OMICS DEEL 2
Femke te Dorsthorst
2 APRIL 2023
UVA
,OMICS part 2
Introduction: From data to knowledge
Anna Heintz-Buschart – 8 mei 2023
Part 2: what an dhow do we learn about biology an dbiomedicine from omics data?
Analysis and modelling to interpret omics data
- How to design a study?
- How to learn about cellular/regulatory responses?
- How to tell health from disease?
- How to analyse interactions?
- How to model and predict a system?
Leerdoelen Blok 2:
• Data van een aantal geselecteerde omics technologieën kunnen interpreteren:
• Eenvoudige experimentele proefopzetten uitleggen en toepassen voor omics
experimenten;
• Simpele (statistische) analyses van omics data kunnen uitleggen en uitvoeren;
• Uitleggen hoe een aantal geselecteerde bioinformatica methoden werken om -omics data
te analyseren;
• Simple dynamische biologische systemen te beschrijven met differentiaal vergelijkingen,
en deze kunnen toepassen.
Part 1:
- how to read a genome and find variants?
- How to characterize microbiomes?
- How to measure transcripts?
- How to identify and measure proteins?
- How to detect and identify metabolites?
- How to store and use data?
Part 2
,Omics data analysis is a set of specific statistical methods to extract information from all the data
measured at the same time. Finding biological parts that differ between conditions, cell types, health
and disease. Finding combinations of parts that may distinguish groups of samples.
Omics data analysis
- Specific statistical methods to extract information
from all the data measured at the same time.
- Finding biological parts that differ between
conditions, cell types, health, and disease.
- Finding combinations of parts that may distinguish
groups of samples.
Omics data analysis aims:
- Which molecules/processes are important for a system’s functioning?
• Find diagnostic markers
• Find targets for prevention/therapy
• Prevent disruption of these processes
• Strengthen/supplement these processes
• Weaken/inhibit processes that are disruptive
- Which molecules work together in a functioning/non-functioning system?
Reductionism and systems biology
Reductionism = “divide and conquer” → Solve complex problems by dividing them into smaller,
tractable problems.
- One risk factor → one disease: e.g. one gene/variant
- One pathogen – one disease
- One disease → one treatment: e.g. one chemotherapeutic
- In omics research: “disentangle”factors/processes
If we measure all components, we still don’t understand the system. → We need to understand (the
results of) their interaction (emerging properties).
Systems biology = integration of wet-lab experiments, bioinformatics, and mathematical modelling
to understand biological systems.
Systems medicine = as outcome, systems medicine aims at a measurable improvement of patient
health trough systems-based approaches and practice.
Systems biology and systems medicine have a holistic perspective.
Interdisciplinary, incl. wet-lab and computational. Applied sciences and
in search of fundamental principles.
You test a hypothesis and with the results you gain a new hypothesis.
, Aims of systems biology and systems medicine:
- What prevents a system from working
• Development of effective therapies
- How can a system work better
• Prevention of disease
• Cost-effective biotechnology
- The role of unknown parts of a system
- Fundamental principles
Biological models = (mathematical) description or representation of a biological system. Falsifiable
simplification, usually of some predictive value. Not the same as a statistical model, although
statistical models sometimes make biological predictions/prognoses
Emergent properties = systems properties that differ from the properties of the system’s parts.
Result from interactions, self-organization.
Predictiveness and hypothesis generation
Models should:
- Be falsifiable
- Allow us to make reliable predictions about responses under different (yet untested)
conditions.
- Let us ask “what if?”, i.e. let us play out manipulations of the system.
Characteristic Reductionism Systems-oriented approach
Principle Behavior of a biological system Biological systems possess
can be explained by the emergent properties that are
properties of its constituent only possessed by the system
parts as a whole and not by any
isolated parts of the system
Approach One factor is singled out for Many factors are
attention and is given simultaneously evaluated to
explanatory weight on its own assess the dynamics of the
system
Critical factors Predictors/associated factors Time, space, context
Model characteristics Linear, predictable Non-linear, chaotic
Characteristic Reductionism Systems-oriented approach
- Principle Behaviour of a biological system can be explained by the properties of its
constituent parts Biological systems possess emergent properties that are only possessed by
the system as a whole and not by any isolated parts of the system
- Approach One factor is singled out for attention and is given explanatory weight on its own
Many factors are simultaneously evaluated to assess the dynamics of the system
- Critical factors Predictors/associated factors Time, space, context
- Model characteristics Linear, predictable Non-linear, chaotic
Femke te Dorsthorst
2 APRIL 2023
UVA
,OMICS part 2
Introduction: From data to knowledge
Anna Heintz-Buschart – 8 mei 2023
Part 2: what an dhow do we learn about biology an dbiomedicine from omics data?
