Evolutionary and Ecological Perspectives on Systems Diseases using Agent-based Modeling

Transcription

1 Evolutionary and Ecological Perspectives on Systes Diseases using Agent-based Modeling Swarfest 2014 Notre Dae University South Bend, IN, June 30, 2014 Gary An, MD Associate Professor of Surgery Departent of Surgery University of Chicago, Chicago, IL

2 Disclosure Consultant for Iunetrics Matheatical Modeling of Sepsis and Execution of In-silico clinical trials

3 U.S. FDA Critical Path Docuent March 2004 Innovation or Stagnation

4 Wandling and An, WJ Eer Surg, 2010

5 Organ Function

6 Organ Function Cell Cell Cell Cell Cell Cell Cell

7 Organ Function Cell Cell Cell Cell Cell Cell Cell

8 Vertical and Parallel Coupling

9 The Translational Dilea

10 Traditional Scientific Cycle Increasing Diensionality of Data Scientific Cycle in Data-Rich, High-throughput Environent Increased Coplexity Systes Diseases

11 What are Systes Diseases? Disorders of Control Structures => Generate Abnoral and Undesirable States Disordered Responsiveness Brittle rather than Fragile Resulting Disease States are Robust because of reaining control structures => Hard to Treat Exaples: Cancer, Sepsis, Diabetes, Autoiune Diseases, anything having to do with inflaation, all ajor health probles today

12 Data Evaluation of Causation Experient (Wet lab or Insilico) Test Mechaniss Curvecreation Dynaic Scientific Process Cyclical Iterative Hypotheses Identification of Correlation Statistics Clustering Networks Identify Relationships Curve-fitting Static The ephasis here is Knowledge as opposed to Data NOTE: Hypothesis are always incoplete!

13 Jorge Luis Borges: On Exactitude in Science In that Epire, the Art of Cartography attained such Perfection that the ap of a single Province occupied the entirety of a City, and the ap of the Epire, the entirety of a Province. In tie, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Epire whose size was that of the Epire, and which coincided point for point with it. The following Generations, who were not so fond of the Study of Cartography as their Forebears had been, saw that the vast Map was Useless...

14 Science is selective Abstraction is the pathway Karr, et al. A Whole-Cell Coputational Model Abstraction! Predicts Phenotype fro Genotype. to Theory! Cell 150(2): , 20 July 20, 2012 What? Mycoplas genitaliu: 525 genes (E. coli has 4288) Cluster of 128 coputers 10 hours runtie for 1 cell division Generates 0.5 GB data

15 The Proble with Biology Alost copletely epirical: My syste is different! Infinite Regress => Just give e ore detail! The Questions of Science: What? = Data How? = Mechanis Why?* = Contextualize The output of Science is Knowledge

16 The What of Biology What? = Description Phenotype = Data concerning a particular state All data goes to defining a phenotype Agnostic to scale

17 The How? of Biology How? = Mechanis Means by which one phenotype arises fro another Dynaic interpretation of data Scale becoes iportant because echaniss don t necessarily coute (Sequence of Events Matters!)

18 The Why of Biology Generally Taboo! => Leads to vitalis, just-so stories, Creationis, etc. But Why? is also a pathway to Theory => Suggests a eta-process Why? can lead to a teleological trap of Final Cause Luckily, Final Cause in Biology = Evolution Evolution = Diversity + Fitness + Selection + Persistence

19 M&S in Bioscience: Path to Theories Follow M&S Counity s Standard of Progressive Tiers of Validation Discovery rather than Engineering* Plausibility versus Prediction Face validity ust coe first! Prediction is not the end-all/be-all, and not even desirable => Biology not ature enough Robust Models => Building Theories Rapid iteration with experientalists

20 The Iportance of Dynaics Dynaic => Syste evolves over tie Mechanistic => Approxiations of Cause and Effect Need to capture oveent fro Health to Disease Sae underlying processes => Different conditions => Different behaviors => Different Phenoena => Heterogeneity Disease as a specific Dynaic State

21 Basic Insights fro Dynaic Modeling Approach Health is a dynaic steady state => this is not trivial to odel Biological systes and Health are very robust => it takes a lot to irrevocably disrupt the Consequently the reaction of the syste needs to be vigorous to be effective Design Principles => Survival, Selection, Econoy and Evolution

