Them from their environment, and state variable nodes that are responsive
Them from their environment, and state variable nodes that are responsive to environmental changes. The models are typically built and validated using 4 types of data:1. Configuration data ?These data specify how various system components are interconnected. Wiring diagrams are an excellent example of this form of data. 2. Isolated component data ?These data describe how system components behave in complete isolation, e.g. enzyme mechanisms and estimates of binding constants. 3. Operating point data ?These data describe the state of the interconnected system in steady state, e.g. metabolite concentrations in whole cell extracts of unperturbed cells.Page 2 of(page number not for citation purposes)BMC Cancer 2006, 6:http://www.biomedcentral.com/1471-2407/6/IUdR salvagedNTP demand is eitherDNA damage driven or S-phase drivenResultsDirect approach to therapeutic gain Iododeoxyuridine (IUdR) sensitization of cells to radiation induced cell killing [39,40] is proportional to its incorporation into DNA [41,42], and IU-DNA has a order Vasoactive Intestinal Peptide (human, rat, mouse, rabbit, canine, porcine) longer half-life in mismatch repair deficient (MMR-) cells than in MMR+ cells [43,44]. Thus, IU-DNA levels at the time of irradiation correlate with the probability of clonogenic cell death and are modulated by the cause of the cancer. Suppose we have a model of IUdR metabolism that includes IU-DNA and MMR. If the MMR parameters of this model are set to MMR deficient values, the model represents MMR- malignant cells, otherwise, with wild type MMR parameters, the model represents MMR+ normal cells. Such a model, or pair of models, could be used to develop novel multi-drug approaches to the treatment of MMR- cancers. Specifically, the models could be used to predict how IUdR metabolism should be manipulated by drugs over time to maximize IU-DNA differences between MMR- and MMR+ cells at some time point. The timing of the maximum difference would then determine the best timing of acute irradiation to exploit the IU-DNA differences for a therapeutic gain. This treatment scenario is depicted in Figure 2.dNTPs + IdUTP de novo focus on mismatch repair defective cancersDNA + IU-DNADNA repairFigure 2 IUdR treatment of MMR defective cancers IUdR treatment of MMR defective cancers.process controllability, leukemia simplicity relative PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/29069523 to solid tumors should favor its curability. In the past, control system design methods have been developed primarily for linear [27] rather than nonlinear systems [28], and most often for systems with rich rather than sparse time course data. New design methods and/or ad hoc solutions will therefore be needed to successfully apply control system approaches to cancer therapy. Previous attempts to apply control theory [29,30] to cancer therapy [31,32] lacked sufficient data, and the process models that were used were not molecularly based. As our understanding of cancer relevant biological processes increases, our knowledge of the dynamic behavior of these processes will improve, and thus so too will the likelihood of successfully applying control system design approaches to cancer therapy. The purpose of this paper is to formulate (but not solve) two systems and control oriented, biochemical system model-based therapeutic gain strategies as abstractions of two conceptually distinct, specific cancer treatment strategies.MethodsSBMLR [33-35] was used with a folate model [3] to map the childhood leukemia diagnostic bone marrow microarray data of Yeoh et al [36] into predicted steady state fluxes of de n.