Boolean Modeling of Biochemical Networks

Abstract

Abstract:The use of modeling to observe and analyze the mechanisms of complex biochemical network function is becoming an important methodological tool in the systems biology era. Number of different approaches to model these networks have been utilized--they range from analysis of static connection graphs to dynamical models based on kinetic interaction data. Dynamical models have a distinct appeal in that they make it possible to observe these networks in action, but they also pose a distinct challenge in that they require detailed information describing how the individual components of these networks interact in living cells. Because this level of detail is generally not known, dynamic modeling requires simplifying assumptions in order to make it practical. In this review Boolean modeling will be discussed, a modeling method that depends on the simplifying assumption that all elements of a network exist only in one of two states.Keywords: Systems biology, dynamical modeling, signal transduction, boolean networks.The modern era of cellular biochemistry has been characterized by the discovery of large interaction networks that are notable for their highly nonlinear connection structures. Such structures complicate study since they are often quite difficult to reduce into linear subsets that function in isolation from the embedding network [1,2]. It is for this reason that the concept of "systems biology" has been developed and is now being applied to these complicated biochemical networks [1,3,4].One of the first steps taken toward applying a systems approach to understanding biochemical networks was to begin organizing the connection information determined in the laboratory by creating connection maps (wiring diagrams) that could then be analyzed using graph theory concepts. While these studies have been highly successful [5], the fact that cells are dynamical systems means the next major step in the study of these systems involves the creation of models that take into account how they move and function in time. Thus we are now in a new era of creating large-scale dynamical models of biochemical systems.An obvious choice for the modeling of dynamical systems is to describe the system with a system of differential equations, and this approach has been used extensively to study protein and/or gene interactions in a variety of relatively small models (for examples, see [6][7][8][9]). One of the advantages of continuous modeling via differential equations is that one can potentially describe molecular interactions with high precision and in quantitative terms that correspond to realistic laboratory measurements. However, that precision tends to make them computationally unwieldy for the large scale models necessary for the systems approach. There *Address correspondence to this author at the Department of Mathematics, University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE 68182, USA; Tel: 402 554-3110: Fax: 402 554-2975; E-mail: jrogers@unmc.edu is currently a great deal of work being done in order to simpl...

DNA Tandem Repeats as Iterable Objects to Count Cell Divisions: A Computational Model

“…Indeed, the hypothesis that tandem repeats might work as genomic counters came up years ago [55]: telomere shortening, a much debated and controversial subject (telomeres, the ends of linear chromosomes, are made up of TRs) had been suspected to work as a "replicometer" [56]; telomere shortening occurs during cell division and has been associated with the replicative capacity of cells, in the sense that its shortening could limit the remaining number of divisions, causing cell senescence [57]. The computational model "Lineages" tests a different method to count cell divisions: no losses of material (telomere shortening is a consequence of rough nucleotide depletions), rather precise, intrinsic, and autonomous molecular mechanisms capable of running over TRs, using, as said, the same Boolean logic-algebra that governs algorithms [46] [47] [48] [49]. To count cell divisions, ordered natural numbers are not necessary for cells: different lengths of satDNA se-quences [58] [59], selected during evolution to satisfy the iterative needs of the species, are enough to count different amounts of iterations.…”

“…The computational model introduced here aims to show how these epigenetic mechanisms can count cell division rounds and keep track of their progressive movements on satDNA sequences: they can proceed linearly on TRs, read one monomer at a time, and leave a flag (cytosine-methylation) as a mark-record for descendant cells, sharing the same Boolean logic algebra that governs algorithms [46] [47] [48] [49].…”

Analysis of Human gene - Disease association as a Social network

“…Researchers [10] have gained significant interest in analyzing molecular network due to easy availability of the biological data. This type of analysis is closely related to SNA, but local patterns in the network are mainly focused in such analysis.…”

Data-driven learning of Boolean networks and functions by optimal causation entropy principle

“…The interconnections among genes can be thought of as forming a Boolean network, via particular sets of Boolean functions that govern the dynamics of such a network. The use of Boolean networks has many applications, such as for the modeling of plant-pollinator dynamics, 1 yeast cell cycles, 2 , 3 pharmacology networks, 4 tuberculosis latency, 5 regulation of bacteria, 6 biochemical networks, 7 immune interactions, 8 signaling networks, 9 gut microbiome, 10 drug targeting, 11 drug synergies, 12 floral organ determination, 13 gene interactions, 14 and host-pathogen interactions. 15 In general, the problem of learning Boolean functions and Boolean networks from observational data in a complex system is an important problem to explain the switching relationships in these and many problems in science and engineering.…”