Monday, June 14 at 03:15pm (PDT)Monday, June 14 at 11:15pm (BST)Tuesday, June 15 07:15am (KST)
SMB2021 FollowMonday (Tuesday) during the "CT01" time block.
University of California, Riverside
"A Metabolism-Based Multiscale Model of Fungal Development and Growth"
Bacterial-fungal interactions play a fundamental role in many processes including crop biofuel development and biosystem design. In this work, we focus on the interactions between the fungi Laccaria bicolor and the bacterium Psuedomonas fluorescens and their integral role in the fitness of the roots of the Populus tree. L. bicolor synthesizes trehalose which stimulates growth and chemotaxis of P. flourescens. Furthermore, P. flourescens provides L. bicolor with thiamine thereby increasing fungal mass. We developed a multiscale computational model to investigate these interdependent interactions. The growth and branching of the fungal mycelia are modeled using an off-lattice spatial discrete submodel which is dependent on both diffusive and active translocation of internal nutrients and uptake of external nutrients. The fungal growth model is coupled with a thermodynamic-kinetic maximum entropy ODE model for metabolism, containing over 200 reactions including protein and nucleic acid synthesis, from which the costs of growth and maintenance can be calculated. Trehalose secretion, especially at the tips of the hyphae, acts as a source of diffusive chemoattractant for P. fluorescens colony. Numerical simulations of these coupled models under various conditions aid in characterizing the energetic costs of growth and maintenance of L. bicolor in the presence of P. fluorescens.
Brenda Lyn A. Gavina
University of the Philippines
"Optimization of dosing strategy for anovulation"
A female's reproductive life from the average age of 12.5 until 51 is governed by the menstrual cycle. During this cycle, pituitary and ovarian hormones fluctuate. Abnormal concentrations of these hormones often cause cycle irregularities. However, there are cases where an abnormal cycle, in particular anovulation, is desired. For instance, in contraception and in managing premenstrual symptoms. Exogenous hormones such as synthetic progesterone and synthetic estrogen have been used to attain anovulatory state by controlling hormone levels in the body. Nonetheless, large doses are associated with adverse effects such as increased risk for thrombosis and myocardial infarction. This talk focuses on the application of optimal control to a simple modification of the model in (Margolskee et al., 2011) in order to determine the minimum dosages of exogenous estrogen and progesterone that result to anovulation. Exogenous hormone profile and timing of administration are obtained. These results may give clinicians insights to improve dosing strategies in ovulation suppression.
University of Kentucky
"Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets"
Pancreatic Ductal Adenocarcinoma is among the leading causes of cancer related deaths globally due to its extreme difficulty to detect and treat. Recently, research focus has shifted to analyzing the microenvironment of pancreatic cancer to better understand its key molecular mechanisms. This microenvironment can be represented with a multi-scale model consisting of pancreatic cancer cells, pancreatic stellate cells, as well as cytokines and growth factors which are responsible for intercellular communication between the PCCs and PSCs. We have built a stochastic Boolean model, validated by literature and clinical data, in which we probed for intervention strategies that force this gene regulatory network from a diseased state to a healthy state. We implemented methods from phenotype control theory to determine a procedure for regulating specific genes within the microenvironment. After applying well studied control methods such as stable motifs, feedback vertex set and computational algebra, we discovered that each produces a different set of control targets that are not necessarily minimal nor unique. Each control set contains cytokines, KRas, and HER2/neu which suggests they are key players in the system's dynamics. Many of these model predictions are supported by literature and have potential to be new targets.
"Integration of Intracellular Kinetic Models to Multiscale Agent-Based Models"
Multiscale Agent-Based Models (ABM) provide a framework to model across different scales while intracellular kinetic models capture dynamic trends at the molecular level. In this work, we integrated intracellular kinetic models to a 3-D physics-based multiscale ABM tool (PhysiCell). Cells are represented as intelligent agents which behave according to rules and parameters. As a result, dynamic molecular models which are represented as ordinary differential equations (ODEs) in Systems Biology Markup Language (SBML) format were solved in PhysiCell using libRoadrunner, a fast, SBML solver package. To achieve this goal, cells uptake/secrete chemicals to/from the microenvironment. Custom data associated with PhysiCell agents is used as an interface between ABM and ODEs, updating the SBML in pre-defined time intervals. Kinetic ODEs are simulated at this point and results are updated to the custom data which can be used to control phenotypic parameters. Therefore, phenotypic changes in intelligent agents are determined by molecular level events. Moreover, having cell-specific custom data provides the heterogeneity through tissue or domain. This advancement makes PhysiCell models easier to produce since modelers can use SBML to write their dynamic phenotypes without writing complex functions in C++.