Skip to main content
SpringerLink
Log in
Book cover

Automated Reasoning for Systems Biology and Medicine pp 37–62Cite as

Automated Reasoning for the Synthesis and Analysis of Biological Programs

  • Chapter
  • First Online:

Part of the book series: Computational Biology ((COBO,volume 30))

Abstract

Cellular decision-making arises as the output of biochemical information processing, as complex cascades of molecular interactions are triggered by input stimuli. Deciphering critical interactions and how they are organised into biological programs is a huge challenge, compounded by the difficulty of manually navigating alternative hypotheses consistent with observed behaviour. Against this backdrop, automated reasoning is a powerful methodology to tackle biological complexity and derive explanations of behaviour that are provably consistent with experimental evidence. We present a reasoning framework that permits the synthesis and analysis of a set of dynamic biological interaction networks. Employing methods based on Satisfiability Modulo Theories (SMT), we encode experimental observations as specifications of expected dynamics, and synthesise networks consistent with these constraints. Predictions of untested behaviour are generated based on all consistent models, without requiring time-consuming simulation or state space exploration, and the method can be used to identify additional components, topological ‘switches’ that allow cell state changes, and to predict gene-level dynamics. We show the reader how to utilise this reasoning framework to encode and explore rich queries for their biological system of choice.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Since the regulation conditions are selected from a predefined set, the BNs we consider are a restriction of more general models with arbitrary Boolean functions as update rules.

  2. 2.

    Experimental observations can also include genetic perturbations, such as knock downs or forced expressions, which restrict the activity of the perturbed component over the complete experiment trajectory.

  3. 3.

    The observations about cell types (Fig. 2.1d) will be discussed later.

  4. 4.

    For ABNs \(\mathcal {A}_1\) and \(\mathcal {A}_2\) with the same set of components \(C_{\mathcal {A}_1} = C_{\mathcal {A}_2} = C\), \(\mathcal {A}_1\subseteq \mathcal {A}_2\) amounts to \(I_{\mathcal {A}_1} \subseteq (I_{\mathcal {A}_2}\cup I^?_{\mathcal {A}_2})\), \(I^?_{\mathcal {A}_1} \subseteq I^?_{\mathcal {A}_2}\), and \(r_{\mathcal {A}_1}(c) \subseteq r_{\mathcal {A}_2}(c)\) for all \(c \in C\).

  5. 5.

    The process of identifying required and disallowed interactions is optimised further by initially generating a single consistent model, since none of the interactions present in this model are disallowed, and none of the absent interactions are required.

References

  1. Abou-Jaoudé W, Monteiro PT, Naldi A, Grandclaudon M, Soumelis V, Chaouiya C et al (2015) Model checking to assess T-helper cell plasticity. Front Bioeng Biotechnol 2

    Google Scholar 

  2. Bartocci E, Lió P (2016) Computational modeling, formal analysis, and tools for systems biology. PLoS Comput Biol 12(1):1–22

    Article  Google Scholar 

  3. Benque D, Bourton S, Cockerton C, Cook B, Fisher J, Ishtiaq S et al (2012) BMA: visual tool for modeling and analyzing biological networks. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics). LNCS, vol 7358, pp 686–692

    Chapter  Google Scholar 

  4. Biere A, Cimatti A, Clarke E, Zhu Y (1999) Symbolic model checking without BDDs. In: Cleaveland WR (ed) Tools and algorithms for the construction and analysis of systems. Lecture notes in computer science, vol 1579. Springer, Berlin, pp 193–207

    Chapter  Google Scholar 

  5. Bjorner N, Moura LD (2011) Satisfiability modulo theories: introduction and applications. Commun ACM 54(9):69–77

    Article  Google Scholar 

  6. Chabrier N, Fages F (2003) Symbolic model checking of biochemical networks. Lect Notes Comput Sci 2602:149–162

    Google Scholar 

  7. Corblin F, Fanchon E, Trilling L (2010) Applications of a formal approach to decipher discrete genetic networks. BMC Bioinform 11:385

