when computing began on the current Solbourne file server and December 1994. The scale is in Central Processing Unit (CPU) time and represents only the time spent in direct computation. Currently usage is approaching 200 CPU hours per month and this growth is expected to continue with the increasing size of sequence data bases and increasing requirement of researchers for computer tools for large scale data analysis. There are now over 400 users with individual accounts from 26 separate institutional departments.

The facility provides both formal instruction through the course "Introduction to the Analysis of Genetic Sequence Data" taught in the Graduate School of Biomedical Sciences and personal tutorials for users. The course is taught in an unusual manner, with lectures on Monday being of general interest and advertised to all users, while the lectures on Wednesday and Friday discuss advanced features and are aimed specifically for students taking the course and others concerned with obtaining more detailed information on each topic. In addition, personnel from the facility speak to laboratory groups and hold workshops on specific areas of interest.

The facility publishes a weekly newsletter to inform users about new features and changes in the computer programs available. The weekly newsletter also provides tips, hints and short reports on improved methods in sequence analysis. In the past year the facility has established a World Wide Web home page with links to commonly used computer sites with public access for users of the facility, as well as links to past issues of the newsletter.

On the research level, the facility also provides long term collaborative relationships with various researchers in the institution. These relationships described under Dr. White's collaborations are in areas requiring novel methods of analysis and computationally intensive procedures, that are not generally accessible to the normal user.