22 Types of Dynaic Coputational Modeling Equation based ethods Ordinary Differential Equations (ODE) Partial Differential Equations (PDE) Shifts in variables/easureents over tie Shifts in variables/easureents over tie and space Stochastic Models (Gillespie Algorith) Models that incorporate probability and randoness into coponent behavior

23 Another Approach: Agent Based Modeling (ABM) Object Oriented => Coponents of the syste => Agents Rule Based => Mechaniss Spatial => Virtual World Intrinsic Stochastics => Heterogeneity Agent interactions => Population Behavior => Syste Behavior Dynaic Knowledge Representation Qualitative versus Quantitative

24 Crossing the Translational Divide with ABM Organis Organs Dynaic, In- Silico ABM of Patient The Surgical Patient Aggregate Agent Populations Gut (Intestinal epitheliu, luen / brush border, vasculature, icrobiota) Tissues Agent Populations Mucosa, epitheliu, bone arrow Cell types Actors/Agents of the Model Gut flora, colonizing pathogens, dendritic cells, acrophage, epithelial cells, goblet cells Molecular echaniss Rules for the Model Signal Cascades/Gene Regulation

25 Cancer: What is it? Disordered Cells with Disordered Growth Disordered fro What? Failure of Cellular Control Structures Cancer is a product of Multicellular Organiss Failure Points are related to evolutionary developent of ulticellularity Resultant Cancer behavior akin to unicellular organiss Different Evolutionary Strategies Iplications for Cancer behavior

26 Proposed Generative Hierarchy for Cancer 1st Order Process: Prootion of Genetic instability/plasticity Genetic daage/dna base pair alterations that accuulates and not just the regulation of the gene expression network. 2nd Order Process: Functional Deficits anifest at the individual cell level. Proliferation, loss of ortality, ipaired DNA daage repair, loss of igration inhibition. Loss of evolutionarily generated control structures required to aintain the integrity of ulticellular organiss. 3rd Order Process: Multicellular effects evident in the behavior of the tuors as a population of cells. Angiogenesis, interactions with the stroa, iune evasion, and release of potentially etastatic cells Hijacking of noral processes present in ulticellular organiss

27 Source of Daage? External Sources: Radiation, Toxins, Infections Endogenous: Inflaation Orqanisal Response to Daage Multi-cellular Organiss Evolutionarily Conserved Pathway to Adaptive Iunity Known Association with Cancers Can we contextualize oncogenesis in inflaatory ilieu?

28 X Therapy Targets 3rd Order Multicellular/Population Effects 2nd Order Functional Consequences/ Deficits at Individual Cell Level Developent of Resistance Adaptation Fitness Frae Shift To Colony Behavior Cancer Cell Colony 1st Order Genetic Instability/ Plasticity Environental Daage (Inflaatory Milieu)

29 Base Inflaation Model ABM of Global Systeic Inflaation, circa 1990 Endothelial/Blood interface Activation/Propagation of Inflaation Endothelial Cells and White Blood Cells Reproduces Dynaics of Pathophysiology Very Abstract! An, Shock 2001 and An, Critical Care Medicine 2004

30 Model of Global Inflaation, circa 1990 Cell types Cell Receptors and Functions Mediators Endothelial cells, neutrophils, onocytes, TH0, TH1, TH2, bacteria, white blood cell generative cells L-selectin, E/P-selectin, CD-11/18, ICAM, TNFr, IL1r, adhesion, igration, respiratory burst, phagocytosis, apoptosis Endotoxin, PAF, TNF, IL-1, IL-4, IL-8, IL-10, IL-12, IFNg, stnfr, IL-1ra, GCSF

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32 Phenotypically Healthy Cell DNA Daage = Basal-DNA _Daage Rate + ROS Functional Abnorality in 1. Suppression of Proliferation 2. Suppression of Migration 3. Apoptosis 4. DNA Daage Repair 5. Overexpression Proliferation Cell Cycle Arrest: Decreased Metabolis w/ Enhanced DNA Repair Stays Until Repair or Replication Generationally Propagated Mutation Y Y Y _ + Intrinsic DNA Repair If Residual Daage in 1/5 Precancerous/ Genes N Silent Mutation If DNA Integrity < Cell- Cycle Arrest Threshold N If Residual Daage Present by Replication Threshold N