    Article  Google Scholar 

  8. Corblin F, Tripodi S, Fanchon E, Ropers D, Trilling L (2009) A declarative constraint-based method for analyzing discrete genetic regulatory networks. Biosystems 98(2):91–104

    Article  Google Scholar 

  9. De Moura L, Bjørner N (2008) Z3: an efficient SMT solver. Tools and algorithms for the construction and analysis of systems. Springer, Berlin, pp 337–340

    Google Scholar 

  10. Dunn SJ, Li MA, Carbognin E, Smith AG, Martello G (2018) A common molecular logic determines embryonic stem cell self-renewal and reprogramming. bioRxiv, p 200501

    Google Scholar 

  11. Dunn SJ, Martello G, Yordanov B, Emmott S, Smith AG (2014) Defining an essential transcription factor program for naive pluripotency. Science 344(6188):1156–1160

    Article  Google Scholar 

  12. Fages F (2002) Modelling and querying interaction networks in the biochemical abstract machine BIOCHAM. J Biol Phys Chem 4(2):64–73

    Article  Google Scholar 

  13. Fisher J, Piterman N, Hubbard EJA, Stern MJ, Harel D (2005) Computational insights into Caenorhabditis elegans vulval development. Proc Natl Acad Sci 102(6):1951–1956

    Article  Google Scholar 

  14. Fisher J, Henzinger T (2007) Executable cell biology. Nat Biotechnol 25(11):1239–1249

    Article  Google Scholar 

  15. Giacobbe M, Guet CC, Gupta A, Henzinger TA, Paixão T, Petrov T (2015) Model checking gene regulatory networks. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics), vol 9035, pp 469–483

    Chapter  Google Scholar 

  16. Gong H, Zuliani P, Wang Q, Clarke EM (2011) Formal analysis for logical models of pancreatic cancer. In: IEEE conference on decision and control and European control conference, pp 4855–4860

    Google Scholar 

  17. Guziolowski C, Videla S, Eduati F, Cokelaer T, Siegel A, Saez-rodriguez J et al (2013) Exhaustively characterizing feasible logic models of a signaling network using answer set programming. Bioinformatics 393

    Google Scholar 

  18. Herrmann F, Groß A, Zhou D, Kestler Ha, Kuhl M (2012) A Boolean model of the cardiac gene regulatory network determining first and second heart field identity. PLoS ONE 7(10):e46798

    Article  Google Scholar 

  19. Hoppe PS, Schwarzfischer M, Loeffler D, Kokkaliaris KD, Hilsenbeck O, Moritz N et al (2016) Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios. Nature 535(7611):299–302

    Article  Google Scholar 

  20. Khalis Z, Comet JP, Richard A, Bernot G (2009) The SMBioNet method for discovering models of gene regulatory networks. Genes Genomes Genomics 3(1):15–22

    Google Scholar 

  21. Krumsiek J, Marr C, Schroeder T, Theis FJ (2011) Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network. PloS One 6(8):e22649

    Article  Google Scholar 

  22. Kugler H, Dunn SJ, Yordanov B (2018) Formal analysis of network motifs. In: Ceska M, Safranek D (eds) Computational methods in systems biology. Springer International Publishing, New York, pp 111–128

    Google Scholar 

  23. Kwiatkowska M, Norman G, Parker D (2010) Probabilistic model checking for systems biology. In: Iyengar MS (ed)

    Google Scholar 

  24. Kwiatkowska M, Norman G, Parker D (2011) PRISM 4.0: verification of probabilistic real-time systems. Formal modeling and verification of cyber-physical systems. Springer Fachmedien Wiesbaden, Wiesbaden, pp 585–591