Recent Publications

  1. Brock, W.A., Tucker, S.L., Geara, F., Turesson, I., Wike, J., Nyman, J., and Peters, L.J. Fibroblast radiosensitivity versus acute and late normal skin responses in patients treated for breast cancer. International Journal of Radiation, Oncology, Biology, Physics, in press.
  2. Coucke, P.A., Schmid, C., Balmer, A., Mirimanoff, R.O., and Thames, H.D. Hypofractionation in retinoblastoma: An increased risk of retinopathy. Radiotherapy and Oncology, 28:157-161, 1993.
  3. Dubray, B., Bataini, J.P., Bernier, J., Thames, H.D., Lave, C., Asselain, B., Jaulerry, C., Brunin, F., and Pontvert, D. Is reseeding from the primary a plausible cause of node failure? International Journal of Radiation, Oncology, Biology, Physics, 25:9-15, 1993.
  4. Gasinska, A., Dubray, B., Hill, S.A., Denekamp, J., Thames, H.D., and Fowler, J.F. Early and late injuries in mouse rectum after x-ray and neutron irradiation. Radiotherapy and Oncology, 26:244-253, 1993.
  5. Heaton, K.M., Rippon, M.B., El-Naggar, A., Tucker, S.L., Ross, M.I., and Balch, C.M. Prognostic implications of DNA index in patients with stage III cutaneous melanoma. American Journal of Surgery, 166:648-652, 1993.
  6. Higashikubo, R., White, R.A., and Roti Roti, J.L. Flow cytometric BrdUrd-pulse-chase study of heat-induced cell-cycle progression delays. Cell Proliferation, 26:337-348, 1993.
  7. Hug, V., Polyzos, A., Tucker, S., and Thames, H. The clonogenic growth of advanced breast tumor lesions adds no value to that of established clinical prognosticators for survival. British Journal of Cancer,67:222-225, 1993.
  8. Imrey, P.B., Chilton, N.W., Pihlstrom, B.L., Proskin, H.M., Kingman, A., Listgarten, M.A., Zimmerman, S.O., et.al. Proposed guidelines for American Dental Association acceptance of products for professional, non-surgical treatment of adult periodontitis. Journal of Periodontal Research, in press.
  9. Imrey, P.B., Chilton, N.W., Pihlstrom, B.L., Proskin, H.M., Kingman, A., Listgarten, M.A., Zimmerman, S.O., et.al. Recommended revisions to American Dental Association guidelines for acceptance of chemotherapeutic products for gingivitis control. Journal of Periodontal Research, 29:299-304, 1994.
  10. Jiang, G.L., Tucker, S.L., Guttenberger, R., Peters, L.J., Morrison, W.H., Garden, A.S., Ha, C.S., and Ang, K.K. Radiation-induced injury to the visual pathway. Radiotherapy and Oncology, 30:17-25, 1994.
  11. Johnston, D.A., Tang, K.S., and Zimmerman, S. Band features as classification measures for G-banded chromosome analysis. Computers in Biology and Medicine, 23:115-129, 1993.
  12. Liao, Z.X., Travis, E.L., and Tucker, S.L. Damage and morbidity from pneumonitis after irradiation of partial volumes in mouse lung. International Journal of Radiation, Oncology, Biology, Physics, in press. 13
  13. Mason, K.A., Thames, H.D., Ochran, T.G., Ruifrok, A.C.C., and Janjan, N. Comparison of continuous and pulsed low dose rate brachytherapy: Biological equivalence in vivo. International Journal of Radiation, Oncology, Biology, Physics, 28:667-671, 1994.
  14. Milas, L., Nakayama, T., Hunter, N., Jones, S., Tsung-min, L., Yamada, S., Thames, H., Peters, L. Dynamics of tumor cell clonogen repopulation in a murine sarcoma treated with cyclophosphamide. Radiotherapy and Oncology, 30:247-253, 1994.
  15. Obeyesekere, M.N., Arbogast, T., and Wheeler, M.F. Numerical methods for the simulation of flow in root-soil systems. SIAM Journal on Numerical Analysis, 30:1677-1702, 1993.
  16. Obeyesekere, M.N., Tucker, S.L., and Zimmerman, S.O. Cyclic models for MPF activity and cyclin concentration. IN: Proceedings of the Third International Conference on Mathematical Population Dynamics, June 1-5, 1992, Pau, France, in press.
  17. Obeysekere, M.N., Tucker, S.L., and Zimmerman, S.O. Mathematical models for regulation of the cell cycle via the concentrations of cellular proteins. IN: Proceedings of the Third International Conference on Mathematical Population Dynamics, June 1-5, 1992, Pau, France, in press.
  18. Obeyesekere, M.N., Tucker, S.L., and Zimmerman, S.O. A model for regulation of the cell cycle incorporating cyclin A, cyclin B, and their complexes. Cell Proliferation, 27:105-113, 1994. (return to Research #1)
  19. Pollack, A., Terry, N.H.A., Wu,C.S., Wise, B.M., White, R.A., and Meistrich, M.L. Specific staining of iododeoxyuridine and bromodeoxyuridine in tumors double-labelled in vivo: A cell kinetic analysis. Cytometry, in press.
  20. Pollack, A., Terry, N.H.A., White, R.A., Cao, S., Meistrich, M.L., and Milas, L. Proliferation kinetics of recruited cells in a mouse mammary carcinoma. Cancer Research, 54:811-817, 1994.
  21. Pollack, A., White, R.A., Cao, S., Meistrich, M.L., and Milas, L. Proliferation kinetics of recruited cells in a mouse mammary carcinoma. Cancer Research, in press.
  22. Pollack, A., White, R.A., Cao, S., Meistrich, M.L. and Terry, N.H.A. Calculating potential doubling time using monoclonal antibodies specific for two halogenated thymidine analogues. International Journal of Radiation, Oncology, Biology, Physics, 27:1131-1139, 1993.
  23. Robbins, M.E. C., Stephens, C.L., Thames, H.D., Gray, K.N., Peters, L.J. and Ang, K.K. Radiation response of the monkey kidney following contralateral nephrectomy. International Journal of Radiation, Oncology, Biology, Physics, 30:347-354, 1994.
  24. Roberts, D.B., Travis, E.L., and Tucker, S.L. Interleukin-1 dose, mouse strain, and end point as they affect protection of mouse jejunum. Radiation Research, 135:56-63, 1993.
