Multi-Layer Modeling in Systems Biology

1. Multi-Layer Modeling in Systems Biology Biological processes involved in diseases and disease progression require integration of multiple types of knowledge. Sriram Iyengar, PhD, Associate Professor, School of Biomedical Informatics University of Texas, Houston

2. A goal of Systems Biology • t = time • Functionality(t) = (y1,y2,y3,..,t) = f(x1,x2,x3,…, t) • Other goals, maybe on the way to achieving the above: Enhance detailed understanding of biological processes

3. Biology is complex! • Interactome - Protein-protein Interaction Map • The Scientist 2004, 18(12):18

4. What can systems biology do for medicine and patient care • Develop accurate specific and personalized models of actual biological processes based on tissue samples and individual genetics • Generate hypotheses for disease presence and disease progression • Support personalized medicine • Develop targeted therapies. • Support translational medicine

5. The biological cell: Nature’s canonical object in the sense of Object oriented programming • “The fundamental unit of life is the biological cell and hence should be the focus for current research in digital biology” • Sidney Brenner, 2002 Nobel laureate in medicine • Cellular process modeling is now an active research area • E-cell project (Japan) • Virtual cell project (National Resource for Cell Analysis and Modeling, USA) • Create in-silico digital cell

6. But: Biological cells are extremely intricate • ~500 types of cells • red blood cells, neurons, and more • >60 billion cells in the human body • >100 billion Symbiotic bacteria • 30,000 proteins expressed at any given time by a cell • That’s only a fraction of what a cell does, or what it looks like • A cubic cm of the human brain can contain 50 million neurons, each supported by 10 glial cells and connected to many other neurons

7. In silico cell modeling • The challenge • Creating an executable software description of a biological cell is an extremely formidable task • The computer science response • Reduce complexity • Use artificial Intelligence and software engineering methods

8. Why do this? Morphoproteomics • Development of malignant tumors depends on • Timing • Cell cycle phase • Location • Interactions in cell • Pathways • Signal cell proliferation • Crosstalk • Messages sent across multiple signaling pathways • Robert Brown, MD, Deputy Chief of Pathology, Univ. Texas, Houston

9. Morphoproteomics visual analysisof stained tumor cells

10. Glioblastoma multiforme

11. Implications for cancer therapy • Multidimensional approach required to analyze • Timing • Location • Signals • Crosstalk • Cell morphology • Where are particular signal transduction events taking place? • How does intracellular morphology, eg cytoskeleton, impact signaling, cross-talk and ultimately, disease progression?

12. Techniques to reduce complexity • Abstraction • Capture the essential or defining attributes of a body of knowledge • Modularity • Divide and conquer • Reduce complex problems into simpler sub-problems • “Solve” each sub-problem by domain specialists

13. Model Abstraction & Assembly • Abstraction • Simplify the system in such a way that the properties of interest are preserved • Einstein: Make the abstracted system “as simple as possible, but no simpler” • Assemble the sub-problems • “Glue” via a suite of interfaces (APIs - Application Programming Interfaces) • Create a multi-layer model: what criteria? Physical scale? Functional behavior? Injection of humility: remember that ‘All models are wrong, but some are useful’… GEP Box (a statistician)

14. Example Multi-layer model • ISO-OSI • International Standards Organization - Open Systems Interconnect • Used to model and create systems for data communications • Email, web, file transfer

15. ISO-OSI model for Data Communications

16. Multi-layer Modeling benefits • Complex problem reduced to tractable sub-problems • Data homogeneous within a layer • Each layer can be dealt with by appropriate specialists • Problems in the physical layer differ from those in the application layer • Different skills needed • No one person must be expert in several disciplines (layers) • Universal focus in thought and discussion • “TCP belongs to transport layer” • Everyone knows what TCP does and does not do

17. Building multi-layer models to support Systems Biology • How can this be done? • Let’s consider one aspect at a time • Simplify the system via divide & conquer • Define the sub-problems and their interfaces • Data • Processes • Interactions • Goal • To create a model based on the synthesis of heterogeneous knowledge • Biological cell seems to be a good place to start

18. Multi-layer Cell Model ____Functionality ____Physical Structure Signaling ____Metabolism / ____Symbolic Biochemistry ____Biochemistry ____Molecular Morphology

19. Molecular Morphology • Bio-molecular structure & form • Molecular modeling • Molecular dynamics • UIUC group, www.ks.uiuc.edu • Protein folding, conformation • Interactions at molecular level • UTH -www.biomachina.org