    Chapter  Google Scholar 

  25. Kwiatkowska M, Thachuk C (2014) Probabilistic model checking for biology. Softw Syst Saf 36:165

    Google Scholar 

  26. Le Novère N (2015) Quantitative and logic modelling of molecular and gene networks. Nat Rev Genet 16(3):146–158

    Article  Google Scholar 

  27. Mishra A, Oulès B, Pisco AO, Ly T, Liakath-Ali K, Walko G et al (2017) A protein phosphatase network controls the temporal and spatial dynamics of differentiation commitment in human epidermis. Elife 6:1–20

    Google Scholar 

  28. Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC et al (2015) Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol 33:269–276

    Article  Google Scholar 

  29. Nichols J, Smith A (2012) Pluripotency in the embryo and in culture. Cold Spring Harb Perspect Biol 4(8):a008128–a008128

    Article  Google Scholar 

  30. Pimanda JE, Ottersbach K, Knezevic K, Kinston S, Chan WYI, Wilson NK et al (2007) Gata2, Fli1, and Scl form a recursively wired gene-regulatory circuit during early hematopoietic development. PNAS 104(45):17692–17697

    Article  Google Scholar 

  31. SA Kauffman (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22(3):437–467

    Article  MathSciNet  Google Scholar 

  32. Schaub T, Siegel A, Videla S (2014) Reasoning on the response of logical signaling networks with ASP. Logical modeling of biological systems. Wiley, Hoboken, pp 49–92

    Google Scholar 

  33. Shavit Y, Yordanov B, Dunn SJ, Wintersteiger CM, Otani T, Hamadi Y et al (2016) Automated synthesis and analysis of switching gene regulatory networks. BioSystems 146:26–34

    Article  Google Scholar 

  34. Stergachis AB, Neph S, Reynolds A, Humbert R, Miller B, Paige SL et al (2013) Developmental fate and cellular maturity encoded in human regulatory DNA landscapes. Cell 154(4):888–903

    Article  Google Scholar 

  35. Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126(4):663–676

    Article  Google Scholar 

  36. Videla S, Guziolowski C, Eduati F, Thiele S, Gebser M, Nicolas J et al (2015) Learning Boolean logic models of signaling networks with ASP. Theor Comput Sci 599:79–101

    Article  MathSciNet  MATH  Google Scholar 

  37. Wang Q, Clarke EM (2016) Formal modeling of biological systems. In: 2016 IEEE international high level design validation and test workshop (HLDVT). IEEE, pp 178–184

    Google Scholar 

  38. Yachie-Kinoshita A, Onishi K, Ostblom J, Langley MA, Posfai E, Rossant J et al (2018) Modeling signaling dependent pluripotency with Boolean logic to predict cell fate transitions. Mol Syst Biol 14(1):e7952

    Article  Google Scholar 

  39. Yordanov B, Dunn SJ, Kugler H, Smith A, Martello G, Emmott S (2016) A method to identify and analyze biological programs through automated reasoning. NPJ Syst Biol Appl 2:16010

    Google Scholar 

  40. Yordanov B, Wintersteiger CM, Hamadi Y, Kugler H (2013) SMT-based analysis of biological computation. In: NASA formal methods symposium, pp 78–92

    Google Scholar 

  41. Yosef N, Shalek AK, Gaublomme JT, Jin H, Lee Y, Awasthi A et al (2013) Dynamic regulatory network controlling TH17 cell differentiation. Nature 496(7446):461–468

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Microsoft Research, 21 Station Road, Cambridge, CB1 2FB, UK

    Sara-Jane Dunn & Boyan Yordanov

Authors
  1. Sara-Jane Dunn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Boyan Yordanov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sara-Jane Dunn .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

    Pietro Liò

  2. School of Computing, Newcastle University, Newcastle, UK

    Paolo Zuliani

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dunn, SJ., Yordanov, B. (2019). Automated Reasoning for the Synthesis and Analysis of Biological Programs. In: Liò, P., Zuliani, P. (eds) Automated Reasoning for Systems Biology and Medicine. Computational Biology, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-17297-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-17297-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17296-1

  • Online ISBN: 978-3-030-17297-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation