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clinical data [2]. Furthermore, various regimes of Denosumab and Pamidronate including combination treatments are explored based on the optimized model. (1). Marathe, A., M.C. Peterson, and D.E. Mager, Integrated Cellular Bone Homeostasis Model for Denosumab Pharmacodynamics in Multiple Myeloma Patients. The Journal of Pharmacology and Experimental Therapeutics, 2008. 326(2): p. 555-562. ⑵. Body, J.‐J., et al., A Study of the Biological Receptor Activator of Nuclear Factor‐KB Ligand Inhibitor, Denosumab, in Patients with Multiple Myeloma or Bone Metastases from Breast Cancer. Clin Cancer Res, 2006. 12(4): p. 1221‐1228OR-009 Computational approach towards promoter sequence comparison via TF mapping Using a new distance measure Meera A*, Lalitha Rangarajana, Savithri Bhat1 B.M.S College of Engineering, Bull temple road, Bangalore- 560 019, India Department of Computer Science, University of Mysore, Mysore, India, Author for correspondence, Tel : 91-080-26622130, Extn 3029, fax: 91-080-26614357, Email: savithri.bhat@gmail.com We propose a method for identifying TFBS in the given Promoter sequence and mapping the Transcription factors (TFs). The proposed algorithm searches the +1Transcription start site (TSS) for eukaryotic and prokaryotic sequences individually. The algorithm was tested with sequences from both eukaryotes and prokaryotes for at least 9 experimentally verified and validated functional TFs in promoter sequences. The order and type of TF binding to the promoter of genes encoding CMP enzyme was tabulated. A new similarity measure was devised for scoring the similarity between a pair of promoter sequences based on the number and order of motifs. Further, these were grouped in clusters considering the scores between them. The distances between each of the clusters in individual pathway was calculated and a phylogenetic tree was developed. This method is being further applied to other pathways such as lipid and amino acid biosynthesis to retrieve and compare experimentally verified and conserved TFBs. Keywords: Promoter sequence, Phylogeny, Database, Central Metabolic Pathway, pattern matching, Transcription factors (TFs), Transcription factor binding sites(TFBS), similarity measure, Cluster. OR-010 MultiScale Modeling of Immunotoxin Efficacies in Vascular Tumors Kevin C. Chen , and Liang Li Shantou University, Shantou, Guangdong, 515063, China One of the major setbacks in cancer chemotherapy is the inability to distinguish malignant cells from normal ones. Consequently, any anticancer therapy that can target only malignant cells would be a significant improvement. The use of recombinant immunotoxins (RITs) to target antigens uniquely or overwhelmingly expressed on cancer cells has become a promising strategy. Based on our previous work [1], in this study we applid modeling approach to explore the effects of various physiological and biological functions of RITs on their antitumor efficacies. Specifically, we included the neogenesis and destruction of tumor vasculatires in our simulations to study their correlations with tumor growth and resistance to anticancer drugs. We studied different RITs based on Pseudomonas exotoxin (PE) -fused recombinaant proteins (Fig. 1). In order to incorporate spatial heterogeneity and increased biological realism, we employed a more computationally challenging scheme based on cellular automata (CA) called the Cellular Potts Model (CPM) which allows us to treat cells as autonomous agents and to construct rules based on the local microenvironment (e.g., see [3]). The original Potts model [4],
generalized by Glazier and Graner [5] for biological modeling, has been widely applied to model differential adhesion and morphogenesis [6-8]. The CPM allows us to describe irregular cell shapes, since unlike many other CA models, each biological cell is treated as a collection of connected automata. In our model, space was fragmented into a uniform 3D grid composed of identical square elements (automata). A given element may represent ECS, or a part of a cell or blood vessel as shown in Figure 2. It was correspondingly assigned a type-index, t={e, c, v}. Contiguous elements making up a biological cell, were assigned a unique cell identity number, s={1, 2, 3…N}, where N is the total number of cells in the system at a given instant. Further, four state variables were assigned to each biological cell, (i) the state of RIT intoxication (unaffected or affected), (ii) the concentration of receptors on the cell surface [Re] and (iii) in the cytoplasm [Ri], and (iv) the age of the cell. The interaction between an element and each of its 8 neighbors was characterized by a “free energy of interaction” J. For example, Jc,c relates (inversely) to the strength of the adhesion between different cells. Large values of J indicate weak adhesion, and hence enhanced cell migration. To keep the size of cells fairly constant, we introduced a Lagrangian constraint into the expression for the total energy E. To study the evolution of the system, we used a standard Metropolis MC method. In the first stage of modeling, we assumed that the blood vessel elements do not evolve. Thus we selected a site at a cell-cell or a cell-ECS interface randomly, and proposed a trial move that involves reassigning its type-index to that of one its neighbors. The change in total energy, DE, was calculated and the trial was accepted with a Boltzmann probability p=max{1, exp(-DE/T)}, where T is a parameter that controls the amplitude of the cytoskeletally driven membrane fluctuations. The division and growth of cells using the CPM resembles previously reported methodologies [9,10], in which the age of a tumor cell is compared with the distribution of cell cycle times, and the cell is divided into two daughter cells if appropriate. The distribution of cell cycle times ensures that all the cells do not attempt to divide at the same time. The constraint on cell size ensures that the daughter cells grow to the target cell size As. Similarly, cell death upon RIT intoxication was modeled by setting the target cell size to zero. The parameters l, and As were estimated from cell size distribution data in a histological cross section. The parameters J and T can be estimated by examining the density and motility of cells. The problem has three widely separated timescales, tRIT
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Page 3 and 4: The nonlinear dynamical interaction
Page 5 and 6: Lim., Carmay Our research interests
Page 7 and 8: Email: jtus@phys.ualberta.caBackgro
Page 9 and 10: Formula drug or nutrient Any LMN is
Page 11 and 12: present study provide support to th
Page 13 and 14: Keywords: Amino acid index, non-cla
Page 15: mainly stabilized by the H-bonds fr
Page 19 and 20: Profile-profile alignment may be th
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Page 27 and 28: fluorescent protein ng, W.-D. Cheng
Page 29 and 30: 2. Email: xpgao@xtu.edu.cn Classifi
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Page 33 and 34: analysis across different platforms
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