  25. Rodriguez, M.A., Fuller, L.M., Zimmerman, S., Allen, P.K., Brown, B.W., Munsell, M.F., Hagemeister, F.B., McLaughlin, P., Velasquez, W.S., Swan, F., and Cabanillas, F.F. Hodgkin's disease: study of treatment intensities and incidences of second malignancies. Annals of Oncology, 4:125-131, 1993.
  26. Ruifrok, A.C.C., Kleiboer, B., and van der Kogel, A.J. Effect of intraspinal cytosine arabinoside on re-irradiation tolerance of young and adult rats. European Journal of Cancer, 29:1766-1770, 1993.
  27. Ruifrok, A.C.C., Kleiboer, B., and van der Kogel, A.J. Repair kinetics of radiation damage in the developing rat cervical spinal cord. International Journal of Radiation Biology, 63:501-508, 1993.
  28. Ruifrok, A.C.C., Mason, K.A., Hunter, N. and Thames, H.D. Changes in the radiation sensitivity of mouse skin during fractionated and prolonged treatment. Radiation Research,139:334-343, 1994.
  29. Ruifrok, A.C.C., Mason, K.A. and Thames, H.D. Response to letter of Drs. Huang, Lin and Schmidt-Ullrich (letter to the editor). International Journal of Radiation, Oncology, Biology, Physics, 31: 206, 1995.
  30. Ruifrok, Arnout C. C. and Thames, H.D. Comparing cell survival estimated from in vivo and in vitro data: Beware in vivo heterogeneity (letter to the editor). Experimental Hematology, 22:535, 1994.
  31. Ruifrok, A.C.C., Stephens, L.C., and van der Kogel, A.J. Radiation response of the rat cervical spinal cord after irradiation at different ages: tolerance, latency, and pathology. International Journal of Radiation, Oncology, Biology, Physics, 28:73-79, 1994.
  32. Ruifrok, A.C.C. and van der Kogel, A.J. A "reappraisal" of the LQ model for the understanding of dose-fractionation in radiotherapy. International Journal of Radiation, Oncology, Biology, Physics, 25: 926-927, 1993.
  33. Seong, D.C., Song, M.Y., Henske, E.P., Zimmerman, S.O., Champlin, R.E., Deisseroth, A.B., and Siciliano, M.J. Detection of Philadelphia translocation in interphase CML cells with radiation hybrid painting probes made by Inter-Alu PC. Blood, 83:2269-2273, 1994.
  34. Stephens, L.C., Robbins, M.E.C., Peters, L.J., Thames, H.D., Price, R.E., Johnston, D.A., and Ang, K.K. Radiation nephropathy in the rhesus monkey: Morphometric analysis of glomerular and tubular alterations. International Journal of Radiation, Oncology, Biology, Physics, in press.
  35. Taylor, J.M.G., Tucker, S.L., and Thames, H.D. The probability of tumor cure: Response to comments by Dr. Yakovlev (letter to the editor). Radiation Research, 134:121-122, 1993.
  36. Thall, P.F., Jacoby, D., Zimmerman, S.O. Estimating genomic category probabilities from fluorescent in situ hybridization counts with misclassification. Applied Statistics, in press. (return to Biostatistics, Research #25)
  37. Thames, H.D., Peters, L.J., and Ang, K.K. Accelerated Fractionation. IN: H.P. Beck Bornholdt (ed.), Medical Radiology: Current Topics in Clinical Radiobiology in Tumors, pp. 1-10. Springer-Verlag: Berlin Heidelberg,1993.
  38. Thames, H., and Rich, T. Geänderte Fraktionierungsstrategien in der Strahlen-therapie gastrointestinaler Tumoren. GBK Fortbildung aktuell, 64:31-32, 1994.
  39. Tucker, S.L. Inference from tumor recurrence data regarding the range of individual differences in tumor-cell radiosensitivity and proliferation rate. IN: B. Paliwal, J.F. Fowler, D. Herbert, and T.J. Kinsella (eds.), Prediction of Response in Radiation Therapy. Radiosensitivity and Repopulation, p. 53. American Institute of Physics, Inc.: New York 1993.
  40. Van der Kogel, A.J. and Ruifrok, A.C.C. Calculation of isoeffect relationships. IN: G.G. Steel (ed.), Basic Clinical Radiobiology, pp. 72-80. Edward Arnold: Sevenoaks, England, 1993.
  41. Van Os, R., Thames, H.D., Konings, A.W.T., and Down, J.D. Radiation dose fractionation and dose-rate relationships for long-term repopulating hemopoietic stem cells in a murine bone marrow transplant model. Radiation Research, 136:118-125, 1993.
  42. Van Rongen, E., Thames, Jr., H.D., and Travis, E.L. Recovery from radiation damage in mouse lung: interpretation in terms of two rates of repair. Radiation Research, 133:225-233, 1993.
  43. Velasquez, W.S., McLaughlin, P., Fuller, L.M., Allen, P.K., Tucker, S.L., Swan, F., Rodriguez, M.A., Hagemeister, F.B., and Cabanillas, F.F. Intermediate-grade lymphomas treated with cyclophosphamide-doxorubicin-vincristine-prednisone bleomycin alternated with cyclophosphamide-methotrexate-etoposide dexamethasone: Application of prognostic models to data analysis. Cancer, 73:2408-2416, 1994.
  44. Velasquez, W.S., McLaughlin, P., Tucker, S., Hagemeister, F.B., Swan, F., Rodriguez, M. A., Romaguerra, J., Rubenstein, E., and Cabanillas, F. ESHAP- An effective chemotherapy regimen in refractory and relapsing lymphoma: A 4 year follow up. Journal of Clinical Oncology, 12:1169-1176, 1994.
  45. White, R.A. Flow Cytometry Data Analysis Basic Concepts and Statistics, by James V. Watson, Cambridge University Press, New York, 1992, (book review). Bulletin of Mathematical Biology, 56:168-170,1994.
  46. White, R.A. and Kallman, R.F. The proliferation of kinetics of experimental tumors after irradiation. Seminars in Radiation Oncology, 3:84-89, 1993.
  47. White, R.A., Pollack, A., and Terry, N.H.A. Simultaneous cytokinetic measurement of aneuploid tumors and associated diploid cells following continuous labelling with chlorodeoxyuridine. Cytometry, 26:311-319, 1994.
  48. White, R.A., Pollack, A., Terry, N.H.A., Meistrich, M.L., and Cao, S. Double labelling to obtain S phase subpopulations: Application to determine cell kinetics of diploid cells in an aneuploid tumor. Cell Proliferation, 27:123-137, 1994.

