https://www.symbiont-project.org/

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57	      <!-- 	The -->
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60		SYMBIONT	
61	      </span>
62	      <span style="font-family:Oswald;font-weight:200;">
63		Project
64	      </span>
65	    </h1>
66	    <H2>Symbolic Methods for Biological Networks</h2>
67	    <p>
68	      SYMBIONT is an international interdisciplinary project ranging from
69	      mathematics via computer science to systems biology and systems
70	      medicine, with a balanced team of researchers from those fields. At
71	      the present stage the project has a clear focus on fundamental
72	      research on mathematical methods, and prototypes in software. Results
73	      are systematically benchmarked against models from computational
74	      biology databases.
75	    </p>
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86	      <h2>Models</h2>
87	      <p>
88		Computational models in systems biology are built from molecular
89		interaction networks and rate laws, involving parameters, resulting
90		in large systems of differential equations. These networks are
91		foundational for systems medicine. One important problem is that the
92		statistical estimation of model parameters is computationally
93		expensive and many parameters are not identifiable from experimental
94		data. In addition, there is typically a considerable uncertainty
95		about the exact form of the mathematical model itself.
96	      </p>
97
98	      <h2>Objectives</h2>
99	      <p>
100		Parametric uncertainty with potential parameter variations over
101		several orders of magnitudes leads to severe limitations of
102		numerical approaches even for rather small and low dimensional
103		models. Furthermore, existing model inference and analysis methods
104		suffer from the curse of dimensionality that sets an upper limit of
105		about ten variables for models to be tractable. Therefore, the
106		currently prevailing numerical approaches shall be complemented with
107		our novel algorithmic symbolic methods aiming at formal deduction of
108		principal qualitative properties of relevant models.
109	      </p>
110	    </div>
111	    
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113	      <h2>Approach</h2>
114	      <p>
115		The principal approach of SYMBIONT is to combine symbolic methods
116		with model reduction methods for the analysis of biological
117		networks. We propose new methods for symbolic analysis, which
118		overcome the above mentioned obstacles and therefore can be applied
119		to large networks. In order to cope more effectively with the
120		parameter uncertainty problem we impose an entirely new paradigm
121		replacing thinking about single instances with thinking about orders
122		of magnitude.
123	      </p>
124	      <h2>Methods</h2>
125	      <p>
126		Our computational methods are diverse and involve various branches
127		of mathematics such as tropical geometry, real algebraic geometry,
128		theories of singular perturbations, invariant manifolds, and
129		symmetries of differential systems.
130	      </p>
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142	  <!--     SYMBIONT Workshop in Kassel, Germany, March 18&ndash;19, 2020 -->
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153	  <!--     starting February 2020&nbsp;&nbsp; -->

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164	  <h3 style="color:var(--bg-dark);">Project Poster</h3>
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