20. Biochemistry (Thermodynamics) • View cell as a dynamic chemical machine • Use reaction kinetics, rate constants • Michaelis-Menten equation • Set multiple equilibria • Identify bio-chemical minutiae by which organic molecules interact with each other • Examples • Plateki (www.biokin.com) • Determines inhibition constants from plate-reader data

21. Symbolic biochemistry • Abstract symbolic models of biochemical assemblies from components • DNA as an assembly of A, G, C, T • Proteins as assemblies of amino acids • Genetic code • Human genome project • Algorithms: Smith-Waterman, Hidden Markov models

22. Metabolism • Cells convert nutrients into energy via metabolic pathways & processes • Symbolic models facilitate understanding of cell’s “Quality of life” • Is cell functioning at 100% energy efficiency? • Examples • Biocyc projects • E-cell project

23. Signaling • Processes by which cell communicates with external entities and intra-cellular components • Signal transduction pathways • Examples • Pathway Logic • Pi Calculus • Rewriting Logic and Protein Functional Domains

24. Physical Structure Cell Morphology • Cell Assembly • anatomic components • 3-D shapes • 3-D spatial relationships • Physical characteristics of cell components • Texture • Permeability • Specialized components • Axons, Dendrons etc • Cytoview Project: In collaboration with Indian Inst of Science • Journal of Biosciences, v32 #5 “Cytoview: Development of a cell modeling framework.

25. Cytoview: Ontology of components

26. Relationship Matrix

27. Functionality • What does cell do? • Use mathematical and software idealizations • Black box, systems approach • Like ISO-OSI application layer • Examples • McCulloch-Pitts model of neuron • Models of optical cells • The challenge of in silico cell modeling • How to relate functionality to “rest of cell”

28. Interfaces • Receptors & other chemical entities • Enable signaling • Serve as interfaces among pathway segments • Receptors • From computing / communications view • Receptors are “control hubs” • They determine topologies of signaling and metabolic pathways • Receptor chemistry is an important discipline in itself • Symbolic descriptions of signaling interfaces help define cell processes

29. Multi-layered synthesis of heterogeneous knowledge • It is most likely impossible that • The complete story • From functionality down to the molecular layer • Can be expressed by a set of mathematical equations • But it ispossible that • Algorithmic procedures encoded in software can do so • The layered model is intended to provide a framework for developing software & underlying knowledge bases • Morphoproteomics • Other applications ???

30. Firing of neuron can be represented as A cascade of events & processes across layers Depending on desired level of detail, a multi-layer software model of neuron firing may instantiate objects, databases, & software from several levels Functionality is clearly dependent on physical structure Axons, dendrites Example

31. Neuron’s continued existence & wellness depends on metabolism Control behavior is mediated by signal transduction Biochemical neural signals at input synapses Biochemistry & molecular dynamics mediate actual passage of electrical signals (Hodgkin-Huxley) Example

32. Layered model tasks • Define overall • Application Programming Interfaces, or rather meta- APIs, which describe how layers should communicate with each other • Define for each layer • Uniform standards for representations of knowledge • Standards for databases and database access • Note: above tasks are simplified since there is greater homogeneity of knowledge within a layer • Interactions between layers: eg, can results derived from reasoning in the symbolic biochemistry layer be used to reduce dimensionality or enhance computational tractability in the biochemistry layer

33. Summary • Bio-molecular complexity may be reduced by • Abstraction • Modularization • The goal of creating an in silico complex biological objects, eg, biological cell, may be facilitated by means of the layered model described • Multi-layer model as an organizing principle to guide discussion, research, development?

34. Challenges • To date most methods have been used for analysis of interactions within a single cell • In reality, cells work together - or aqainst each other - in biological processes • Cancer involves millions of cells and inter-cellular interactions (metastasis) • Computer science can offer tools to solve these challenging problems • Morphoproteomics, with its depth of heterogeneous knowledge, is a unique application area that bridges “bench to bedside” : Translational, personalized medicine

35. Acknowledgements • Mary McGuire, MS, PhD, UT Houston • Jack W Smith, MD, PhD, UT Houston • Robert Brown, MD, UT Houston • Pat Lincoln, PhD, SRI • Carolyn Talcott, PhD, SRI • David Mercer, MD, University of Nebraska • Suma Chandra, PhD, Indian Institute of Science • N. Balakrishnan ,PhD, Indian institute of Science