We are analyzing https://link.springer.com/article/10.1186/1751-0473-3-16.

Title:
Boolean network simulations for life scientists | Source Code for Biology and Medicine
Description:
Modern life sciences research increasingly relies on computational solutions, from large scale data analyses to theoretical modeling. Within the theoretical models Boolean networks occupy an increasing role as they are eminently suited at mapping biological observations and hypotheses into a mathematical formalism. The conceptual underpinnings of Boolean modeling are very accessible even without a background in quantitative sciences, yet it allows life scientists to describe and explore a wide range of surprisingly complex phenomena. In this paper we provide a clear overview of the concepts used in Boolean simulations, present a software library that can perform these simulations based on simple text inputs and give three case studies. The large scale simulations in these case studies demonstrate the Boolean paradigms and their applicability as well as the advanced features and complex use cases that our software package allows. Our software is distributed via a liberal Open Source license and is freely accessible from http://booleannet.googlecode.com
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Keywords {🔍}

boolean, pubmed, article, google, scholar, network, state, model, nodes, cas, simulation, models, simulations, node, closure, cells, software, time, modeling, biological, systems, albert, system, regulatory, rules, states, case, networks, figure, cell, immune, biol, central, aicd, apoptosis, mcl, analysis, biology, large, steps, continuous, piecewise, pathway, initial, sfas, based, qualitative, linear, variable, leukemic,

Topics {✒️}

𝑎 𝑛 𝑑 cd95/cd95 ligand-apoptotic pathway pair-rule cross-regulatory module b-cell leukemia/lymphoma 2 medicine istván albert cd8+ t-cell memory higher rank group author information authors istván albert rank group leukemic t-lgl cells piece-wise linear frameworks article download pdf antigen-primed mature cytotoxic yeast cell-cycle network piece-wise linear model random initial states piece-wise linear formalism resting-t-cell induce normal ctl activation piece-wise linear simulation leukemic t-lgl search drosophila gap-gene system article albert author correspondence high-level expression large scale simulations rule-based uncertainty model authors’ original file computational cell biology modeling systems-level regulation cell-fate determination random variables rc bordetella bronchiseptica rb50 ra directed research genetic network analyzer granular lymphocytes full size image python programming language privacy choices/manage cookies piece-wise formulation abscisic acid insensitive kauffman sa author gene regulatory networks form defective bordetella strain bordetella pertussis tohama

Questions {❓}

Mills KH: Regulatory T cells: friend or foe in immunity to infection?

Schema {🗺️}

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         headline:Boolean network simulations for life scientists
         description:Modern life sciences research increasingly relies on computational solutions, from large scale data analyses to theoretical modeling. Within the theoretical models Boolean networks occupy an increasing role as they are eminently suited at mapping biological observations and hypotheses into a mathematical formalism. The conceptual underpinnings of Boolean modeling are very accessible even without a background in quantitative sciences, yet it allows life scientists to describe and explore a wide range of surprisingly complex phenomena. In this paper we provide a clear overview of the concepts used in Boolean simulations, present a software library that can perform these simulations based on simple text inputs and give three case studies. The large scale simulations in these case studies demonstrate the Boolean paradigms and their applicability as well as the advanced features and complex use cases that our software package allows. Our software is distributed via a liberal Open Source license and is freely accessible from http://booleannet.googlecode.com
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      headline:Boolean network simulations for life scientists
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         Random Initial State
         Activation Induce Cell Death
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         Bioinformatics
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