4 Search Results for "Wark, Ian"

Document
DOI: 10.4230/LIPIcs.DNA.31.1
Algorithmic Hardness of the Partition Function for Nucleic Acid Strands

Authors: Gwendal Ducloz, Ahmed Shalaby, and Damien Woods

Published in: LIPIcs, Volume 347, 31st International Conference on DNA Computing and Molecular Programming (DNA 31) (2025)

Abstract
To understand and engineer biological and artificial nucleic acid systems, algorithms are employed for prediction of secondary structures at thermodynamic equilibrium. Dynamic programming algorithms are used to compute the most favoured, or Minimum Free Energy (MFE), structure, and the Partition Function (PF) - a tool for assigning a probability to any structure. However, in some situations, such as when there are large numbers of strands, or pseudoknotted systems, NP-hardness results show that such algorithms are unlikely, but only for MFE. Curiously, algorithmic hardness results were not shown for PF, leaving two open questions on the complexity of PF for multiple strands and single strands with pseudoknots. The challenge is that while the MFE problem cares only about one, or a few structures, PF is a summation over the entire secondary structure space, giving theorists the vibe that computing PF should not only be as hard as MFE, but should be even harder. We answer both questions. First, we show that computing PF is #P-hard for systems with an unbounded number of strands, answering a question of Condon Hajiaghayi, and Thachuk [DNA27]. Second, for even a single strand, but allowing pseudoknots, we find that PF is #P-hard. Our proof relies on a novel magnification trick that leads to a tightly-woven set of reductions between five key thermodynamic problems: MFE, PF, their decision versions, and #SSEL that counts structures of a given energy. Our reductions show these five problems are fundamentally related for any energy model amenable to magnification. That general classification clarifies the mathematical landscape of nucleic acid energy models and yields several open questions.
Cite as

Gwendal Ducloz, Ahmed Shalaby, and Damien Woods. Algorithmic Hardness of the Partition Function for Nucleic Acid Strands. In 31st International Conference on DNA Computing and Molecular Programming (DNA 31). Leibniz International Proceedings in Informatics (LIPIcs), Volume 347, pp. 1:1-1:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ducloz_et_al:LIPIcs.DNA.31.1,
  author =	{Ducloz, Gwendal and Shalaby, Ahmed and Woods, Damien},
  title =	{{Algorithmic Hardness of the Partition Function for Nucleic Acid Strands}},
  booktitle =	{31st International Conference on DNA Computing and Molecular Programming (DNA 31)},
  pages =	{1:1--1:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-399-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{347},
  editor =	{Schaeffer, Josie and Zhang, Fei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DNA.31.1},
  URN =		{urn:nbn:de:0030-drops-238504},
  doi =		{10.4230/LIPIcs.DNA.31.1},
  annote =	{Keywords: Partition function, minimum free energy, nucleic acid, DNA, RNA, secondary structure, computational complexity, #P-hardness}
}
Document
DOI: 10.4230/LIPIcs.WABI.2025.13
Spark: Sparsified Hierarchical Energy Minimization of RNA Pseudoknots

Authors: Mateo Gray, Sebastian Will, and Hosna Jabbari

Published in: LIPIcs, Volume 344, 25th International Conference on Algorithms for Bioinformatics (WABI 2025)

Abstract
Motivation. Determining RNA structure is essential for understanding RNA function and interaction networks. Although experimental techniques yield high‑accuracy structures, they are costly and time‑consuming; thus, computational approaches - especially minimum‑free‑energy (MFE) prediction algorithms - are indispensable. Accurately predicting pseudoknots, however, remains challenging because their inclusion usually leads to prohibitive computational complexity. Recent work demonstrated that sparsification can improve the efficiency of complex pseudoknot prediction algorithms such as Knotty. This finding suggests similar gains are possible for already efficient algorithms like HFold, which targets a complementary class of hierarchically constrained pseudoknots. Results. We introduce Spark, an exact, fully sparsified algorithm for predicting pseudoknotted RNA structures. Like its non‑sparsified predecessor HFold, Spark searches for the minimum‑energy structure under the HotKots 2.0 energy model, a pseudoknot extension of the Turner model. Because the sparsification is non‑heuristic, Spark preserves the asymptotic time‑ and space‑complexity guarantees of HFold while greatly reducing the constant factors. We benchmarked the performance of Spark against HFold and, as a pseudoknot‑free baseline, RNAfold. Compared with HFold, Spark substantially lowers both run time and memory usage, while achieving run‑time figures close to those of RNAfold. Across all tested sequence lengths, Spark used the least memory and consistently ran faster than HFold. Conclusion. By extending non‑heuristic sparsification to hierarchical pseudoknot prediction, Spark delivers an exceptionally fast and memory‑efficient tool accurate prediction of pseudoknotted RNA structures, enabling routine analysis of long sequences. The algorithm broadens the practical scope of computational RNA biology and provides a solid foundation for future advances in structure‑based functional annotation. Availability. Spark’s implementation and detailed results are available at https://github.com/TheCOBRALab/Spark.
Cite as