33 Healthy Outcoe Microtuors Cancers 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Basal-DNA-Daage-Rate

34 Evidence of Sustained Inflaation Increasing Syste Daage Initial Injury Nuber

35 Persistent Reactive Oxygen Species 0.8 Increasing Reactive Oxygen Species Initial Injury Nuber

36 Healthy Outcoe Microtuors Cancers 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Initial Injury Nuber

37 Accuulated DNA Daage/tuor cell Accuulated DNA Daage (x 10 6 Unit-less Value) Initial Injury Nuber

38 Accuulated DNA Daage/icrotuor cell Accuulated DNA Daage (x 10 5 Unit-less Value) Initial Injury Nuber

39 Iplications for Cancer Treatent Addressing 2nd and 3rd Order Processes need to account for effect on 1st Order Process Alteration in Selective Forces/Fitness Functions Less Disordered: Less iune response but less evolutionary forcing More Disordered: Iune response, but generates ore evolutionary forcing Eradicative Therapies => Increased Evolutionary Forcing Environental Control versus Eradication

40 tions within highly variable latent periods. While there has been hese infections with antibiotics, environental control easures, and of HostthatPathogen Interactions therespectru is copelling evidence persistent and recurring HAIs are Infection Pathogenic Pathogen Metabolis Healthcare Associated Host Stress Host Metabolis O O O OH HO OH HO O HO OH HO OH HO OH OH HO OH OH HO OH HO OH OH OH Pathogen Regulation Pathogen Signaling Host Signaling Host Regulation Benign Healthcare Associated Pathogen Virulence Micro-environent Health Host-Pathogen Interaction Figure 1. Escalation of hostility in host-pathogen interactions: bidirectional olecular effects of host stress and pathogen virulence

41 Clostridiu difficile Infection (CDI) 2nd ost coon hospital related infections 15-25% of all infectious diarrhea in U.S. 14,000 deaths in U.S. Pathophysiology: Follows systeic antibiotic treatent Depletion of baseline intestinal icrobioe C. difficile has an inert spore for and a toxin producing gerinated for Treatent Anti-CDI antibiotics Fecal icrobial Transplant (FMT)

42 Intestinal Luen Antibiotics Fecal Microbial Transplant Coensal Microbes Hepatic Production TCA DCA Nutrient Copetition Gerination C. difficile Gerinated C. difficile Spores Sporulation Induced by Nutrient Scarcity

43 A B C D CDI E CDI Taurocholate CDIABM Baseline Health CDI Deoxycholate CDI Toxin View

44 Total Syste Daage fro Paraeter Sweep Resource Replenish Rate

45 End Coensal Population fro Paraeter Sweep Resource Replenish Rate

46 End Total C. difficule Spores and Gerinateds fro Paraeter Sweep Resource Replenish Rate

47 End Count C. difficile Gerinated fro Paraeter Sweep Resource Replenish Rate

48 End Count C. difficile Spores fro Paraeter Sweep Resource Replenish Rate

49 What does this all get you? If Model Behavior atches real world observations The Thought Experient is a Plausible representation of the real world Look for ways to break it If Model Behavior does not atch real world observations Re-exaine underlying assuptions Utilize Modularity for differential fitness Science Progresses via Hypothesis Nullification It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so. -- Mark Twain

50 Using Context to Constrain Need to liit the possible interpretations of data Need to liit the possible configurations of echanis Pathway to Theory => Why it ust be so Theory is what guides Mapping Interpretation of cross-platfor results Personalization => Generation of State

51 The Translational Goal Exercise Control Actionable Knowledge Establish Sufficient Trust, not Ontological Truth Look for conserved functions and architectures => Theories There are no Shortcuts But we can accelerate the cycle...

52 petitive sapling of the HPI over the course of a a. l d e d r d s n Proposed Process Flow Integrated Approach to Host-Pathogen Interactions High-throughput Bio-analysis Technologies Inforatics Core B C Pathogen Metabolis Micro-environent Host Metabolis O Huan Saples G O O A OH HO OH HO O HO HO OH OH HO OH OH HO OH OH HO OH OH HO OH OH Pathogen Regulation Clinically Relevant Anial Models Pathogen Signaling Host Signaling F Host Regulation Infected Huan Cell Cultures E Modeling and Siulation Core D Siulation In Silico Workflow HPI Hypotheses

53 Knowledge Ecologies: Science as Evolution An, Science Translational Medicine, 2010

54 Finis