Submitted Publications

  1. Duvic, M., Lemak, N.A., Redman, J., Eifel, P.J., Tucker, S.L., Cabanillas, F.F., and Kurzrock, R. Combined modality therapy for cutaneous T cell lymphoma.
  2. Fisk, B., Flytzanis, C.N., Tucker, S., and Ioannides, C.G. Characterization of T-cell receptor Vß repertoire in ovarian tumor infiltrating lymphocytes: Dissimilar TCR Vß frequencies in T1L from different sites of the same tumor.
  3. Gisslinger, H., Kurzrock, R., Wetzler, M., Tucker, S., Kantarjian, H., Robertson, B., and Talpaz, M. Apoptosis in chronic myelogenous leukemia: studies of stage-specific differences.
  4. Liao, Z.X., Travis, E.L., and Tucker, S.L. Unilateral nephrectomy 24 hours after irradiation does not precipitate latent renal functional damage.
  5. Obeyesekere, M.N., Herbert, J.R., and Zimmerman, S.O. A model of the G1 phase of the cell cycle incorporating cyclin E/cdk2 complex and retinoblastoma protein. (return to Research #1)
  6. Robbins, M.E.C., Stephens, L.C., Thames, H.D., Price, R.E., Peters, L.J., and Ang, K.K. The effect of unilateral nephrectomy on the subsequent radiation response of the monkey kidney.
  7. Rodriguez, M.A., Tucker, S., Hill, D., Swan, F., and Cabanillas, F., Socioeconomic status correlates with stage of disease at presentation in lymphoma patients.
  8. Tucker, S.L., Geara, F.B., Peters, L.J., Brock, W.A. Influence of dose and fibroblast radiosensitivity on the incidence of normal-tissue complications: Implications for predictive assays. (return to Research #7)
  9. Tucker, S.L., Liao, Z.X., and Travis, E.L. Estimation of the spatial distribution of target cells in mouse lung based on response to partial volume irradiation. (return to Research #6)
  10. Tucker, S.L. and Taylor, J.M.G. A new model for the probability of tumor cure after fractionated radiotherapy. (return to Research #5)
  11. van Limbergen, E., Dubray, B., Thames, H.D., and van der Schueren, E. Influence of total treatment time on local control rates of breast cancer after radiotherapy only.
  12. van Rongen, E., Thames, H.D., and Travis, E.L. Repair rate in mouse kidney after a dose of 3 Gy.
  13. Winchester, D., Tucker, S.L., and Singletary, S.E. Tubular carcinoma of the breast: risk factors for axillary nodal metastases and recurrence.

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