Meeting Programme
Dr Christine Faulkner (Oxford Brookes University)
The regulation of cell-to-cell communication during pathogen invasion
- Cell-to-cell communication is a fundamental biological process, necessary for co-ordination of development and environmental responses in multicellular organisms. The receptor-like protein AtLYM2 is located at plasmodesmata and mediates chitin-triggered changes to cell-to-cell communication during fungal pathogen invasion . AtLYM2 functions independently of the chitin receptor CERK1 and thus identifies that there are multiple chitin perception mechanisms in plants.
Dr Santanu Datta (Cellworks)
Delineating the genetic and chemical vulnerability of anti-infective drug targets
- The equity of a drug target is majorly evaluated by its genetic vulnerability with tools ranging from antisense and microRNA driven knockdowns to induced expression of the target protein. In order to upgrade the process of antibacterial target identification and discern its most effective type of inhibition, an in silico tool box that evaluates its genetic and chemical vulnerability leading either to stasis or cidal outcome was constructed and validated.
Dr Ashwani Pareek (JNU, New Delhi)
Dissecting out the complex trait of abiotic stress tolerance in plants: will a systems biology approach be of some help?
- Abiotic stresses cause a significant decline in crop yield worldwide. We are trying to dissect of the complex trait of salinity and drought response in rice plants. Contemporary tools of transcriptomics, proteomics, Ionomics, metabolomics etc are being used to understand the response of rice seedlings towards these stresses. One of the important conclusion which can be drawn easily from these analysis is that the response is quite complex in nature. Hundereds of genes, proteins and metabolites are found to be up regulated or down regulated in rice seedlings within few minutes of salinity stress. The question still remains with us - which gene shall we engineer? which genes should be pyramided? will systems biology approach provide a solution to this? We have picked up a set of "candidate genes" from this list and have used the tools of functional genomics to validate the role of these unknown genes in stress tolerance. Data pertaining to these experiments will be discussed.
Prof A S Raghavendra (University of Hyderabad)
Optimisation of Photosynthesis by Multiple Intracellular Compartments in Plant Cells: A Systems Biochemistry Outlook
- Key metabolic processes in plant cells depend on dynamic inter-organelle interactions. The crosstalk between compartments is mediated by multiple signals, such as, metabolite movements, redox status and even pH. My talk would describe the signalling network between chloroplasts, mitochondria, peroxisomes, and cytoplasm, which achieves not only optimisation of photosynthesis but also protects against photoinhibition.
Dr Anu Raghunathan (National Chemical Laboratory, Pune)
From cell lines to Tissue specific Flux Balance Models: Cancer Cell Growth and Metabolism
- Complex multi-hit, multi phenotype diseases like cancer have poor prognosis and their etiology is difficult to fathom due to multi-factorial emergent responses of the human system. Patients of such diseases would benefit highly from a personalized and individualized approach for treatment. Constraints-based flux balance models seem to provide such a platform for data integration and analysis of complex diseases. The focus of this talk is the development and analysis of tissue specific models for cancer. Global microarray gene expression data is used to develop cell-line and patient models for lung cancer using from legacy data using the genome scale model of human metabolism, Recon1 as the basis. The paradigm for metabolic systems biology as applied to lung cancer will be discussed in the context of computing the cancer phenotype. The predictions of clinical models using tissue data and blood data will be compared to the classical cell line models. Integration of results of routine blood work, specialized clinical tests like PET (Positron Emission Tomography) scans allow for better prognosis but also fundamental understanding of the disease. The challenge of such a task in the context of flux balance metabolic models for predictive differentiation of progressive disease states and their ability to predict outcomes will also be discussed.
Prof David Fell (Oxford Brookes University)
Modelling Tomato Fruit Metabolism
Both genetic and environmental factors have a strong and multifaceted influence on tomato fruit quality. They act and interact in such a complex way that it is extremely difficult to study their effects by experiment alone. The EraSysBio project Fruit Integrative Modelling involves partners from Oxford and two groups in France. We aim to build a virtual tomato fruit that enables the prediction of metabolite levels given genetic and environmental inputs, by an iterative process between laboratories that combine expertise in fruit biology, ecophysiology, theoretical and experimental biochemistry, and biotechnology. Our component is to build a kinetic model encompassing the routes carbon takes, once imported into the fruit cells from the source organs of the mother plant. This will be combined with To integrate the kinetic model with a phenomenological model predicting sugar and organic acid contents as functions of time, light intensity, temperature and water availability being developed in one of the French labs. Our approach to building the metabolic simulation will be described, as well as its context within the whole project.
Prof K V Venkatesh (Dept Chemical Engineering, IIT Bombay) (with Pramod Somvanshi and Anil Pate)
Multi-scale Structured Kinetic Model for Analyzing Disease States in Metabolic Syndrome
Metabolic syndrome is among the most complex diseases with multiple complications associated with it such as insulin resistance, central obesity, Type 2 diabetes, hypercholesterolemia, hypertension, atherosclerosis and coronary artery disease. It has been found that these are system level diseases with defects at metabolic, signaling and genetic levels rather than defects in individual biological entities. In metabolic syndrome, insulin resistance is the key defects which affect various metabolic processes. Defects in insulin secretion, its activity, and its signaling generate insulin resistance. Insulin signaling pathway orchestrates its response with various other pathways coupled with feedback mechanisms to generate multiple effects. Moreover various other hormones and inflammatory pathways are known to modulate the insulin activity. Insulin resistance is thus a combinatorial effect of all these factors.
The systems biological approach with mathematical modeling of the biological networks serves as an important way to assess these diseases. We attempt to integrate the models of meal simulation, whole body metabolism, insulin signaling pathway and insulin secretion which scale from cellular to organ levels. We assort to the kinetic modeling and analysis of the integrated network and analyze the metabolic response with respect to perturbation in various parameters of the model. The detailed integrated network of various affected metabolic, signaling and genetic pathways is prepared. The kinetic models are developed that describe various interactions. Parameter values and other constants are collected from the literature and are also estimated through optimization and curve fitting for the model. The models are then simulated for sensitivity and phase-plane analysis to notice the key parameters that have major influence on the overall network behavior. Furthermore, Perturbation analysis is performed to get the matrix of parameters for healthy and disease phenotype. Such an analysis helps in understanding the system dynamics and help in answering various ‘What-if’ kinds of questions that can aid in identification of potential drug targets and design effective therapies. We further extend the analysis to address the link between metabolism and tumor genesis by extending the above model by integrating P53 pathway to AKT-PTEN and further representing the regulation of metabolism through HIF1 activity. Analysis shows that the perturbation at AKT node can affect metabolism leading to tumor genesis demonstrating Warburg Effect. Thus the model for the whole-body metabolism including signaling pathways can yield insights into the emergence of disease states.