Mateo Gray, Sebastian Will, and Hosna Jabbari. Spark: Sparsified Hierarchical Energy Minimization of RNA Pseudoknots. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 13:1-13:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{gray_et_al:LIPIcs.WABI.2025.13,
  author =	{Gray, Mateo and Will, Sebastian and Jabbari, Hosna},
  title =	{{Spark: Sparsified Hierarchical Energy Minimization of RNA Pseudoknots}},
  booktitle =	{25th International Conference on Algorithms for Bioinformatics (WABI 2025)},
  pages =	{13:1--13:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-386-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{344},
  editor =	{Brejov\'{a}, Bro\v{n}a and Patro, Rob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2025.13},
  URN =		{urn:nbn:de:0030-drops-239383},
  doi =		{10.4230/LIPIcs.WABI.2025.13},
  annote =	{Keywords: RNA, MFE, Secondary Structure Prediction, Pseudoknot, Sparsification, Space Complexity, Time Complexity}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.130
An Efficient Algorithm to Compute the Minimum Free Energy of Interacting Nucleic Acid Strands

Authors: Ahmed Shalaby and Damien Woods

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
The information-encoding molecules RNA and DNA bind via base pairing to form an exponentially large set of secondary structures. Practitioners need algorithms to predict the most favoured structures, called minimum free energy (MFE) structures, or to compute a partition function that allows assigning a probability to any structure. MFE prediction is NP-hard in the presence pseudoknots - base pairings that violate a restricted planarity condition. However, for single-stranded unpseudoknotted structures, there are polynomial time dynamic programming algorithms. For multiple strands, the problem is significantly more complicated: Codon, Hajiaghayi and Thachuk [DNA27, 2021] proved it NP-hard for N bases and 𝒪(N) strands. Dirks, Bois, Schaeffer, Winfree and Pierce [SIAM Review, 2007] gave a polynomial time partition function algorithm for multiple (𝒪(1)) strands, now widely-used, however their technique did not generalise to MFE which they left open. We give an 𝒪(N⁴) time algorithm for unpseudoknotted multiple (𝒪(1)) strand MFE prediction, answering the open problem from Dirks et al. The challenge lies in considering the rotational symmetry of secondary structures, a global feature not immediately amenable to local subproblem decomposition used in dynamic programming. Our proof has two main technical contributions: First, a characterisation of symmetric secondary structures implying only quadratically many need to be considered when computing the rotational symmetry penalty. Second, that bound is leveraged by a backtracking algorithm to efficiently find the MFE in an exponential space of contenders.
Cite as

Ahmed Shalaby and Damien Woods. An Efficient Algorithm to Compute the Minimum Free Energy of Interacting Nucleic Acid Strands. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 130:1-130:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{shalaby_et_al:LIPIcs.ICALP.2025.130,
  author =	{Shalaby, Ahmed and Woods, Damien},
  title =	{{An Efficient Algorithm to Compute the Minimum Free Energy of Interacting Nucleic Acid Strands}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{130:1--130:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.130},
  URN =		{urn:nbn:de:0030-drops-235071},
  doi =		{10.4230/LIPIcs.ICALP.2025.130},
  annote =	{Keywords: Minimum free energy, MFE, partition function, nucleic acid, DNA, RNA, secondary structure, computational complexity, algorithm analysis and design, dynamic programming}
}
Document
DOI: 10.4230/LIPIcs.WABI.2017.12
Sparsification Enables Predicting Kissing Hairpin Pseudoknot Structures of Long RNAs in Practice

Authors: Hosna Jabbari, Ian Wark, Carlo Montemagno, and Sebastian Will

Published in: LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)

Abstract
While computational RNA secondary structure prediction is an important tool in RNA research, it is still fundamentally limited to pseudoknot-free structures (or at best very simple pseudoknots) in practice. Here, we make the prediction of complex pseudoknots - including kissing hairpin structures - practically applicable by reducing the originally high space consumption. For this aim, we apply the technique of sparsification and other space-saving modifications to the recurrences of the pseudoknot prediction algorithm by Chen, Condon and Jabbari (CCJ algorithm). Thus, the theoretical space complexity of free energy minimization is reduced to Theta(n^3+Z), in the sequence length n and the number of non-optimally decomposable fragments ("candidates") Z. The sparsified CCJ algorithm, sparseCCJ, is presented in detail. Moreover, we provide and compare three generations of CCJ implementations, which continuously improve the space requirements: the original CCJ implementation, our first modified implementation, and our final sparsified implementation. The two latest implementations implement the established HotKnots DP09 energy model. In our experiments, using 244GB of RAM, the original CCJ implementation failed to handle sequences longer than 195 bases; sparseCCJ handles our pseudoknot data set (up to about length 400 bases) in this space limit. All three CCJ implementations are available at https://github.com/HosnaJabbari/CCJ.
Cite as

Hosna Jabbari, Ian Wark, Carlo Montemagno, and Sebastian Will. Sparsification Enables Predicting Kissing Hairpin Pseudoknot Structures of Long RNAs in Practice. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 12:1-12:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{jabbari_et_al:LIPIcs.WABI.2017.12,
  author =	{Jabbari, Hosna and Wark, Ian and Montemagno, Carlo and Will, Sebastian},
  title =	{{Sparsification Enables Predicting Kissing Hairpin Pseudoknot Structures of Long RNAs in Practice}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  pages =	{12:1--12:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Schwartz, Russell and Reinert, Knut},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017.12},
  URN =		{urn:nbn:de:0030-drops-76408},
  doi =		{10.4230/LIPIcs.WABI.2017.12},
  annote =	{Keywords: RNA, secondary structure prediction, pseudoknots, space efficiency, sparsification}
}
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