Analysis and modelling to interpret omics data
- How to design a study?
- How to learn about cellular/regulatory responses?
- How to tell health from disease?
- How to analyse interactions?
- How to model and predict a system?
Leerdoelen Blok 2:
• Data van een aantal geselecteerde omics technologieën kunnen interpreteren:
• Eenvoudige experimentele proefopzetten uitleggen en toepassen voor omics
experimenten;
• Simpele (statistische) analyses van omics data kunnen uitleggen en uitvoeren;
• Uitleggen hoe een aantal geselecteerde bioinformatica methoden werken om -omics data
te analyseren;
• Simple dynamische biologische systemen te beschrijven met differentiaal vergelijkingen,
en deze kunnen toepassen.
Part 1:
- how to read a genome and find variants?
- How to characterize microbiomes?
- How to measure transcripts?
- How to identify and measure proteins?
- How to detect and identify metabolites?
- How to store and use data?
Part 2
,Omics data analysis is a set of specific statistical methods to extract information from all the data
measured at the same time. Finding biological parts that differ between conditions, cell types, health
and disease. Finding combinations of parts that may distinguish groups of samples.
Omics data analysis
- Specific statistical methods to extract information
from all the data measured at the same time.
- Finding biological parts that differ between
conditions, cell types, health, and disease.
- Finding combinations of parts that may distinguish
groups of samples.
Omics data analysis aims:
- Which molecules/processes are important for a system’s functioning?
• Find diagnostic markers
• Find targets for prevention/therapy
• Prevent disruption of these processes
• Strengthen/supplement these processes
• Weaken/inhibit processes that are disruptive
- Which molecules work together in a functioning/non-functioning system?
Reductionism and systems biology
Reductionism = “divide and conquer” → Solve complex problems by dividing them into smaller,
tractable problems.
- One risk factor → one disease: e.g. one gene/variant
- One pathogen – one disease
- One disease → one treatment: e.g. one chemotherapeutic
- In omics research: “disentangle”factors/processes
If we measure all components, we still don’t understand the system. → We need to understand (the
results of) their interaction (emerging properties).
Systems biology = integration of wet-lab experiments, bioinformatics, and mathematical modelling
to understand biological systems.
Systems medicine = as outcome, systems medicine aims at a measurable improvement of patient
health trough systems-based approaches and practice.
Systems biology and systems medicine have a holistic perspective.
Interdisciplinary, incl. wet-lab and computational. Applied sciences and
in search of fundamental principles.
You test a hypothesis and with the results you gain a new hypothesis.
, Aims of systems biology and systems medicine:
- What prevents a system from working
• Development of effective therapies
- How can a system work better
• Prevention of disease
• Cost-effective biotechnology
- The role of unknown parts of a system
- Fundamental principles
Biological models = (mathematical) description or representation of a biological system. Falsifiable
simplification, usually of some predictive value. Not the same as a statistical model, although
statistical models sometimes make biological predictions/prognoses
Emergent properties = systems properties that differ from the properties of the system’s parts.
Result from interactions, self-organization.
Predictiveness and hypothesis generation
Models should:
- Be falsifiable
- Allow us to make reliable predictions about responses under different (yet untested)
conditions.
- Let us ask “what if?”, i.e. let us play out manipulations of the system.
Characteristic Reductionism Systems-oriented approach
Principle Behavior of a biological system Biological systems possess
can be explained by the emergent properties that are
properties of its constituent only possessed by the system
parts as a whole and not by any
isolated parts of the system
Approach One factor is singled out for Many factors are
attention and is given simultaneously evaluated to
explanatory weight on its own assess the dynamics of the
system
Critical factors Predictors/associated factors Time, space, context
Model characteristics Linear, predictable Non-linear, chaotic
Characteristic Reductionism Systems-oriented approach
- Principle Behaviour of a biological system can be explained by the properties of its
constituent parts Biological systems possess emergent properties that are only possessed by
the system as a whole and not by any isolated parts of the system
- Approach One factor is singled out for attention and is given explanatory weight on its own
Many factors are simultaneously evaluated to assess the dynamics of the system
- Critical factors Predictors/associated factors Time, space, context
- Model characteristics Linear, predictable Non-linear, chaotic
