Graph Theory for Systems Biology

Jungck, John R.; Viswanathan, R.

doi:10.1016/b978-0-12-801213-0.00001-0

Cited by 5 publications

(3 citation statements)

References 27 publications

(35 reference statements)

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“…We applied the steps as in Chapter II to connect the overlapping segments. Using the method in 14 to rearrange the segments, the first sequencing suggested by the program was shown in Figure 4, which is Alpha⇨ Beta⇨ Gamma⇨ A1⇨ A2⇨ …⇨ A11⇨ A12⇨ B1⇨ B2⇨ B3⇨ G1⇨ G2. We shall call it .…”

Section: Results and Difficultiesmentioning

confidence: 99%

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Interval graphs for Genome-Fragment combinations

KAO,

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20

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Section: Results and Difficultiesmentioning

confidence: 99%

“…The following steps will be taken to convert our laboratory data into an interval graph. See 14 for details. For the convenience of the reader, here we define some terms related to graph theory that will be used in the sequel.…”

Section: Mathematical Proceduresmentioning

confidence: 99%

Interval graphs for Genome-Fragment combinations

KAO,

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20

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“…In studying GRNs, 2 main approaches for extracting information exist: (i) static network analysis , and (ii) dynamical modeling , each of which offers different amounts and types of information regarding the network organization, topology, and behavior [ 10 , 11 ]. Inferring and modeling these regulatory networks, however, is a challenging reverse engineering process and requires the combination of both a thorough biological understanding of the system, as well as accurate and advanced computational inference methods [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Marku

Pancaldi

2023

PLoS Comput Biol

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Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

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Computing List Homomorphisms in Geometric Intersection Graphs

Kisfaludi-Bak

Okrasa

Rzążewski

2022

Lecture Notes in Computer Science

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A homomorphism from a graph G to a graph H is an edge-preserving mapping from V (G) to V (H).Let H be a xed graph with possible loops. In the list homomorphism problem, denoted by LH (H), the instance is a graph G, whose every vertex is equipped with a subset of V (H), called list. We ask whether there exists a homomorphism from G to H, such that every vertex from G is mapped to a vertex from its list.We study the complexity of the LH (H) problem in intersection graphs of various geometric objects. In particular, we are interested in answering the question for what graphs H and for what types of geometric objects, the LH (H) problem can be solved in time subexponential in the number of vertices of the instance.We fully resolve this question for string graphs, i.e., intersection graphs of continuous curves in the plane. Quite surprisingly, it turns out that the dichotomy exactly coincides with the analogous dichotomy for graphs excluding a xed path as an induced subgraph [Okrasa, Rzążewski, STACS 2021].Then we turn our attention to subclasses of string graphs, de ned as intersections of fat objects. We observe that the (non)existence of subexponential-time algorithms in such classes is closely related to the size mrc(H) of a maximum re exive clique in H, i.e., maximum number of pairwise adjacent vertices, each of which has a loop. We study the maximum value of mrc(H) that guarantees the existence of a subexponential-time algorithm for LH (H) in intersection graphs of (i) convex fat objects, (ii) fat similarly-sized objects, and (iii) disks. In the rst two cases we obtain optimal results, by giving matching algorithms and lower bounds.Finally, we discuss possible extensions of our results to weighted generalizations of LH (H).

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Graph Theory for Systems Biology

Cited by 5 publications

References 27 publications

Interval graphs for Genome-Fragment combinations

Interval graphs for Genome-Fragment combinations

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Computing List Homomorphisms in Geometric Intersection Graphs

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