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Mathematical Modeling In Systems Biology: An Introduction (MIT Press) Download _TOP_ Pdf



Support of ABMs from the Scientific Community. Several introductory tutorials describe the features of ABMs, including Bonabeau (2002), Matthews (2007), Macal (2010), Heath (2010), Niazi & Hussain (2011), Heard (2015), and Weimer (2016). Generic reviews of ABMs include Gu & Blackmore (2015), Gooding (2019), Heath (2009), Grimm (2010), and Hanappi (2017). In a slight extension of ABMs, Lattilä (2010) and Cisse (2013) described hybrid simulations involving ABMs and dynamic systems, and Heard (2015) discussed statistical methods of ABM analysis. These introductory tutorials and reviews, however, are typically not designed for undergraduates with limited mathematical or computational modeling experience. The scientific community has also worked hard on facilitating the use of ABMs by offering software like NetLogo, Swarm, RePast, and Mason. Summaries and evaluations of some of the currently pertinent software are available in Berryman (2008) and Abar (2017), and NetLogo is described more fully in Sect. 3.




Mathematical Modeling In Systems Biology: An Introduction (MIT Press) Download Pdf



Rapid advances in computing power over the past decades have made agent-based modeling a feasible and appealing tool to study biological systems. In undergraduate mathematical biology education, there are multiple modes by which ABMs are utilized and taught in the classroom. In biology classrooms, ABMs can be used to engage students in hypothesis testing and in the experimental design and data collection processes of otherwise infeasible experiments, and to enable students to utilize models as a part of the scientific process. All of this can be done without students having to learn a programming language. By contrast, students who have had some exposure to computer programming can learn the construction, implementation, and analysis of agent-based models in a math or computer science modeling class. Biological applications are ideal systems for first attempts at agent-based models as they typically do not necessitate learning extensive new vocabulary and theory to understand the basic components that need to be included in the model. Throughout this article, we endeavored to articulate the benefits and challenges of including ABMs in undergraduate life science and math modeling courses.


In systems biology experimental approaches are combined with mathematical modeling to understand complex behavior of cells and organisms. Experimental approaches and mathematical models are connected through a cyclic workflow [9]. Experimental data is used as input for mathematical models that, in turn, generate biological predictions. These predictions are then again verified by experimental approaches, thus completing the cycle. Experimental approaches, used in systems biology of apoptosis so far, include quantitative Western Blot, cell death assays, single cell analysis and mass spectrometry.


Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems.


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