Systems Biology


The general goal of the Systems Biology curricular unit is to provide advanced training, competencies and skills in systems level analysis of complex biological phenomena following a holistic modeling paradigm. Students are trained in a number of topics that are central to Systems Biology

  • Translation of biological mechanisms into mathematical systems.
  • Implementation of simulation models in computers according to the standards currently adopted in Systems Biology.
  • Bottom-up (holistic) analysis. Integration of mechanisms to infer biological function from mechanisms.
  • Top-down (holistic) analysis: systems analysis to identify mechanisms from high throughput molecular biology data.

Through attending this curricular unit, students are expected to acquire critical practical skills to solve systems biology problems, namely:

  • The ability to organize disparate biological information into a coherent self-consistent whole using mathematical models.
  • The ability to identify important components/interactions within a complex biological system from the analysis of molecular biology data.
  • The ability to disprove hypotheses and to define improved hypotheses in the process of model development.
  • The ability to identify and understand the essential features of a biological system.
  • The ability to simulate, predict, and optimize procedures, experiments and technologies.

This curricular unit also aims at framing these subjects within relevant biotechnological applications. Thus another important goal of this curricular unit is to provide specific training in the application of systems biology fundaments to the engineering of cell factories and processes, namely:

  • Metabolic (genetic or medium) engineering,
  • Synthetic biology, and
  • Bioprocess engineering.

General characterization





Responsible teacher

Rui Manuel Freitas Oliveira


Weekly - 4

Total - 85

Teaching language



Students attending the Systems Biology curricular unit should have a strong background in

  • Mathematics, namely linear algebra, statistics and differential calculus;
  • Numeric methods and computation;
  • Systems theory, particularly dynamic systems theory and dynamic systems properties;
  • Biochemistry and molecular cell biology. 


Mainly scientific papers.


 - D. Wilkinson. Stochastic Modeling for Systems Biology, Chapman&Hall, 2006

- Alon, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. Boca Raton, FL: Chapman & Hall, 2007. ISBN: 9781584886426.

Teaching method

Subjects are covered in theoretical lectures (2 × 14 = 28 hours) where topics are exposed in a powerpoint. Illustrative examples of modeling methods will be presented and supported by in class MATLAB simulations. Problem-solving sessions take place in computer classrooms (4 × 14 = 56 hours hours). MATLAB is the computational language adopted in this curricular unit. Students typically implement models, simulate and optimize solutions of problems representative of the subjects taught in the theoretical lectures.

Given the highly interdisciplinary nature of Systems Biology, a cooperative-learning strategy will be adopted for instruction of systems biology (Anuj Kumar, 2005). Students normally have some background in a given research method or two; however, very rarely they have a background in all topics encompassed within a typical systems biology problem. For this reason, working collectively in groups is a major component of the teaching method. Work groups are built based on their backgrounds and skills in a way to ensure a broad knowledge base with students within each group complementing each other''''s background. In order to achieve this, students will be interviewed individually and fill a form with a set of question on their background and skills, based on which the teacher decides upon the composition of each group of 4 elements.

Evaluation method

Biologia de Sistemas


A – Relatório trabalho MATLAB 

B – Seminário sobre trabalhos MATLAB

C – Teste


Nota final

      Nota final avaliação contínua = 33% A+ 33% B + 33%C 

      Para aproveitamento ao módulo I o mínimo da nota final é 9,5



A – Relatório trabalho MATLAB 

B – Seminário sobre trabalhos MATLAB

C – Exame final


Nota final

      Nota final exame final = 33% A+ 33% B + 33%C

      Para aproveitamento ao módulo I o mínimo da nota final é 9,5

Subject matter

1   Introduction to Systems Biology

1.1    Molecular biology in the post-genomic era

1.2    Introduction to the discipline of systems biology

2   Computational biology elements

2.1    Modeling biochemical networks

2.1.1   Metabolic networks and signal transduction networks

2.1.2   Stoichiometric and graph theoretical representation

2.1.3   Structural properties of networks

2.1.4   Computation of elementary flux modes and extreme pathways

2.2    Thermodynamics and kinetics of biological reactions

2.2.1   DNA replication and transcription

2.2.2   Protein translation

2.2.3   Thermodynamic equilibrium and kinetic traps

2.2.4   Example of thermodynamic and kinetic virus-like particles assembly

2.3    Stochastic kinetic modeling

2.3.1   The chemical master equation (CME)

2.3.2   Simulation of stochastic kinetics by the tau-leaping method

2.3.3   Examples of viral infection and protein glycosylation

3   Holistic approaches

3.1    Bottom-up systems biology by constraints based modeling

3.1.1    Metabolic flux analysis

3.1.2   Flux balance analysis

3.1.3   Gene regulation networks

3.2    Top-down systems biology models

3.2.1   Nonparametric modeling

3.2.2   Classification methods

3.2.3   Linear and non-linear regression methods

3.3    Hybrid bottom-up/top-down systems biology by semiparametric models

4   Systems bio(techno)logy

4.1    Recombinant proteins: modeling and optimization of transcription and translation

4.2    Genetic engineering: prediction of gene knock-outs

4.3    Synthetic biology: pathway-level synthetic design

4.4    Multi-scale modeling: optimization and control of macroscopic processes

5   Systems entrepreneurship

5.1    Success stories of entrepreneurship anchored in Systems Biology