Introduction: Prof. Egils Stalidzans received the Dr.sc.ing. degree in information technology from the Riga Technical University, Latvia. He started research in the Biosystems group at Latvia University of Agriculture. He has contributed to European level coordination projects in fields of synthetic biology (ERASynBio) and systems biology (ERASysAPP). His interests cover different branches of biology. He is director of Institute of Microbiology and Biotechnology at University of Latvia, professor in the Department of Computer Systems at Latvia University of Agriculture and senior researcher at Latvian Biomedical Study and Research Centre. His research interests include modeling and optimisation of metabolic, signaling and regulatory biochemical networks and performance analysis of optimisation methods. Several software products like Paint4Net toolbox for COBRA software and SpaceScanner wrapper for COPASI software have been developed. Speech Title: "Implementation of Constraints in Kinetic and Stoichiometric Models to Improve Model Based Metabolism Design" Abstract: The implementation of model-based designs in metabolic engineering and synthetic biology lags behind expectations because some designs fail. One of reasons is inclusion of just a part of the real world complexity in models. The models can not be as complex as the real world, but some knowledge can be simplified and taken into account in the form of optimization constraints to improve the feasibility of model-based designs of metabolic pathways in organisms. The fact that some constraints are not applied indicates a potential for design improvement by appropriate application of the constraints. Some constraints (mass balance, energy balance, steady state assumption) serve as a basis for many modelling approaches. Some other constraints (total enzyme activity constraint, homeostatic constraint) are known for decades but are frequently ignored in design development. Several new approaches of cellular analysis have made possible the application of constraints like cell size, surface, resource balance, etc. A specific type of constraint is the total optimization potential (TOP) approach is applied to find minimal set of adjustable parameters necessary to reach requested fraction of the objective function value when all possible adjustable parameters are included in the combination.